无人驾驶汽车（UAV）在许多领域都受雇于摄影，紧急，娱乐，国防，农业，林业，采矿和建筑。在过去的十年中，无人机技术在许多施工项目阶段中找到了应用程序，从现场映射，进度监控，建筑物检查，损坏评估和材料交付等等。尽管已经对无人机在各种施工相关的过程中的优势进行了广泛的研究，但关于提高任务能力和效率的无人机协作的研究仍然很少。本文提出了一种基于塔格狩猎游戏和粒子群优化（PSO）的多个无人机的新合作路径计划算法。首先，定义了每个无人机的成本函数，并包含多个目标和约束。然后，开发了无人机游戏框架，以将多功能路径计划制定到寻找回报优势均衡的问题。接下来，提出了基于PSO的算法来获得无人机的最佳路径。由三个无人机检查的大型建筑工地的仿真结果表明，在检查任务期间，提出的算法在为无人机形成的可行和高效飞行路径生成可行，高效的飞行路径上的有效性。

最近的人工智能（AI）算法已在各种医学分类任务上实现了放射科医生级的性能。但是，只有少数研究涉及CXR扫描异常发现的定位，这对于向放射学家解释图像级分类至关重要。我们在本文中介绍了一个名为Vindr-CXR的可解释的深度学习系统，该系统可以将CXR扫描分类为多种胸部疾病，同时将大多数类型的关键发现本地化在图像上。 Vindr-CXR接受了51,485次CXR扫描的培训，并通过放射科医生提供的边界盒注释进行了培训。它表现出与经验丰富的放射科医生相当的表现，可以在3,000张CXR扫描的回顾性验证集上对6种常见的胸部疾病进行分类，而在接收器操作特征曲线（AUROC）下的平均面积为0.967（95％置信区间[CI]：0.958---------0.958------- 0.975）。 VINDR-CXR在独立患者队列中也得到了外部验证，并显示出其稳健性。对于具有14种类型病变的本地化任务，我们的自由响应接收器操作特征（FROC）分析表明，VINDR-CXR以每扫描确定的1.0假阳性病变的速率达到80.2％的敏感性。还进行了一项前瞻性研究，以衡量VINDR-CXR在协助六名经验丰富的放射科医生方面的临床影响。结果表明，当用作诊断工具时，提出的系统显着改善了放射科医生本身之间的一致性，平均Fleiss的Kappa的同意增加了1.5％。我们还观察到，在放射科医生咨询了Vindr-CXR的建议之后，在平均Cohen的Kappa中，它们和系统之间的一致性显着增加了3.3％。

本文研究了通过机器学习模型估计特征对特定实例预测的贡献的问题，以及功能对模型的总体贡献。特征（变量）对预测结果的因果效应反映了该特征对预测的贡献。一个挑战是，如果没有已知的因果图，就无法从数据中估算大多数现有的因果效应。在本文中，我们根据假设的理想实验定义了解释性因果效应。该定义给不可知论的解释带来了一些好处。首先，解释是透明的，具有因果关系。其次，解释性因果效应估计可以数据驱动。第三，因果效应既提供了特定预测的局部解释，又提供了一个全局解释，显示了一个特征在预测模型中的总体重要性。我们进一步提出了一种基于解释性因果效应来解释的方法和组合变量的方法。我们显示了对某些现实世界数据集的实验的定义和方法。

在本文中，我们使用混合整数编程（MIP）探索基于模型的培训鲁棒和可解释的二金属化回归模型的培训鲁棒和可解释的二值化回归模型。我们的MIP模型通过使用加权目标来余额来实现预测边距和模型大小的优化，即：最大限度地减少错误分类的培训实例的总余量，最大限度地提高了正确分类的培训实例的总余量，并最大限度地提高了整体模型正则化。我们进行两组实验，以便在多个分类数据集的标准和损坏版本上测试MIP模型的分类准确性。在第一组实验中，我们表明我们的MIP模型优于等效的伪布尔优化（PBO）模型，并在标准数据集中的分类精度方面实现了对逻辑回归（LR）和梯度下降（GD）的竞争结果。在第二组实验中，我们表明我们的MIP模型在分类准确性方面优于大多数损坏的数据集的分类准确性。最后，我们在目视展示了MIP模型在其在MNIST DataSet上的学习参数方面的可解释性。总体而言，我们展示了使用MIP培训培训稳健和可解释的二值化回归模型的有效性。

在过去的几十年里，互联网用户在网上举办了实时事件并与现场，互动受众分享经历的日益增长的需求。像抽搐一样的在线流媒体服务吸引了数百万用户来流并窥视。关于抽搐对流动性普及的预测有很少的研究。在本文中，我们看起来可能有助于娱乐的潜在因素。在4周时段期间，通过使用Twitch的API一致的跟踪收集娱乐数据。收集每个用户的流信息，例如当前观看者和追随者的数量，流类型等。从结果中，我们发现流媒体会话的频率，内容的类型和流的长度是确定在会话期间可以获得多少观众和订户的垃圾媒体。

Configurable software systems are employed in many important application domains. Understanding the performance of the systems under all configurations is critical to prevent potential performance issues caused by misconfiguration. However, as the number of configurations can be prohibitively large, it is not possible to measure the system performance under all configurations. Thus, a common approach is to build a prediction model from a limited measurement data to predict the performance of all configurations as scalar values. However, it has been pointed out that there are different sources of uncertainty coming from the data collection or the modeling process, which can make the scalar predictions not certainly accurate. To address this problem, we propose a Bayesian deep learning based method, namely BDLPerf, that can incorporate uncertainty into the prediction model. BDLPerf can provide both scalar predictions for configurations' performance and the corresponding confidence intervals of these scalar predictions. We also develop a novel uncertainty calibration technique to ensure the reliability of the confidence intervals generated by a Bayesian prediction model. Finally, we suggest an efficient hyperparameter tuning technique so as to train the prediction model within a reasonable amount of time whilst achieving high accuracy. Our experimental results on 10 real-world systems show that BDLPerf achieves higher accuracy than existing approaches, in both scalar performance prediction and confidence interval estimation.

Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars. In addition to a lack of labeled data, these applications also suffer from distributional shifts. Therefore, an SSL method should provide robust generalization and uncertainty estimation in the test dataset to be considered a reliable model in such high-stakes domains. However, existing approaches often focus on generalization, without evaluating the model's uncertainty. The ability to compare SSL techniques for improving these estimates is therefore critical for research on the reliability of self-supervision models. In this paper, we explore variants of SSL methods, including Jigsaw Puzzles, Context, Rotation, Geometric Transformations Prediction for vision, as well as BERT and GPT for language tasks. We train SSL in auxiliary learning for vision and pre-training for language model, then evaluate the generalization (in-out classification accuracy) and uncertainty (expected calibration error) across different distribution covariate shift datasets, including MNIST-C, CIFAR-10-C, CIFAR-10.1, and MNLI. Our goal is to create a benchmark with outputs from experiments, providing a starting point for new SSL methods in Reliable Machine Learning. All source code to reproduce results is available at https://github.com/hamanhbui/reliable_ssl_baselines.

Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation. However, such complex behaviors are difficult to learn because they involve both high-level planning and low-level motor control. We present a novel framework, Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively leveraging a library of previously learned control policies. Our framework learns multiplicative policy composition, task-specific residual actions, and synthetic goal information simultaneously while freezing the prerequisite policies. We further explicitly control the style of the motion by regularizing residual actions. We show that our framework learns joint-level control policies for a diverse set of motor skills ranging from basic locomotion to complex interactive navigation, including navigating around obstacles, pushing objects, crawling under a table, pushing a door open with its leg, and holding it open while walking through it. The proposed CCRL framework leads to policies with consistent styles and lower joint torques, which we successfully transfer to a real Unitree A1 robot without any additional fine-tuning.

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.

Monitoring machine learning systems post deployment is critical to ensure the reliability of the systems. Particularly importance is the problem of monitoring the performance of machine learning systems across all the data subgroups (subpopulations). In practice, this process could be prohibitively expensive as the number of data subgroups grows exponentially with the number of input features, and the process of labelling data to evaluate each subgroup's performance is costly. In this paper, we propose an efficient framework for monitoring subgroup performance of machine learning systems. Specifically, we aim to find the data subgroup with the worst performance using a limited number of labeled data. We mathematically formulate this problem as an optimization problem with an expensive black-box objective function, and then suggest to use Bayesian optimization to solve this problem. Our experimental results on various real-world datasets and machine learning systems show that our proposed framework can retrieve the worst-performing data subgroup effectively and efficiently.