基于梯度的高参数调整的优化方法可确保理论收敛到固定解决方案时，对于固定的上层变量值，双光线程序的下层级别强烈凸（LLSC）和平滑（LLS）。对于在许多机器学习算法中调整超参数引起的双重程序，不满足这种情况。在这项工作中，我们开发了一种基于不精确度（VF-IDCA）的基于依次收敛函数函数算法。我们表明，该算法从一系列的超级参数调整应用程序中实现了无LLSC和LLS假设的固定解决方案。我们的广泛实验证实了我们的理论发现，并表明，当应用于调子超参数时，提出的VF-IDCA会产生较高的性能。

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.

Facial action units (FAUs) are critical for fine-grained facial expression analysis. Although FAU detection has been actively studied using ideally high quality images, it was not thoroughly studied under heavily occluded conditions. In this paper, we propose the first occlusion-robust FAU recognition method to maintain FAU detection performance under heavy occlusions. Our novel approach takes advantage of rich information from the latent space of masked autoencoder (MAE) and transforms it into FAU features. Bypassing the occlusion reconstruction step, our model efficiently extracts FAU features of occluded faces by mining the latent space of a pretrained masked autoencoder. Both node and edge-level knowledge distillation are also employed to guide our model to find a mapping between latent space vectors and FAU features. Facial occlusion conditions, including random small patches and large blocks, are thoroughly studied. Experimental results on BP4D and DISFA datasets show that our method can achieve state-of-the-art performances under the studied facial occlusion, significantly outperforming existing baseline methods. In particular, even under heavy occlusion, the proposed method can achieve comparable performance as state-of-the-art methods under normal conditions.

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.

金属有机框架（MOF）是一类模块化的多孔晶体材料，具有巨大的革命性应用，例如储气，分子分离，化学感应，催化和药物输送。剑桥结构数据库（CSD）报告了10,636个合成的MOF晶体，此外还包含CA。114,373个类似MOF的结构。综合数量（加上可能合成的）MOF结构数量庞大，需要研究人员追求计算技术来筛选和分离MOF候选物。在此演示论文中，我们描述了我们在利用知识图方法方面促进MOF预测，发现和综合方面的努力。我们提出了有关（1）从结构化和非结构化来源构建MOF知识图（MOF-KG）的挑战和案例研究，以及（2）利用MOF-KG来发现新知识或缺失知识。

尽管电子健康记录是生物医学研究的丰富数据来源，但这些系统并未在医疗环境中统一地实施，并且由于医疗保健碎片化和孤立的电子健康记录之间缺乏互操作性，可能缺少大量数据。考虑到缺少数据的案例的删除可能会在随后的分析中引起严重的偏见，因此，一些作者更喜欢采用多重插补策略来恢复缺失的信息。不幸的是，尽管几项文献作品已经通过使用现在可以自由研究的任何不同的多个归档算法记录了有希望的结果，但尚无共识，MI算法效果最好。除了选择MI策略之外，归纳算法及其应用程序设置的选择也至关重要且具有挑战性。在本文中，受鲁宾和范布伦的开创性作品的启发，我们提出了一个方法学框架，可以应用于评估和比较多种多个插补技术，旨在选择用于计算临床研究工作中最有效的推断。我们的框架已被应用于验证和扩展较大的队列，这是我们在先前的文献研究中提出的结果，我们在其中评估了关键患者的描述符和Covid-19的影响在2型糖尿病患者中的影响，其数据为2型糖尿病，其数据为2型糖尿病由国家共同队列合作飞地提供。

脑电图（EEG）解码旨在识别基于非侵入性测量的脑活动的神经处理的感知，语义和认知含量。当应用于在静态，受控的实验室环境中获取的数据时，传统的EEG解码方法取得了适度的成功。然而，开放世界的环境是一个更现实的环境，在影响EEG录音的情况下，可以意外地出现，显着削弱了现有方法的鲁棒性。近年来，由于其在特征提取的卓越容量，深入学习（DL）被出现为潜在的解决方案。它克服了使用浅架构提取的“手工制作”功能或功能的限制，但通常需要大量的昂贵，专业标记的数据 - 并不总是可获得的。结合具有域特定知识的DL可能允许开发即使具有小样本数据，也可以开发用于解码大脑活动的鲁棒方法。虽然已经提出了各种DL方法来解决EEG解码中的一些挑战，但目前缺乏系统的教程概述，特别是对于开放世界应用程序。因此，本文为开放世界EEG解码提供了对DL方法的全面调查，并确定了有前途的研究方向，以激发现实世界应用中的脑电图解码的未来研究。

用于核细胞分割的注释显微镜图像是费力且耗时的。为了利用少数现有的注释，也跨越多种方式，我们提出了一种基于生成的对抗网络（GAN）的新型显微镜式增强技术。与其他风格转移方法不同，它不仅可以处理不同的细胞测定类型和照明条件，还可以与不同的成像方式，例如亮场和荧光显微镜。使用Disentangled表示的内容和风格，我们可以在增强期间改变其风格的同时保留原始图像的结构。我们在2018年数据科学碗数据集上评估我们的数据增强，包括各种细胞测定，照明条件和成像方式。凭借我们的增强，竞争中两个排名排名蒙版R-CNN的核细胞分割算法的分割精度显着增加。因此，我们的增强技术使下游任务更加强大地对测试数据异质性，并有助于抵消类别不平衡而不重新采样少数类。

Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher. However, applying KD in image regression with a scalar response variable has been rarely studied, and there exists no KD method applicable to both classification and regression tasks yet. Moreover, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on cGANs, termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 show that we can combine state of the art KD methods with the cGAN-KD framework to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable.

最近，已经积极研究了从无条件生成的对抗网络（GANS）产生的来自无条件生成的对抗网络（GANS）来改善整体图像质量的子采样或精炼图像。不幸的是，这些方法通常观察到处理条件GAN（CGANS） - 在类（AKA类条件GANS）或连续变量（AKA连续CGANs或CCGANs）上调节条件的效率较低或效率低。在这项工作中，我们引入了一个有效且有效的回顾性方案，命名为条件密度比引导抑制采样（CDR-RS），以从CGANS采样高质量的图像。具体地，我们首先制定一种新的条件密度比估计方法，称为CDRE-F-CSP，通过提出条件的SOFTPLUS（CSP）损耗和改进的特征提取机制。然后，我们导出了在CSP丢失训练的密度比模型的误差。最后，我们在其估计的条件密度比方面接受或拒绝假图像。还开发了一种过滤方案以增加假图像的标签一致性，而不会在从CCGANs采样时失去多样性。我们在五个基准数据集中广泛地测试CDR-RS的有效性和效率在各种条件的GANS和CCGANS中取样。当从类条件的GAN进行采样时，CDR-RS在有效性方面，CDR-RS通过大型余量（除DRE-F-SP + RS除外）优于现代最先进的方法。尽管CDR-RS的有效性通常与DRE-F-SP + RS的有效性相当，但CDR-RS基本上更有效。当从CCGANS取样时，在有效性和效率方面，CDR-RS的优越性甚至更加明显。值得注意的是，随着合理的计算资源的消耗，CDR-RS可以大大减少标签分数而不降低CCGAN生成的图像的多样性，而其他方法通常需要交易大量的多样性以略微改善标签分数。