Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another population. Most existing transfer learning approaches are based on fine-tuning pre-trained neural network models, and fail to provide crucial uncertainty quantification. We develop a statistical framework for model predictions based on transfer learning, called RECaST. The primary mechanism is a Cauchy random effect that recalibrates a source model to a target population; we mathematically and empirically demonstrate the validity of our RECaST approach for transfer learning between linear models, in the sense that prediction sets will achieve their nominal stated coverage, and we numerically illustrate the method's robustness to asymptotic approximations for nonlinear models. Whereas many existing techniques are built on particular source models, RECaST is agnostic to the choice of source model. For example, our RECaST transfer learning approach can be applied to a continuous or discrete data model with linear or logistic regression, deep neural network architectures, etc. Furthermore, RECaST provides uncertainty quantification for predictions, which is mostly absent in the literature. We examine our method's performance in a simulation study and in an application to real hospital data.

The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.

解释视觉场景的含义不仅需要识别其成分对象，还需要对象相互关系的丰富语义表征。在这里，我们通过将现代计算技术应用于复杂自然场景引起的人类脑反应的大规模7T fMRI数据集，研究视觉语义转换的神经机制。使用通过将语言深度学习模型应用于人类生成的场景描述获得的语义嵌入，我们确定了编码语义场景描述的大脑区域的广泛分布网络。重要的是，这些语义嵌入比传统对象类别标签更好地解释了这些区域的活动。此外，尽管参与者没有积极从事语义任务，但它们还是活动的有效预测指标，这表明Visuo-Semantic转换是默认的视觉方式。为了支持这种观点，我们表明，可以直接通过大脑活动模式直接将场景字幕的高度精确重建。最后，经过语义嵌入训练的经常性卷积神经网络进一步超过了语义嵌入在预测大脑活动时的语义嵌入，从而提供了大脑视觉语义转换的机械模型。这些实验和计算结果在一起表明，将视觉输入转换为丰富的语义场景描述可能是视觉系统的核心目标，并且将重点放在这一新目标上可能会导致改进人类大脑中视觉信息处理的模型。

存储数十万个材料结构及其相应特性的开放材料数据库已成为现代计算材料科学的基石。然而，模拟的原始输出，例如分子动力学模拟的轨迹和密度功能理论计算的电荷密度，通常由于其较大的尺寸而没有共享。在这项工作中，我们描述了一个基于云的平台，以促进原始数据的共享，并在云中启用快速的后处理以提取用户定义的新属性。作为初始演示，我们的数据库目前包括6286个用于无定形聚合物电解质的分子动力学轨迹和5.7吨数据库。我们在https://github.com/tri-amdd/htp_md上创建一个公共分析库，使用专家设计的功能和机器学习模型，从原始数据中提取多个属性。该分析是通过云中的计算自动运行的，然后结果填充可以公开访问的数据库。我们的平台鼓励用户通过公共接口贡献新的轨迹数据和分析功能。新分析的属性将纳入数据库。最后，我们在https://www.htpmd.matr.io上创建了一个前端用户界面，以浏览和可视化数据。我们设想该平台将是一种为计算材料科学界共享原始数据和新见解的新方法。

超越地球轨道的人类空间勘探将涉及大量距离和持续时间的任务。为了有效减轻无数空间健康危害，数据和空间健康系统的范式转移是实现地球独立性的，而不是Earth-Reliance所必需的。有希望在生物学和健康的人工智能和机器学习领域的发展可以解决这些需求。我们提出了一个适当的自主和智能精密空间健康系统，可以监控，汇总和评估生物医学状态;分析和预测个性化不良健康结果;适应并响应新累积的数据;并提供对其船员医务人员的个人深度空间机组人员和迭代决策支持的预防性，可操作和及时的见解。在这里，我们介绍了美国国家航空航天局组织的研讨会的建议摘要，以便在太空生物学和健康中未来的人工智能应用。在未来十年，生物监测技术，生物标志科学，航天器硬件，智能软件和简化的数据管理必须成熟，并编织成精确的空间健康系统，以使人类在深空中茁壮成长。

空间生物学研究旨在了解太空飞行对生物的根本影响，制定支持深度空间探索的基础知识，最终生物工程航天器和栖息地稳定植物，农作物，微生物，动物和人类的生态系统，为持续的多行星寿命稳定。要提高这些目标，该领域利用了来自星空和地下模拟研究的实验，平台，数据和模型生物。由于研究扩展到低地球轨道之外，实验和平台必须是最大自主，光，敏捷和智能化，以加快知识发现。在这里，我们介绍了由美国国家航空航天局的人工智能，机器学习和建模应用程序组织的研讨会的建议摘要，这些应用程序为这些空间生物学挑战提供了关键解决方案。在未来十年中，将人工智能融入太空生物学领域将深化天空效应的生物学理解，促进预测性建模和分析，支持最大自主和可重复的实验，并有效地管理星载数据和元数据，所有目标使生活能够在深空中茁壮成长。

参数化量子电路（PQCS）的变分训练支撑在近期嘈杂的中间刻度量子（NISQ）器件上采用的许多工作流程。它是一种混合量子 - 经典方法，最小化相关的成本函数，以便培训参数化的ansatz。在本文中，我们适应\ Cite {Goodfellowive，Li2017Visualizing}和\ Cite {Draxler2018essentially}中使用的连接测试中引入的神经网络的定性损失景观特征，以研究PQC培训中的丢失景观功能。我们使用双层电路Ansatz验证了在简单的回归任务上培训的PQC的结果，该方法由参数化旋转门和缠绕栅极的交替层组成。多个电路培训，培训3美元$不同的批量梯度优化器：随机梯度下降，量子自然梯度和亚当。我们确定景观中的大功能，可以导致培训工作流程更快地收敛。

对头部磁共振成像（MRI）检查的需求不断增长，以及全球放射科医生的短缺，导致在全球报告头部MRI扫描所花费的时间增加。对于许多神经系统疾病，这种延迟会导致发病率和死亡率增加。一种自动分解工具可以通过在成像时识别异常并确定这些扫描的报告优先级来减少异常检查的报告时间。在这项工作中，我们提出了一个卷积神经网络，用于检测$ \ text {t} _2 $加权的头部MRI扫描中临床上相关的异常。使用经过验证的神经放射学报告分类器，我们从两家英国两家大型医院进行了43,754张标记的数据集，以进行模型培训，并在800张测试集上证明了准确的分类（AUC下的区域（AUC）= 0.943），由800张扫描集进行了标签。神经放射学家团队。重要的是，当仅在一家医院接受扫描培训时，模型从另一家医院进行了扫描（$ \ delta $ auc $ \ leq $ 0.02）。一项模拟研究表明，我们的模型将使异常检查的平均报告时间从28天到14天，并从两家医院的9天到5天，这表明在临床分类环境中使用了可行性。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

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/ .