Location Prediction from Social Media Contents using Location Aware Attention LSTM Network

Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.

基于BiLSTM-Attention的F_(10.7)指数预测模型与中国自主数据集的应用

F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.

Detection of fusarium head blight using a YOLOv5s-based method improved by attention mechanism

Fusarium head blight (FHB) is one of the most destructive diseases in global wheat production. In order to count the FHB-infected wheat ears under field conditions, this study proposed an algorithm for diseased wheat ear detection based on improved YOLOv5s (Tr-YOLOv5s). The Swin Transformer was used to replace the CSPDarknet backbone network to enhance the extraction of characteristic information of the population wheat ears of FHB in the field background. The convolutional block attention module (CBAM) attention mechanism was added to improve the detection effect of target wheat ears, subsequently improving the overall accuracy of the model. The original loss function complete intersection over union (CIoU) was replaced by Scylla intersection over union (SIoU) loss to accelerate the model convergence and decrease the loss value. The results showed that the mean average precision (mAP) of the Tr-YOLOv5s model reached 90.64%, making a 4.63% improvement compared to the original YOLOv5s model. The improved model could quickly detect and count wheat FHB ear in the field environment, which laid a foundation for the subsequent automatic disease identification and grading of wheat FHB under field conditions.

An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

RT-YOLO:A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

In recent years,target detection of aerial images of unmannedaerial vehicle(UAV)has become one of the hottest topics.However,targetdetection of UAV aerial images often presents false detection and misseddetection.We proposed a modified you only look once(YOLO)model toimprove the problems arising in object detection in UAV aerial images:(1)A new residual structure is designed to improve the ability to extract featuresby enhancing the fusion of the inner features of the single layer.At the sametime,triplet attention module is added to strengthen the connection betweenspace and channel and better retain important feature information.(2)Thefeature information is enriched by improving the multi-scale feature pyramidstructure and strengthening the feature fusion at different scales.(3)A newloss function is created and the diagonal penalty term of the anchor frame isintroduced to improve the speed of training and the accuracy of reasoning.The proposed model is called residual feature fusion triple attention YOLO(RT-YOLO).Experiments showed that the mean average precision(mAP)ofRT-YOLO is increased from 57.2%to 60.8%on the vehicle detection in aerialimage(VEDAI)dataset,and the mAP is also increased by 1.7%on the remotesensing object detection(RSOD)dataset.The results show that theRT-YOLOoutperforms other mainstream models in UAV aerial image object detection.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Television as Popular Culture Media and Parental Attention and Their Correlation to the Students＇ Motivation to Choose Major

Television as one of the popular culture media plays an important role in the development of students＇ personality and knowledge because it provides countless information and entertainment that can enhance their knowledge as the viewers. This study was conducted to give an idea whether television viewing and parental attention can assist students to take a decision of choosing major at the senior high school. This study used descriptive method which analyzed the correlation among television viewing, parental attention, and the students＇ motivation to choose a major. The sample of this study consisted of 100 students of the state senior high school in Malang, East Java. The finding shows that there is no correlation among television viewing, parental attention, and the students＇ motivation to choose major at the senior high school. In other words, the possibility of choosing the major can be attributed to some other factors such as interest, talent, aspiration, and other expectation to achieve their goals

Influence of Media Attention on Investors'Heterogeneous Beliefs:A Case Study of China's Stock Market

With the rapid development of Internet media,Internet media coverage has more or less influence on investors'psychological level.This article uses Python technology to climb 2019.9 to 2020.1 of the monthly news reports on A share listed companies in the Snowball net,and studies the relationship between media attention and investors'heterogeneous beliefs.It is found that media attention is positively correlated with investors'heterogeneous beliefs,that is,investors are more likely to choose stocks frequently reported by media.Further research finds that media reports will strengthen investors'heterogeneous beliefs,affect investors'investment behavior,and ultimately lead to the increase of stock trading volume.

基于FasterNet和YOLOv5改进的玻璃绝缘子自爆缺陷快速检测方法

为了实现对电力输电线路中绝缘子缺陷实时快速的巡检需求,提出了一种结合FasterNet-tiny和YOLOv5-s-v6.1网络模型改进的缺陷快速检测算法FasterNet-YOLOv5。首先引入参数量小推理速度更快的FasterNet网络替换原先的CSPDarkNet53主干网络,加快网络的检测速度。然后结合由GhostNetv2网络提出的解耦全连接注意力机制(decoupled fully connected,DFC),在主干特征提取网络中设计了DFC-FasterNet模块,模块中的DFC Attention机制可以在特征提取过程中增大感受野,提升网络的检测精度。最后针对玻璃绝缘子自爆缺陷目标较小和背景较复杂的情况,重新设计Neck模块,提出BiFPN-F特征融合模块,使网络更精确地定位绝缘子缺陷区域。实验结果表明:改进后的算法可以快速精准定位,其均值平均精度(mean average precision,mAP)达到93.3%,相较于改进前提升5.67%,检测速度达到45.7 Hz,较改进前提升近1倍。同时与最新的YOLOv8n和YOLOv7-tiny相比,改进后的FasterNet-YOLOv5在自爆缺陷上的检测精度和速度更具优势,该文所提算法能够更快速地对绝缘子及其自爆缺陷实时定位识别。

内外双“管”下ESG表现对企业融资成本的影响研究

本文基于沪深A股上市工业企业2015-2020年面板数据,实证检验了ESG表现对企业融资成本的影响,以及内部控制和媒体关注在二者关系中的重要补充机制。研究结果显示,企业ESG表现及其三个维度对企业融资成本具有差异化负向效应,良好的环境责任和公司治理表现更能显著降低企业融资成本;内部控制和媒体关注度会放大ESG表现对企业融资成本的抑制作用,即随着内控水平和媒体关注度的提高,企业ESG表现及其三个维度对融资成本的抑制效果逐渐得到强化。其中,内部控制的强化效果在公司治理方面体现更为明显,媒体关注的强化效果则在环境责任方面更为突出。

数字化转型与企业ESG表现——基于媒体关注度与高管过度自信的双重视角

基于2017—2022年沪深A股制造业上市公司的面板数据,从媒体关注度和高管过度自信两个视角出发,实证研究上市公司数字化转型影响ESG表现的两条路径。相关数据研究表明:第一,数字化转型与企业ESG表现存在显著的线性相关关系;第二,高管过度自信会影响数字化转型对企业ESG表现的促进作用;第三,数字化转型通过媒体关注度影响企业ESG表现。稳健性检验结果表明,在考虑倾向得分匹配法、工具变量法、替换被解释变量等方法后,数字化转型对企业ESG表现的促进作用仍然成立。异质性分析结果表明,国有企业和大规模企业能更好地利用数字化转型,积极履行社会责任,帮助企业提升ESG表现,为企业实现高质量发展提供了指引。

数字化转型对企业ESG表现的影响研究

现阶段,通过数字化转型提升ESG表现已成为企业提升竞争力、实现可持续发展的重要策略选择。文章基于2011—2021年我国沪深两市A股制造业上市公司数据实证考察企业数字化转型对ESG表现的影响效应与作用机制。研究结果表明:企业数字化转型对ESG表现具有显著正向效应,且这一结论在进行多项内生性处理和稳健性检验后仍然成立;企业数字化转型主要通过提升企业绿色创新绩效、抑制管理者短视行为及提高资源配置效率三条途径提升ESG表现;分析师关注和媒体关注强化了企业数字化转型对ESG表现的促进作用;基于所有权性质、要素密集度及规模等级的不同,企业数字化转型对ESG表现的影响具有显著异质性。研究结论可为企业推进数字化转型、提升ESG表现提供相应对策建议。

基于改进YOLOv5s的输电线路螺栓缺销检测方法

针对无人机输电线路巡检图像中螺栓缺销检测精度较低、漏检较多的问题,提出了一种基于改进YOLOv5s的输电线路螺栓缺销检测方法。在Backbone部分嵌入Coordinate Attention注意力模块;在Neck部分原有的“FPN+PAN”结构的基础上,新增一条“自顶向下”的特征信息传递路径,跨越临近的同尺度特征层,与较浅层网络以加权融合的方式进行特征融合;将Head部分设置为解耦检测头,将对螺栓检测的分类任务与定位任务分开进行。改进后的YOLOv5s算法增强了对螺栓特征信息的学习能力。使用本方法在螺栓缺销数据集上实验,精确率提升了2.3%,召回率提升了3.4%,平均精度提升了3.1%,检测速度达到了41.1帧/秒,表明改进后的方法能提升输电线路螺栓缺销的检测能力,在智能巡检中具有一定的应用价值。

ESG表现如何影响企业高质量发展? ——兼论媒体关注的调节作用

ESG表现是衡量企业非财务业绩的综合指标,为企业高质量发展注入内生动力。以2010—2021年我国A股上市公司为研究样本,实证检验了ESG表现对企业高质量发展的影响。研究发现:ESG表现的提升能够显著促进企业高质量发展,且通过替换变量、滞后一期、缩短年限进行稳健性检验后,结论依然成立;媒体关注对ESG表现与企业高质量发展之间的关系有正向调节作用,媒体关注度越高,ESG表现对企业高质量发展的促进作用越强;ESG表现主要通过推动企业绿色技术创新和降低企业经营风险促进企业高质量发展;企业ESG表现的促进作用在国有企业和重污染企业中更为显著。以上研究表明了ESG的积极效应和媒体关注的外部治理作用,为相关部门完善和推广ESG政策提供了一定的经验证据。

三维各向异性TI介质中的P/S波快速解耦技术及在弹性波逆时偏移中的应用

随着多分量采集技术的发展,弹性波逆时偏移技术在三维各向异性介质复杂地质构造成像中得到了广泛的应用.然而耦合的P波场和S波场,会在传播过程中产生串扰噪声,降低弹性波逆时偏移的成像精度.为了解决这一问题,本研究针对具有倾斜各向异性对称轴的三维横向各向同性(Transverse Isotropy,TI)介质,提出了一种矢量弹性波场快速解耦方法,可以有效提高偏移剖面的成像质量.该方法首先通过坐标转换,将观测系统坐标系的垂直轴旋转到TI介质的对称轴方向,在新坐标系下,根据具有垂直对称轴的三维横向各向同性(Vertical Transverse Isotropy,VTI)介质中的分解算子,推导出三维TI介质解耦算子表达式.接着引入一种在空间域快速计算分解波场的方法,来实现空间域矢量P波场和S波场分离,极大地提高了计算效率.最后,通过点积成像条件,将提出的P/S波分解方法引入到三维TI介质弹性波逆时偏移中,得到高精度的PP和PS成像.与以往的波场分解方法相比,本文方法具有数值稳定和计算效率高的特点.数值算例表明,应用上述三维TI分解算子得到的偏移剖面有效压制了噪声,提高了成像质量.

ESG表现对制造业企业创新水平的影响机制研究

党的二十大报告指出坚持把发展经济的着力点放在实体经济上,而制造业是实体经济高水平发展的重要支撑。如何以创新驱动制造业高质量发展提升实体经济发展水平是我国当前发展的战略重点。同时,作为考察企业在环境、社会和治理方面表现的综合指标,环境、社会和治理(ESG)指标对企业的创新和绿色发展有重要影响。鉴于此,本研究以2009—2021年中国沪深A股上市公司中的制造业企业为研究样本,建立面板双向固定效应模型,从微观视角实证检验ESG表现对制造业企业创新水平的影响及作用机制。结果表明:良好的ESG表现可以有效提升制造业企业的创新水平,媒体关注度和政府补助在其中发挥部分中介作用。并且,这一促进作用在国有企业和大型企业中更为显著;东部地区企业的ESG表现对企业实质性创新水平的提升效果更强,而中西部地区企业的ESG表现对企业策略性创新的促进效应更强。为促进企业创新,助力实体经济实现高质量发展,应鼓励企业积极承担社会责任,健全ESG信息披露制度和表现评价体系,引导投资者树立ESG投资理念。

供应链数字化与企业ESG分歧——基于供应链创新与应用试点的准自然实验

在数字技术赋能供应链转型升级的背景下,厘清供应链数字化对企业ESG分歧的影响对企业践行ESG理念与推动现代供应链发展具有重要意义。本文基于供应链创新与应用试点工作,以2015~2022年上市公司为研究样本,利用双重差分模型探究供应链数字化对企业ESG分歧的影响。研究结果显示,供应链数字化降低了企业ESG分歧,环境信息披露的增加与媒体关注度的提高是供应链数字化降低企业ESG分歧的主要渠道。进一步分析发现,供应链数字化带来的企业ESG分歧下降,有助于缓解融资约束、降低经营风险和提升企业价值。本文的研究为ESG分歧的影响因素提供了新的解释视角,对于理解供应链创新与应用试点政策具有一定的启示意义。

ESG评级软监管对企业碳排放强度的影响研究

企业碳减排不仅事关其自身长远发展,更对我国“双碳”目标实现具有重要意义。基于此,采用2009—2021年沪深A股上市公司制造业企业数据,以资本市场第三方ESG评级公布为准自然实验,运用多时点DID方法系统考察了ESG评级软监管对上市公司碳排放强度的影响。研究发现,ESG评级软监管能够显著降低企业碳排放强度。影响机制检验表明,ESG评级软监管主要通过提高媒体关注度与抑制管理者短视途径影响企业碳排放强度的治理。调节效应显示,ESG评级分歧度会削弱ESG评级的碳强度治理效果。上述关系在环境规制力度较强以及机构投资持股比例较高的样本中更显著。ESG评级软监管还会降低同城市、同行业中其他企业碳排放强度,呈现出一定的溢出效应。研究结论不仅拓展了资本市场ESG评级监管以及企业碳排放强度各自领域的相关文献与研究视角,也对完善优化资本市场ESG评级监管及更有效促进企业碳减排形成有益启发。

智慧城市试点政策能否提高ESG信息披露质量?——基于准自然实验的实证研究

智慧城市作为推动城市经济高质量发展的重要战略,可以帮助上市企业加快可持续发展并提高其信息披露质量。以企业ESG信息披露质量为研究视角,运用多时点双重差分的固定效应模型,基于2010—2021年A股上市企业数据,探讨智慧城市试点政策对企业ESG信息披露质量的影响效应及作用机制。研究发现:(1)智慧城市试点政策对企业ESG信息披露质量具有显著的促进作用,且该结论通过一系列稳健性检验依然成立。(2)智慧城市试点政策通过研发创新、媒体关注和数字化转型三条路径增强了企业ESG信息披露质量。(3)在内部控制指数低、市场化程度低、财政支出水平高的样本中,智慧城市试点政策对于企业ESG信息披露质量的提升效果更为显著。研究结论为企业、市场投资者和政府提供了丰富的建议。