Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art.

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.

估计分配转移的基于软件AI的医疗设备的测试性能对于评估临床部署之前的安全性，效率和可用性至关重要。由于受管制的医疗设备软件的性质以及获取大量标记的医疗数据集的困难，我们考虑了在未标记的目标域上预测任意黑框模型的测试准确性的任务，而无需修改原始培训过程或原始训练过程或原始源数据的任何分布假设（即，我们将模型视为“黑框”，仅使用预测的输出响应）。我们在几种临床上相关的分配转移类型（机构，硬件扫描仪，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，Atlas，乳房X线摄影，皮肤病学和组织病理学）下，提出了一种基于共形预测的“黑盒”测试估计技术，并根据三个医学成像数据集（乳房X线摄影，皮肤病学和组织病理学）对其他方法进行评估。医院）。我们希望通过促进黑盒模型的实用有效估计技术，医疗设备的制造商将制定更标准化和现实的评估程序，以提高临床AI工具的鲁棒性和可信度。

人们认为，深度学习模型以不可预测的和可能的灾难性方式失败，因此受到了监管AI的监管批准和广泛的临床部署。缺乏统计上严格的不确定性量化是破坏对AI结果的信任的重要因素。无分配不确定性量化的最新发展通过为任意数据分布的黑框模型提供可靠性保证，作为正式有效的有限样本预测间隔，为这些问题提供了实用解决方案。我们的工作将这些新的不确定性定量方法（特别是共形预测）应用于腰椎MRI中脊柱狭窄严重程度的深度学习模型。我们展示了一种用于形成顺序预测集的技术，该技术可以保证在用户定义的概率（置信区间）内包含正确的狭窄严重程度。在通过深度学习模型处理的409个MRI考试的数据集中，共形方法提供了较小的预测集尺寸的紧密覆盖范围。此外，我们通过量化明显成像异常的患病率的提高（例如，运动伪像，金属伪像和肿瘤）可以在预测性能中降低预测性能，从而探索具有高不确定性预测（大预测集）标记病例的潜在临床适用性。与随机病例样本相比。

乳腺癌是最常见的癌症，乳房X线摄影筛查的早期检测对于改善患者预后至关重要。评估乳房X线乳房密度在临床上很重要，因为浓密的乳房具有更高的风险，并且更有可能阻塞肿瘤。专家的手动评估既耗时又受评估者间的可变性。因此，对乳房X线乳房密度评估的深度学习方法的发展有所增加。尽管深度学习在乳房X线摄影的应用中表现出了令人印象深刻的表现，但在仍然相对较少的深度学习系统中的临床部署中；从历史上看，乳房X线摄影计算机辅助诊断（CAD）已过分宣传，无法提供。这部分是由于无法直观地量化临床医生算法的不确定性，这将大大提高可用性。共形预测非常适合增加对深度学习工具的可靠和信任，但它们缺乏对医疗数据集的现实评估。在本文中，我们介绍了应用于医学成像任务的三个可能应用的详细分析：分配转移表征，预测质量的改善和亚组公平分析。我们的结果表明，无分配不确定性量化技术的潜力可以增强对AI算法的信任并加快其翻译为使用。

积极的数据增强是视觉变压器（VIT）的强大泛化能力的关键组成部分。一种这样的数据增强技术是对抗性培训;然而，许多先前的作品表明，这通常会导致清洁的准确性差。在这项工作中，我们展示了金字塔对抗训练，这是一种简单有效的技术来提高韦维尔的整体性能。我们将其与“匹配”辍学和随机深度正则化配对，这采用了干净和对抗样品的相同辍学和随机深度配置。类似于Advprop的CNNS的改进（不直接适用于VIT），我们的金字塔对抗性训练会破坏分销准确性和vit和相关架构的分配鲁棒性之间的权衡。当Imagenet-1K数据训练时，它导致ImageNet清洁准确性的182美元的vit-B模型的精确度，同时由7美元的稳健性指标同时提高性能，从$ 1.76 \％$至11.45 \％$。我们为Imagenet-C（41.4 MCE），Imagenet-R（$ 53.92 \％$），以及Imagenet-Sketch（41.04美元\％$）的新的最先进，只使用vit-b / 16骨干和我们的金字塔对抗训练。我们的代码将在接受时公开提供。

尽管对临床机器学习研究有强烈的关注和相当大的投资，但在现实世界的临床环境中，在大规模的应用中已经部署了相对较少的应用。虽然研究在推进最先进的情况下很重要，但翻译同样重要的是，使这些技术和技术能够最终影响医疗保健。我们认为对几个考虑缺乏升值是在期望和现实之间这种差异的主要原因。为了更好地描述研究人员和从业者之间的整体视角，我们调查了几个从业人员在开发CML中进行临床部署的商业经验。使用这些洞察力，我们确定了几个主要类别的挑战，以便更好地设计和开发临床机学习应用。

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

Regularising the parameter matrices of neural networks is ubiquitous in training deep models. Typical regularisation approaches suggest initialising weights using small random values, and to penalise weights to promote sparsity. However, these widely used techniques may be less effective in certain scenarios. Here, we study the Koopman autoencoder model which includes an encoder, a Koopman operator layer, and a decoder. These models have been designed and dedicated to tackle physics-related problems with interpretable dynamics and an ability to incorporate physics-related constraints. However, the majority of existing work employs standard regularisation practices. In our work, we take a step toward augmenting Koopman autoencoders with initialisation and penalty schemes tailored for physics-related settings. Specifically, we propose the "eigeninit" initialisation scheme that samples initial Koopman operators from specific eigenvalue distributions. In addition, we suggest the "eigenloss" penalty scheme that penalises the eigenvalues of the Koopman operator during training. We demonstrate the utility of these schemes on two synthetic data sets: a driven pendulum and flow past a cylinder; and two real-world problems: ocean surface temperatures and cyclone wind fields. We find on these datasets that eigenloss and eigeninit improves the convergence rate by up to a factor of 5, and that they reduce the cumulative long-term prediction error by up to a factor of 3. Such a finding points to the utility of incorporating similar schemes as an inductive bias in other physics-related deep learning approaches.

While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in other languages or do not reach the same performance level. This undesired effect of AI advancements increases the gap between access to new technology from different populations across the world. This unsought bias mainly discriminates against individuals whose English skills are less developed, e.g., non-English speakers children. Following significant advancements in AI research in recent years, OpenAI has recently presented DALL-E: a powerful tool for creating images based on English text prompts. While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations. An additional limitation of the current DALL-E model is that it only allows for the creation of a few images in response to a given input prompt, rather than a series of consecutive coherent frames that tell a story or describe a process that changes over time. Here, we present an easy-to-use automatic DALL-E storytelling framework that leverages the existing DALL-E model to enable fast and coherent visualizations of non-English songs and stories, pushing the limit of the one-step-at-a-time option DALL-E currently offers. We show that our framework is able to effectively visualize stories from non-English texts and portray the changes in the plot over time. It is also able to create a narrative and maintain interpretable changes in the description across frames. Additionally, our framework offers users the ability to specify constraints on the story elements, such as a specific location or context, and to maintain a consistent style throughout the visualization.