There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .
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自然行为由不可预测的动力学组成,可以突然切换并在许多不同的时间尺度上展开。尽管在受约束或简化的基于任务的条件下构建行为的表示方面已经找到了一些成功,但由于它们假设单一的时间动力学规模,因此无法将其中许多模型应用于自由和自然主义的设置。在这项工作中,我们跨多个尺度(BAMS)引入引导程序,这是一种多尺度表示模型:我们结合了一个汇总模块,该模块汇总了与具有不同时间接收场的编码器上提取的特征,并设计了一组潜在目标,以进行引导程序各个空间中的表示,以鼓励不同时间尺度的分离。我们首先将我们的方法应用于在不同地形类型中导航的四倍的数据集上,并表明我们的模型捕获了行为的时间复杂性。然后,我们将我们的方法应用于MABE 2022多代理行为挑战,我们的模型在两个子任务中排名第三,第一个排名第1,并在分析行为时显示了合并多时间尺度的重要性。
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Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.
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An expansion of aberrant brain cells is referred to as a brain tumor. The brain's architecture is extremely intricate, with several regions controlling various nervous system processes. Any portion of the brain or skull can develop a brain tumor, including the brain's protective coating, the base of the skull, the brainstem, the sinuses, the nasal cavity, and many other places. Over the past ten years, numerous developments in the field of computer-aided brain tumor diagnosis have been made. Recently, instance segmentation has attracted a lot of interest in numerous computer vision applications. It seeks to assign various IDs to various scene objects, even if they are members of the same class. Typically, a two-stage pipeline is used to perform instance segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo takes dataset as picture format and corresponding text file. You Only Look Once (YOLO) is a viral and widely used algorithm. YOLO is famous for its object recognition properties. You Only Look Once (YOLO) is a popular algorithm that has gone viral. YOLO is well known for its ability to identify objects. YOLO V2, V3, V4, and V5 are some of the YOLO latest versions that experts have published in recent years. Early brain tumor detection is one of the most important jobs that neurologists and radiologists have. However, it can be difficult and error-prone to manually identify and segment brain tumors from Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the condition, an automated brain tumor detection system is necessary. The model of the research paper has three classes. They are respectively Meningioma, Pituitary, Glioma. The results show that, our model achieves competitive accuracy, in terms of runtime usage of M2 10 core GPU.
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Shape displays are a class of haptic devices that enable whole-hand haptic exploration of 3D surfaces. However, their scalability is limited by the mechanical complexity and high cost of traditional actuator arrays. In this paper, we propose using electroadhesive auxetic skins as a strain-limiting layer to create programmable shape change in a continuous ("formable crust") shape display. Auxetic skins are manufactured as flexible printed circuit boards with dielectric-laminated electrodes on each auxetic unit cell (AUC), using monolithic fabrication to lower cost and assembly time. By layering multiple sheets and applying a voltage between electrodes on subsequent layers, electroadhesion locks individual AUCs, achieving a maximum in-plane stiffness variation of 7.6x with a power consumption of 50 uW/AUC. We first characterize an individual AUC and compare results to a kinematic model. We then validate the ability of a 5x5 AUC array to actively modify its own axial and transverse stiffness. Finally, we demonstrate this array in a continuous shape display as a strain-limiting skin to programmatically modulate the shape output of an inflatable LDPE pouch. Integrating electroadhesion with auxetics enables new capabilities for scalable, low-profile, and low-power control of flexible robotic systems.
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Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model.
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远程光插图学(RPPG)是一种快速,有效,廉价和方便的方法,用于收集生物识别数据,因为它可以使用面部视频来估算生命体征。事实证明,远程非接触式医疗服务供应在COVID-19大流行期间是可怕的必要性。我们提出了一个端到端框架,以根据用户的视频中的RPPG方法来衡量人们的生命体征,包括心率(HR),心率变异性(HRV),氧饱和度(SPO2)和血压(BP)(BP)(BP)用智能手机相机捕获的脸。我们以实时的基于深度学习的神经网络模型来提取面部标志。通过使用预测的面部标志来提取多个称为利益区域(ROI)的面部斑块(ROI)。应用了几个过滤器,以减少称为血量脉冲(BVP)信号的提取的心脏信号中ROI的噪声。我们使用两个公共RPPG数据集培训和验证了机器学习模型,即Tokyotech RPPG和脉搏率检测(PURE)数据集,我们的模型在其上实现了以下平均绝对错误(MAE):a),HR,1.73和3.95 BEATS- beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-beats-s-s-s-s-s-y-peats-beats-beats-beats-ship-s-s-s-in-chin-p-in-in-in-in-in-c--in-in-c-le-in-in- -t一下制。每分钟(bpm),b)分别为HRV,分别为18.55和25.03 ms,c)对于SPO2,纯数据集上的MAE为1.64。我们在现实生活环境中验证了端到端的RPPG框架,修订,从而创建了视频HR数据集。我们的人力资源估计模型在此数据集上达到了2.49 bpm的MAE。由于没有面对视频的BP测量不存在公开可用的RPPG数据集,因此我们使用了带有指标传感器信号的数据集来训练我们的模型,还创建了我们自己的视频数据集Video-BP。在我们的视频BP数据集中,我们的BP估计模型的收缩压(SBP)达到6.7 mmHg,舒张压(DBP)的MAE为9.6 mmHg。
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