GHOSTS AND GORE A closer look at depiction of women in Thai horror movies

The 1999 Thai horror movie Nang Nak was the turning point for the“new wave”of Thai movies,and it became a model for the horror genre in Thailand.Even today,vengeance from female ghosts remains a central theme of Thai horror movies.

Ghosts in the shell:identification of microglia in the human central nervous system by P2Y12 receptor

Microglia are the tissue resident macrophages of the brain and represent the sole immune population located in the parenchyma of the central nervous system （CNS）. These cells are hidden be-tween neurons, astrocytes as well as oligodendrocytes and account for only 5-10% of CNS cells. Even though microglia were already identified in 1913 by the Spanish neuroanatomist Ramon y Cajal and further seminally investigated by his student Pio del Rio Hortega,

On the Stringy Ghosts Which We Call the Missing Dark Energy of the Cosmos

Dark energy is explained using familiar notions and concepts used in quantum field theory, string theory and the exact mathematical theory of spacetime. The main result of the present work is first a new mathematical definition of pre-quantum spacetime (QST) as a multiset made of infinitely many empty Cantor sets connected to pre-quantum wave empty set (QW) and the pre-quantum particle (QP) zero set via the cobordism equation ∂(QW) = (QP)U(QST). Second, and in turn, this new path of reasoning is used to validate the quantum splitting of Einstein’s E = mc2 into the sum of the ordinary energy E = mc2/22 of the quantum particle and the dark energy E = mc2(21/22) of the quantum wave, used predominantly to explain the observed accelerated expansion of the universe.

Study on Genetic Inactivation Bacterial Ghosts of Pasteurella multocida based PhiX174 Gene E Lysis Cassette Mediated

[Objective] To study on genetic inactivation bacterial ghosts of Pasteurella multocida based PhiX174 gene E lysis cassette mediated. [ Method ] Recombinant pPBA1100-e was constructed by which the gene E of bacteriophage Phix174 and temperature sensitivity expressing control system hybridized with plasmid pPBA1100 by genetic engineering method. Recombinant was transformed to Pasteurella multocida and lysis gene E expressed by temperature induction. Recombinant was detected by restriction endonuclease. Cell morphology of bacterial ghost of Pasteurella mul- tocida was observed by scanning electron microscopy and inactivation ratio was estimated by CFU analysis. I Result~ The results indicated that the recombination plasmid presented three bands by restriction endonuclease and agarose electrophoresis and that molecular weight of every band ac- corded with theoretical value. The result of SEM observing showed that recombination plasmid expressed successfully in P. multocida and produced bacterial ghost. The result of CFU detecting demonstrated that inactivation ratio of P. multocida reached 99 per cent. ~Conclusion~ This study pro- vided technical bases for the preparation of antigen vaccine of natural bacterial outer membrane protein.

Familiar Ghosts:Imagining Lives,Re-imagining the Nation,Inventing the Future

This article focuses on novels that,located on the boundary between biography,autobiography and fiction,between detailed archival historical research and imagination,between the documentary and the speculative,seek to reconstruct the life of an ancestor of the writer-narrator to reflect on the traumas,exploitation,hopes,and desires of generations who,in their diasporas,also helped create their modern nations,or whose story challenges the exclusions on which the concept of the nation has been built.The texts discussed are Melania Mazzucco's Vita,Vona Groarke's Hereafter:The Telling Life of Ellen O'Hara,Wu Ming 2 and Antar Mohamed's Timira:Romanzo Meticcio,and Maryse Conde's Victoire,les saveurs et les mots.At their center is the negotiation between the individual truth and the relational story across generations on the one hand,and,on the other hand,the tracing of collective histories such as those of nation,of colonization,of diaspora,of internal and external displacement,of rights and emancipation(the downtrodden,the poor,women,the migrant,the refugees),of the origins of dispossession and the permanence of its effects.At the core of the narratives is also the ghostliness of the erased(from history,from memory,from citizenship),the departed(in the sense of being dead and of having traveled away),and the in-between(generic:between the historical and documentary on the one hand,and the novelistic,the fable,fantasy on the other;geographical:between countries,between places,nationalities,national affiliations;historical:across generations;racial:the mestizo,the mulatto).It is,specifically,the biofictional imagination that enables these diasporic,transnational,transracial accounts to open up the possibility of a different,liberating future,by offering the space to imagine such a future,but also by recognizing,through its explicit acknowledgement of its recourse to fictionality,that it does not aim to construct a new myth to replace historical fact,but that it invites all of us to imagine,and strive to bring into existence,a different reality.

Cancer cell membrane-coated bacterial ghosts for highly efficient paclitaxel delivery against metastatic lung cancer

Chemotherapy is one of the major approaches for the treatment of metastatic lung cancer,although it is limited by the low tumor delivery efficacy of anticancer drugs.Bacterial therapy is emerging for cancer treatment due to its high immune stimulation effect;however,excessively generated immunogenicity will cause serious inflammatory response syndrome.Here,we prepared cancer cell membrane-coated liposomal paclitaxel-loaded bacterial ghosts(LP@BG@CCM)by layer-by-layer encapsulation for the treatment of metastatic lung cancer.The preparation processes were simple,only involving film formation,electroporation,and pore extrusion.LP@BG@CCM owned much higher 4T1 cancer cell toxicity than LP@BG due to its faster fusion with cancer cells.In the 4T1 breast cancer metastatic lung cancer mouse models,the remarkably higher lung targeting of intravenously injected LP@BG@CCM was observed with the almost normalized lung appearance,the reduced lung weight,the clear lung tissue structure,and the enhanced cancer cell apoptosis compared to its precursors.Moreover,several major immune factors were improved after administration of LP@BG@CCM,including the CD4^(+)/CD8a^(+)T cells in the spleen and the TNF-α,IFN-γ,and IL-4 in the lung.LP@BG@CCM exhibits the optimal synergistic chemo-immunotherapy,which is a promising medication for the treatment of metastatic lung cancer.

Ghosts in the Machine

Resurrecting loved ones has become a new business model for individual Al developers,but amid the hype,many people and even big tech companies are struggling to find paths to monetize the nascent sector.

基于YOLO v5的农田杂草识别轻量化方法研究

针对已有杂草识别模型对复杂农田环境下多种目标杂草的识别率低、模型内存占用量大、参数多、识别速度慢等问题,提出了基于YOLO v5的轻量化杂草识别方法。利用带色彩恢复的多尺度视网膜(Multi-scale retinex with color restoration, MSRCR)增强算法对部分图像数据进行预处理,提高边缘细节模糊的图像清晰度,降低图像中的阴影干扰。使用轻量级网络PP-LCNet重置了识别模型中的特征提取网络,减少模型参数量。采用Ghost卷积模块轻量化特征融合网络,进一步降低计算量。为了弥补轻量化造成的模型性能损耗,在特征融合网络末端添加基于标准化的注意力模块(Normalization-based attention module, NAM),增强模型对杂草和玉米幼苗的特征提取能力。此外,通过优化主干网络注意力机制的激活函数来提高模型的非线性拟合能力。在自建数据集上进行实验,实验结果显示,与当前主流目标检测算法YOLO v5s以及成熟的轻量化目标检测算法MobileNet v3-YOLO v5s、ShuffleNet v2-YOLO v5s比较,轻量化后杂草识别模型内存占用量为6.23 MB,分别缩小54.5%、12%和18%;平均精度均值(Mean average precision, mAP)为97.8%,分别提高1.3、5.1、4.4个百分点。单幅图像检测时间为118.1 ms,达到了轻量化要求。在保持较高模型识别精度的同时大幅降低了模型复杂度,可为采用资源有限的移动端设备进行农田杂草识别提供技术支持。

改进YOLOv7-tiny的手语识别算法研究

在与听障人士进行交流时,常常会面临交流不便的困难,文中提出一种手语识别的改进模型来解决这个困难。该模型基于YOLOv7-tiny网络模型,并对其进行了多项改进,旨在提高模型的精度和速度。首先,通过对CBAM注意力机制的通道域进行改进,解决了因降维而造成的通道信息缺失问题,并将改进后的CBAM加入到YOLOv7-tiny的Neck层中,从而使模型更加精准地定位和识别到关键的目标;其次,将传统的CIoU边界框损失函数替换为SIoU边界框损失函数,以加速边界框回归的同时提高定位准确度;此外,为了减少计算量并加快检测速度,还将颈部层中的普通卷积模块替换为Ghost卷积模块。经过实验测试,改进后网络模型的平均精度均值(mAP)、精准率和召回率分别提升了5.31%、6.53%、2.73%,有效地提高了手语识别网络的检测精确度。

结合Segformer与增强特征金字塔的文本检测方法

针对自然场景文本检测算法中的小尺度文本漏检、类文本像素误检以及边缘定位不准确的问题,提出一种基于Segformer和增强特征金字塔的文本检测模型。该模型首先采用基于混合Transformer(mix Trans-former,MiT)的编码器生成多尺度特征图;然后,在具有特征金字塔结构解码器的上采样部分,提出级联融合注意力模块,通过全局平均池化、全局最大池化和Ghost模块获取全局通道信息并保留文本特征;接着,在解码器的特征融合部分提出两级正交融合注意力模块,利用非对称卷积分别从水平和垂直方向进行信息增强;最后,利用可微分二值化对结果进行后处理。将本文方法在ICDAR2015、ShopSign1265和MTWI 3个数据集上进行实验,相比于其他8种方法,本文方法的F值均为最优,分别达到了87.8%、59.1%和74.8%。结果表明,本文方法有效提高了文本检测的准确率。

基于改进U-Net的轻量级眼底病变分割算法设计

精准的糖尿病视网膜病变的分割是实现视网膜病变自动诊断的前提条件和关键步骤,然而现有大部分的分割模型存在着参数量大、模型训练效果不理想、甚至是无法正常处理数据集等局限性。为此,在原U-Net网络中加入改进的Ghost卷积模块与多尺度特征融合模块,提出一种改进U-Net眼底病变分割图像的算法。该模型能以少量的参数量、较低的计算复杂度获得良好的分割结果。利用GhostModel替换原始卷积,设计出Ghost卷积与Ghost下采样卷积模块,在保证准确度的同时降低参数量;设计出一种轻量级的Half-UNet多尺度特征融合模块来获取多尺度信息,针对不同尺度病变目标,引入CBAM注意力机制以改善其适应性,从而更好的提取细小的病变信息。改进后的模型在e_optha与IDRiD两个公开数据集上的mIoU分别为61.42%、61.84%,F1-Score分别为70.59%、69.41%。模型参数量、FLOPs分别仅为5.48M、35.46GMac,较U-Net、Att-UNet等模型更加精简,分割精度更高。

WGAN-GP结合CBAM-VGG16轻量化网络滚动轴承故障诊断

针对滚动轴承故障诊断中存在的故障数据不平衡且诊断效率低的问题,提出一种将改进的生成对抗网络(Wasserstein Generative Adversarial Network with Gradient Penalty,WGAN-GP)和轻量化卷积神经网络相结合的滚动轴承故障诊断方法。首先,利用连续小波变换(Continuous Wavelet Transform,CWT)生成二维时频图,并通过WGAN-GP进行数据增强;其次在视觉几何群网络(Visual Geometry Group Network-16,VGG16)的基础上,引入Ghost模块和全局平均池化(Global Average Pooling,GAP)对其进行轻量化改进;再次,利用卷积注意力模块(Convolutional Block Attention Module,CBAM)和带重启的余弦退火衰减法提高VGG16模型的性能,构建CBAM-VGG16轻量化卷积神经网络模型,将增强后的数据进行预处理后输入到模型中进行训练,建立故障诊断模型;最后采用西储大学轴承数据集进行模型验证和分析。实验结果表明:该方法证实了故障数据不足时进行故障诊断的可行性,缩短了模型的训练时间、诊断时间并缩减了模型的大小和参数量,提高了故障诊断的效率和准确率。

基于轻量化YOLOv5的交通标志检测

为了提高道路交通标志的检测速度,提出一种基于轻量化YOLOv5的改进模型。首先,使用Ghost卷积和深度分离卷积(DWConv)构建新的主干模块,减少计算量和参数量;引入加权特征融合网络(BiFPN)结构,增强特征融合能力;将CIoU损失函数替换为SIoU损失函数,关注真实锚框与预测的角度信息,提升检测精度。其次,对TT100K数据集进行优化,筛选出标签个数大于200的交通标志图片和标注信息共24类。最后,实验结果取得84%的准确率、81.2%的召回率和85.4%的所有类别平均精确率的平均值mAP@0.5,相比原始YOLOv5,参数量减少29.0%,计算量减少29.4%,mAP@0.5仅下降0.1百分点,检测帧率提升了34帧/s。使用改进后的模型进行检测,检测速度有了明显提升,基本达到了在保持检测精度的基础上压缩模型的目的。

基于YOLO v5的水稻害虫分类

针对水稻害虫识别过程中存在的检测难度大、模型精度低、计算量大等问题,以稻纵卷叶螟等14类水稻害虫为研究对象,改进了YOLO v5检测算法,引入高效通道注意力机制(efficient channel attention, ECA)与EIoU(efficient-IoU)损失函数,并结合Ghost卷积,提出了一种基于改进的YOLO v5水稻害虫识别方法:(1)通过引入ECA注意力机制实现对水稻害虫识别过程中重要信息的处理,采用跨通道信息交互,保证模型性能和降低复杂度;(2)引入EIoU损失函数代替CIoU(complete-IoU)损失函数,从而降低原有CIoU损失函数存在的回归精度问题;(3)利用Ghost卷积替换CBS模块及C3模块中的标准卷积,实现模型轻量化处理。结果表明,改进后的模型较原始YOLO v5模型精度略微提升,参数量减少,模型体积降低至7.38 MB,较原模型减少了46%,与YOLO v7、Faster-RCNN模型相比,mAP比YOLO v7高1.49百分点,比Faster-RCNN高12.89百分点,且本研究模型体积最小,检测速度满足实时性要求,使水稻害虫检测识别能够更加高效地完成,为水稻害虫检测提供了一种更优的方法,对于防治水稻害虫有重要意义。

基于改进YOLOv8算法的绝缘子缺陷检测模型

目前YOLO目标检测算法在绝缘子缺陷检测领域任然是最主流的方法,然而现有的YOLO模型框架参数量庞大导致户外部署难度加大,同时户外拍摄的绝缘子图像背景复杂,其缺陷更是微小导致难以被检测。针对上述问题,本文提出了一种基于YOLOv8n目标检测框架而改进得到的绝缘子缺陷检测模型YOLOv8-GCS,以降低模型的参数量并提高模型的检测精度。首先将模型中的C2f模块换成更加轻量级的Ghost卷积模块,以降低模型的计算量和参数量。然后在主干网络末尾和第二个检测头处加入CoordAtt注意力模块,抑制复杂背景对绝缘子缺陷部位的影响从而提高模型的检测精度。最后再引入一个SPD-Conv模块,让网络模型在二倍下采样的过程中无重要信息的损失同时增强网络模型对重要特征的学习率,进一步提高模型的检测性能。分析实验结果可知,本文算法与基线模型相比mAP50提高了4%,召回率和查全率分别提高了4.7%和1.3%,参数量降低了26.7%,保存结果的权重文件大小降低了1.5 MB,绝缘子破损和闪络缺陷的AP 50分别提高了4%和8.1%。

基于改进YOLOv5的铝型材表面缺陷检测算法

针对工业生产中铝合金型材表面缺陷在实际检测中出现漏检和误检的情况,提出一种YOLOv5-Ghost-CBAM-BiFPN模型对铝型材缺陷进行更加精确的检测。首先在YOLOv5 Backbone网中引入了一个轻量级Ghost模块,在保证准确性的前提下显著提高了检测速度。其次,将卷积块注意机制(CBAM)模块添加到Backbone网络的卷积层,以增强特征提取,进一步提高检测精度。此外,考虑到铝型材缺陷尺寸差异,在Neck模块中使用了用于多尺度特征融合的双向特征金字塔网络(Bi-FPN)来聚合不同缺陷类型的特征。实验表明:优化后的模型mAP、精确率P、召回率R都有明显提高。

基于Ghost-SE-Res2Net的多模型融合语音唤醒词检测方法

语音唤醒词检测(WWD)是语音交互中的关键技术,选择合适大小的检测窗对WWD性能的影响很大。提出一种新的多模型融合方法,通过融合小检测窗和大检测窗的检测结果来提高WWD性能。多模型融合方法包含两个分类模型,分别使用小检测窗和大检测窗,均基于轻量化的挤压与激励残差网络(SE-Res2Net)模块,即GhostSE-Res2Net,SE-Res2Net结构的多尺度机制可显著提升WWD的能力。在Ghost-SE-Res2Net中,首先使用Ghost卷积替换SE-Res2Net中的普通卷积以降低模型参数量,然后使用注意力池化层替换SE-Res2Net中的全局平均池化层进一步提升WWD能力。在实际检测时融合连续3个小检测窗模型的检测结果的最大值和1个大检测窗模型的检测结果,来判断唤醒词是否被触发。在训练时引入困难样本挖掘算法,选择性地学习较难检测的唤醒词信息以提高分类模型的检测性能。在包含2个唤醒词的Mobvoi数据集上评估系统性能,实验结果表明,在每小时0.5次错误唤醒的情况下,该系统在2个唤醒词上的错误拒绝率分别为0.46%和0.43%,实现了与先进基线相似的性能,并且系统参数量比基线少31%。

面向交通场景的轻量级行人检测算法

针对交通场景下行人检测模型网络复杂、参数量大以及难以在低性能设备上部署的问题,基于YOLOv5s网络模型提出了一种改进的轻量级行人检测算法。首先,使用Ghost模块重构YOLOv5s网络进行特征提取,降低模型的参数量和计算量,提高推理速度。其次,引入坐标注意力机制提高模型对目标特征的提取能力,提升其对小目标行人的检测效果。最后,采用SIoU损失函数加快模型的收敛速度,提高模型的识别准确率。实验结果表明,改进后的算法能保证较高的检测精度,与原始YOLOv5s算法相比参数量减少47.1%,计算量减少48.7%,提高了交通场景下行人检测的速度且易于部署。

基于YOLOv5-TGs的PCB缺陷检测算法研究

针对目前PCB缺陷检测算法在实际应用中检测精度低等问题,提出基于改进YOLOv5s的PCB缺陷检测算法YOLOv5-TGs。该算法以YOLOv5s算法模型为基础,首先在主干网络中引入Swin Transformer结构,并取代C3模块中的Bottleneck模块,并使用Ghost卷积模块替换Conv模块,降低了模型的计算复杂度,实现轻量化,同时增加了其接收域,增强PCB缺陷的小目标的特征表达能力;其次,在颈部网络的C3结构后面添加全局注意力机制,更大程度地保留通道和空间信息,在减少特征信息弥散的情况下放大全局跨纬度的交互特征,提高检测效率。最后用SIoU损失函数来代替原有的CIoU损失函数,通过在损失函数代价中引入方向性,加快模型收敛速度,提高回归精度。本文实验使用的是北京大学实验室公开发布的PCB缺陷数据集,结果表明:改进算法的平均精度均值达到98.2%,精确率达到95.5%;相较于YOLOv5s,改进算法的平均精度均值提升了7.3%,精确率提升了7.5%。