许多钥匙孔干预依赖于双手动处理外科手术器械，强迫主要外科医生依靠第二个外科医生作为相机助理。除了过度涉及手术人员的负担外，这可能导致图像稳定性降低，增加任务完成时间，有时由于任务的单调而有时会出现错误。由一组基本说明控制的机器人内窥镜持有者已被提出作为替代方案，但它们的不自然处理可能会增加（SOLO）外科医生的认知负荷，这阻碍了它们的临床验收。如果机器人内窥镜持有者通过语义上丰富的指令与操作外科医生合作的机器人内窥镜持有者，则可以实现手术工作流程的更无缝集成。作为概念证明，本文介绍了一种新颖的系统，为外科医生和机器人内窥镜支架之间的协同相互作用铺平了道路。该拟议的平台允许外科医生执行生理协调和导航任务，而机器人臂自动执行内窥镜定位任务。在我们的系统中，我们提出了一种基于外科刀具分割的新型工具提示定位方法和一种新型的视觉伺服方法，可确保内窥镜摄像机的平滑和适当的运动。我们验证了我们的视觉管道并运行了对该系统的用户学习。通过使用欧洲妇科手术课程验证的腹腔镜运动来确保研究的临床相关性，涉及双部手动协调和导航。我们拟议的系统的成功应用提供了更广泛的临床采用机器人内窥镜架的有希望的起点。

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.

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

这项研究的重点是在分析二维肺X射线图像中的特定人工智能子场的应用，以辅助医学诊断普通肺炎。卷积神经网络算法是在基于Python编码的基于烧瓶的Web应用程序中实现的，该应用程序可以分析X射线图像以检测普通肺炎。由于卷积神经网络算法依靠机器学习来识别和检测模式，因此实施了一种称为转移学习的技术来训练神经网络，以识别和检测数据集中的模式。开源肺X射线图像被用作训练数据，以创建一个知识库，该知识库是Web应用程序的核心元素，实验设计采用了5次验证性测试来验证Web应用程序。 5次验证性测试的结果显示，每次试验的诊断精度百分比，一般诊断精度百分比和一般诊断错误百分比的计算，而混淆矩阵进一步显示了标签和Web应用程序相应诊断结果之间的关系。每个测试图像。开发的Web应用程序可以由医生可以在A.I.辅助诊断普通肺炎的诊断中以及计算机科学和生物信息学领域的研究人员中使用。

本文提出了一种新的加速马尔可夫链蒙特卡洛（MCMC）方法，以在成像逆问题中有效地执行贝叶斯计算。所提出的方法源自兰格文扩散过程，并源于紧密整合两个最先进的近端Langevin MCMC采样器，SK-ROCK和SPLIST GIBBS采样（SGS），它们采用明显不同的策略来提高收敛速度。更确切地说，我们在Langevin扩散过程的水平上展示了如何集成基于随机的Runge-Kutta-chebyshev扩散的近端SK-ROCK采样器，该采样器具有模型增强和放松策略，可用于扩散以牺牲渐近偏差为代价加快贝叶斯计算的速度。这导致了一种新的，更快的近端SK-ROCK采样器，将原始SK-Rock采样器的加速质量与增强和放松的计算益处相结合。此外，我们建议将增强和放松的模型视为目标模型的近似值，而是将放松定位在偏见 - 差异权衡中，而是建议将增强和放松的模型视为目标模型的概括。然后，这使我们能够仔细校准放松量，以同时提高模型的准确性（通过模型证据衡量）和采样器的收敛速度。为了实现这一目标，我们得出了一种经验性的贝叶斯方法，可以通过最大的边际似然估计自动估计最佳的松弛量。通过与图像脱毛和内化相关的一系列数值实验，以及与艺术状态的替代方法进行比较，证明了所提出的方法。

鉴于诸如相关风险和道德问题等潜在影响，人工智能（AI）等先进技术的规定变得越来越重要。由于能够首先提供这种技术，安全预防措施和社会后果所承诺的巨大福利可以忽略或换档以换取加快发展，因此在开发人员之间发挥赛车叙事。从一个游戏理论模型开始，描述了一个在一个混合的球员世界的理想化技术比赛中，我们调查了种族参与者之间的不同互动结构如何改变集体选择和对监管行为的要求。我们的研究结果表明，当参与者在连接和同伴影响方面描绘了强大的多样性时（例如，当缔约方之间的无垢网络形状相互作用）时，均匀设置中存在的冲突显着降低，从而减少了对监管的需求行动。此外，我们的结果表明，技术治理和监管可能从公司和国家之间的专利异质性和不平等中获利，以便能够对少数参与者进行细致的干预措施，这能够影响整个人口一种道德和可持续利用先进技术。

LIDAR语义分割提供有关环境的3D语义信息，在其决策过程中为智能系统提供基本提示。深度神经网络正在实现这项任务的大型公共基准的最先进结果。不幸的是，找到概括井或适应其他域的模型，其中数据分布不同，仍然是一个重大挑战。这项工作解决了LIDAR语义分段模型的无监督域适应问题。我们的方法将新颖的想法结合在最新的最先进的方法之上，并产生了新的最先进的结果。我们提出了简单但有效的策略，以通过对齐输入空间的数据分布来减少域移位。此外，我们提出了一种基于学习的方法，使目标域的语义类的分布对准到源域。呈现的消融研究表明，每个部分如何促成最终表现。我们的策略显示在三个不同的域上运行的比较以前的域适应方法。

Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C\# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard.

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.