支持向量回归（SVR）的古典机器学习模型（SVR）广泛用于回归任务，包括天气预报，股票市场和房地产定价。但是，SVR的实际可实现的量子版本仍有待配制。我们设计了基于退火的算法，即模拟和量子古典的混合动力车，用于训练两个SVR模型，并比较他们对Python Scikit-Greats包的SVR实现和基于SVR的最新算法的实证性能面部地标检测（FLD）问题。我们的方法是为训练SVR模型的优化问题推导出二次非判断 - 二进制制定，并使用退火解决这个问题。使用D-Wave的混合求解器，我们构建了一项量子辅助的SVR模型，从而展示了关于地标检测精度的古典模型的略有优势。此外，我们观察到基于退火的SVR模型预测与通过贪婪优化程序训练的SVR模型相比具有较低差异的地标。我们的工作是一个概念验证示例，用于使用小型训练数据集将量化的SVR应用于监督的学习任务。

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.

心脏死亡和心律不齐占全世界所有死亡的很大一部分。心电图（ECG）是用于心血管疾病的最广泛使用的筛查工具。传统上，ECG信号是手动分类的，需要经验和良好的技巧，同时又耗时且容易出错。因此，机器学习算法因其执行复杂数据分析的能力而被广泛采用。从ECG（主要是Q，r和s）中引入的特征广泛用于心律不齐。在这项工作中，我们证明了使用混合功能和三种不同模型的ECG分类的性能提高了，这是我们过去提出的1D卷积神经网络（CNN）模型的建立。这项工作中提出的基于RR间隔的模型的准确性为98.98％，这是对基线模型的改进。为了使模型免疫噪声，我们使用频率功能更新了模型，并在噪声的存在下实现了良好的持续性能，精度略低为98.69％。此外，开发了另一个结合频率特征和RR间隔功能的模型，在嘈杂的环境中，持续性能良好，高精度为99％。由于其高精度和噪声免疫力，结合了多个混合功能的拟议模型非常适合门诊可穿戴感应应用。

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。

光酸产生剂（PAG）是在暴露于光线时释放酸（$ H ^ + $离子）的化合物。这些化合物是用于制造半导体逻辑和存储芯片的光刻工艺的关键组分。半导体需求的指数增加突出了发现新型光酸发生器的需求。虽然De Novo分子设计使用深度生成模型被广泛用于药物发现和材料设计，但其在创建新颖的光酸发电机的应用构成了几个独特的挑战，例如缺乏房地产标签。在本文中，我们突出了这些挑战，并提出了一种生成的建模方法，该方法利用预先训练的深度自动化器和循环技术的条件生成。在主题专家的帮助下评估了拟议方法的有效性，表明在创建新型光酸生成器之外的应用方法的承诺。

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/ .

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.