在这项工作中，我们研究了对象检测模型的自我监督预审计的不同方法。我们首先设计一个通用框架，通过随机采样和投射框来学习从图像中学习空间一致的密集表示，并将其投影到每个增强视图，并最大程度地提高相应的盒子功能之间的相似性。我们研究文献中的现有设计选择，例如盒子生成，功能提取策略，并使用其在实例级图像表示学习技术上获得成功启发的多种视图。我们的结果表明，该方法对超参数的不同选择是可靠的，并且使用多个视图不如实例级图像表示学习所显示的那样有效。我们还设计了两个辅助任务，以通过（1）通过使用对比度损失从采样设置中预测盒子中的一个视图中的框来预测框，并且（2）使用变压器预测盒子坐标，这可能会受益。下游对象检测任务。我们发现，在标记数据上预审计的模型时，这些任务不会导致更好的对象检测性能。

自我监督的代表学习使对比学习的进步推动了显着的跨利赛，这旨在学习嵌入附近积极投入对的转变，同时推动负对的对。虽然可以可靠地生成正对（例如，作为相同图像的不同视图），但是难以准确地建立负对对，定义为来自不同图像的样本，而不管它们的语义内容或视觉功能如何。对比学习中的一个基本问题正在减轻假底片的影响。对比假否定引起了两个代表学习的关键问题：丢弃语义信息和缓慢的收敛。在本文中，我们提出了识别错误否定的新方法，以及减轻其效果的两种策略，即虚假的消极消除和吸引力，同时系统地执行严格的评估，详细阐述了这个问题。我们的方法表现出对基于对比学习的方法的一致性改进。没有标签，我们在想象中的1000个语义课程中识别出具有40％的精度，并且在使用1％标签的FINETUNING时，在先前最先进的最先进的前1个精度的绝对提高5.8％的绝对提高。我们的代码可在https://github.com/gogle-research/fnc上获得。

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is challenging and cannot be guaranteed in a finite time. We propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.

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.

Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.

Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions. We present the safety assurance case in detail for Artificial Pancreas Systems (APS) as a representative application of learning-enabled MCPS, and provide a detailed analysis by implementing a deep neural network for the prediction in APS. We check the sufficiency of the ML data and analyze the correctness of the ML-based prediction using formal verification. Finally, we outline open research problems based on our experience in this paper.

Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, assessing credit risk, etc. This calls for handling massive incoming information with minimum response delay while ensuring fair and high quality decisions. Recent discrimination-aware learning methods are optimized based on overall accuracy. However, the overall accuracy is biased in favor of the majority class; therefore, state-of-the-art methods mainly diminish discrimination by partially or completely ignoring the minority class. In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes. Our proposed algorithm is simple, fast, and attains multi-objective optimization goals. To handle class imbalance and concept drifts, a dynamic instance weighting module is proposed, which gives more importance to recent instances and less importance to obsolete instances based on their membership in minority or majority class. We conducted experiments on a range of streaming and static datasets and deduced that our proposed methodology outperforms existing state-of-the-art fairness-aware methods in terms of both discrimination score and balanced accuracy.

神经科学方面的巨大努力正在努力绘制许多新物种的连接群，包括果蝇果蝇的接近完成。重要的是要问这些模型是否可以使人工智能受益。在这项工作中，我们提出了两个基本问题：（1）生物连接组可以在机器学习中提供的何处以及何时提供使用，（2）哪些设计原理对于提取连接组的良好表示是必要的。为此，我们将秀丽隐杆线虫线虫的运动电路转化为以不同水平的生物物理现实主义水平的人工神经网络，并评估了这些网络在运动和非运动行为任务上训练这些网络的结果。我们证明，生物物理现实主义不必维持使用生物回路的优势。我们还确定，即使没有保留确切的接线图，建筑统计数据也提供了有价值的先验。最后，我们表明，虽然秀丽隐杆线虫运动电路对运动问题提供了强大的感应偏见，但其结构可能会阻碍与运动无关的任务（例如视觉分类问题）。

在本文中，我们提出了一种算法，以在动态场景的两对图像之间插值。尽管在过去的几年中，在框架插值方面取得了重大进展，但当前的方法无法处理具有亮度和照明变化的图像，即使很快将图像捕获也很常见。我们建议通过利用现有的光流方法来解决这个问题，这些方法对照明的变化非常健壮。具体而言，使用使用现有预训练的流动网络估算的双向流，我们预测了从中间帧到两个输入图像的流。为此，我们建议将双向流编码为由超网络提供动力的基于坐标的网络，以获得跨时间的连续表示流。一旦获得了估计的流，我们就会在现有的混合网络中使用它们来获得最终的中间帧。通过广泛的实验，我们证明我们的方法能够比最新的框架插值算法产生明显更好的结果。

尽管在时间序列重建的深度学习方法中取得了长足的进步，但由于其对优化损失的贡献可忽略不计，因此没有设计现有的方法来揭示具有微小信号强度的本地活动。但是，这种局部活动可以表示生理系统中重要的异常事件，例如额外的焦点触发心脏电波异常的传播。我们讨论了一种重建这种本地活动的新技术，尽管信号强度很小，但它是随后具有较大信号强度的全球活动的原因。我们的中心创新是通过明确建模并解开系统潜在的潜在隐藏内部干预措施的影响来解决此问题。在状态空间模型（SSM）的新型神经公式中，我们首先通过分别描述的相互作用的神经ODES系统引入潜在动力学的因果效应建模1）内部干预的连续时间动力学； 2）它对系统本地状态轨迹的影响。因为不能直接观察干预措施，而必须与观察到的后续效果脱离，所以我们整合了对系统的无天然干预动态的知识，并通过假设它是对实际观察到的差异来推断隐藏干预措施的推断和假设的无干预动态。我们证明了对重建异位焦点的提出框架的概念证明，从而破坏了从远程观察到正常心脏电气传播的过程。