近年来，无人驾驶航空公司（无人机）的扩散急剧增加。无人机可以以可靠且具有成本效益的方式完成复杂或危险的任务，但仍然受到功耗问题的限制，这对飞行持续时间和能源苛刻任务的完成构成了严重的限制。以能源有效的方式提供具有高级决策功能的无人机的可能性是非常有益的。在本文中，我们提出了一个实际的解决方案，对这个问题进行了深入学习的问题。开发系统将OpenMV微控制器集成到DJI Tello Micro Acial车辆（MAV）中。微控制器托管一组机器学习的推理工具，协作控制无人机的导航并完成给定的任务目标。这种方法的目标是利用TINYML的新机遇特征通过OpenMV，包括离线推断，低延迟，能效和数据安全性。该方法在实际应用程序上成功验证，该应用程序包括在拥挤环境中穿着保护面具的人们的船上检测。

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

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.

One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algorithm, a Deep Reinforcement Learning algorithm that leverages a control tutor, i.e., an exogenous control law, to reduce learning time. The tutor can be designed using an approximate model of the system, without any assumption about the knowledge of the system's dynamics. There is no expectation that it will be able to achieve the control objective if used stand-alone. During learning, the tutor occasionally suggests an action, thus partially guiding exploration. We validate our approach on three scenarios from OpenAI Gym: the inverted pendulum, lunar lander, and car racing. We demonstrate that CT-DQN is able to achieve better or equivalent data efficiency with respect to the classic function approximation solutions.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

本文提出了一种以非零速度的效果友好型捕捉对象的混合优化和学习方法。通过受约束的二次编程问题，该方法生成最佳轨迹，直至机器人和对象之间的接触点，以最小化其相对速度并减少初始影响力。接下来，生成的轨迹是由基于人类的捕捉演示的旋风动作原始词更新的，以确保围绕接口点的平稳过渡。此外，学习的人类可变刚度（HVS）被发送到机器人的笛卡尔阻抗控制器，以吸收后影响力并稳定捕获位置。进行了三个实验，以将我们的方法与固定位置阻抗控制器（FP-IC）进行比较。结果表明，所提出的方法的表现优于FP-IC，同时添加HVS可以更好地吸收影响后力。

本文介绍了一种新型的因果结构，即多尺度非平稳的定向无环图（MN-DAG），该图将DAG概括为时频域。我们的贡献是双重的。首先，通过利用光谱和因果关系的结果，我们揭露了一种新型的概率生成模型，该模型允许根据用户指定的先验对因果图的时间依赖性和多尺度属性进行采样。其次，我们通过随机变异推理（SVI）（称为多阶层非稳态的因果结构学习者（MN-Castle））设计了一种用于估计Mn-DAGS的贝叶斯方法。除了直接观察外，MN-Castle还通过不同时间分辨率的时间序列的总功率谱分解来利用信息。在我们的实验中，我们首先使用所提出的模型根据潜在的MN-DAG生成合成数据，这表明数据生成的数据再现了不同域中时间序列的众所周知的特征。然后，我们将学习方法的MN媒体与基线模型进行比较，该模型在使用不同的多尺度和非平稳设置生成的合成数据上进行了比较，从而证实了MN-Castle的良好性能。最后，我们展示了一些从MN-Castle的应用中得出的一些见解，以研究COVID-19期间7个全球股票市场的因果结构。

在随机子集总和问题中，给定$ n $ i.i.d.随机变量$ x_1，...，x_n $，我们希望将[-1,1] $ in [-1,1] $的任何点$ z \作为合适子集的总和$ x_ {i_1（z）}，...，x_ {i_s（z）} $的$，最多$ \ varepsilon $。尽管有简单的陈述，但这个问题还是理论计算机科学和统计力学的基本兴趣。最近，它因其在人工神经网络理论中的影响而引起了人们的重新关注。该问题的一个明显的多维概括是考虑$ n $ i.i.d. \ $ d $ - 二维随机向量，目的是近似于[-1,1]^d $的每个点$ \ Mathbf {z} \。令人惊讶的是，在Lueker的1998年证明，在一维设置中，$ n = o（\ log \ frac 1 \ varepsilon）$ samples $ samples $ samples具有很高可能性的近似属性，在实现上述概括方面几乎没有进展。在这项工作中，我们证明，在$ d $ dimensions中，$ n = o（d^3 \ log \ frac 1 \ varepsilon \ cdot（\ log \ frac 1 \ frac 1 \ varepsilon + log d d））$ samples $ sample近似属性具有很高的概率。作为强调该结果潜在兴趣的应用程序，我们证明了最近提出的神经网络模型表现出\ emph {通用}：具有很高的概率，该模型可以在参数数量中近似多项式开销中的任何神经网络。

认识人类所采取的行动以及对他们的意图的预期是重要的推动力，可以在人类机器人团队中产生社交和成功的合作。同时，机器人应具有由协作任务或人类引起的多种目标和约束的能力。在这方面，我们提出了视力技术来执行人类的行动识别和图像分类，这些技术被整合到增强的层次二次编程（AHQP）方案中，以层次优化机器人的反应性行为和人类的人体工程学。所提出的框架允许执行任务时，可以直观地在空间中命令机器人。该实验证实了人体工程学和可用性的增加，这是减少肌肉骨骼疾病并增加自动化信任的基本参数。

航天器微型振动的隔离对于成功依靠高精度指向的工具部署至关重要。 Hexapod平台代表了一个有前途的解决方案，但是与在可接受的质量和复杂性预算中获得理想的3D动态相关的困难导致了最小的实际采用。本文介绍了支柱边界条件（BCS）对系统级机械干扰抑制的影响。传统的全旋转关节构型的固有局限性被突出显示，并显示为链接质量和旋转惯性。提出并在分析上提出了针刺的BC替代方案，以减轻2D和3D的缓解。新BC的优势在任意平行操纵器中具有，并通过数值测试证明了几种六角形的几何形状。提出了具有良好性能的配置。最后，描述并验证了允许物理实现的新型平面关节。因此，这项工作可以开发不需要主动控制的微型启动平台。