农业面临着劳动危机，导致人们对小型，伪造机器人（AGBOTS）的兴趣增加，这些机器人可以执行精确的，有针对性的行动（例如，农作物侦察，除草，受精），同时由人类操作员进行监督。但是，农民不一定是机器人技术方面的专家，也不会采用增加其工作量的技术或不提供立即回报的技术。在这项工作中，我们探讨了远程人类操作员与多个Agbot之间进行通信的方法，并研究音频通信对操作员的偏好和生产率的影响。我们开发了一个模拟平台，在该平台中，AGBOT在一个字段中部署，随机遇到故障，并呼吁操作员寻求帮助。随着AGBOTS报告错误，测试了各种音频通信机制，以传达哪种机器人失败以及发生了什么类型的故障。人类的任务是在完成次要任务时口头诊断失败。进行了一项用户研究，以测试三种音频通信方法：耳塞，单短语命令和完整的句子通信。每个参与者都完成了一项调查，以确定他们的偏好和每种方法的总体效率。我们的结果表明，使用单个短语的系统是参与者最积极的看法，可以使人更有效地完成次要任务。该代码可在以下网址获得：https：//github.com/akamboj2/agbot-sim。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone. We investigate whether these methods can directly address the problem of sequential decision-making. We view decision-making not through the lens of reinforcement learning (RL), but rather through conditional generative modeling. To our surprise, we find that our formulation leads to policies that can outperform existing offline RL approaches across standard benchmarks. By modeling a policy as a return-conditional diffusion model, we illustrate how we may circumvent the need for dynamic programming and subsequently eliminate many of the complexities that come with traditional offline RL. We further demonstrate the advantages of modeling policies as conditional diffusion models by considering two other conditioning variables: constraints and skills. Conditioning on a single constraint or skill during training leads to behaviors at test-time that can satisfy several constraints together or demonstrate a composition of skills. Our results illustrate that conditional generative modeling is a powerful tool for decision-making.

这项工作扩展了遗传指纹欺骗的先前进步，并引入了多样性和新颖的大师。该系统使用质量多样性进化算法来生成人造印刷的字典，重点是增加数据集对用户的覆盖范围。多样性大师图的重点是生成与以前发现的印刷品未涵盖的用户匹配的解决方案印刷品，而新颖的主版印刷明确地搜索了与以前的印刷品相比，在用户空间中更多的印刷品。我们的多印刷搜索方法在覆盖范围和概括方面都优于奇异的深层印刷，同时保持指纹图像输出的质量。

多项研究表明，从孕妇中期超声检查（USG）检查获得标准化的胎儿脑生物特征？获得这些测量值是高度主观的，专业驱动的，需要多年的培训经验，从而限制了所有怀孕母亲的优质产前护理。在这项研究中，我们提出了一种深度学习方法（DL）方法，以通过准确和自动化的卡钳放置（每次生物测量法）将其作为地标建模，从而从跨炉平面（TC）的2D USG图像（TC）计算3个关键的胎儿脑生物特征。检测问题。我们利用了临床相关的生物识别约束（卡尺点之间的关系）和与域相关的数据增强，以提高U-NET DL模型的准确性（经过训练/测试：596张图像，473个受试者/143张图像，143个受试者）。我们进行了多个实验，证明了DL主链，数据增强，推广性和基准测试，通过广泛的临床验证（DL与7位经验丰富的临床医生）对最新的最新方法进行了测试。在所有情况下，单个卡尺点和计算生物特征的放置的平均误差都与临床医生之间的错误率相当。所提出的框架的临床翻译可以帮助新手用户在可靠和标准化的胎儿大脑超声图评估中的新手使用者。