We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

Autonomous robotic surgery has advanced significantly based on analysis of visual and temporal cues in surgical workflow, but relational cues from domain knowledge remain under investigation. Complex relations in surgical annotations can be divided into intra- and inter-relations, both valuable to autonomous systems to comprehend surgical workflows. Intra- and inter-relations describe the relevance of various categories within a particular annotation type and the relevance of different annotation types, respectively. This paper aims to systematically investigate the importance of relational cues in surgery. First, we contribute the RLLS12M dataset, a large-scale collection of robotic left lateral sectionectomy (RLLS), by curating 50 videos of 50 patients operated by 5 surgeons and annotating a hierarchical workflow, which consists of 3 inter- and 6 intra-relations, 6 steps, 15 tasks, and 38 activities represented as the triplet of 11 instruments, 8 actions, and 16 objects, totaling 2,113,510 video frames and 12,681,060 annotation entities. Correspondingly, we propose a multi-relation purification hybrid network (MURPHY), which aptly incorporates novel relation modules to augment the feature representation by purifying relational features using the intra- and inter-relations embodied in annotations. The intra-relation module leverages a R-GCN to implant visual features in different graph relations, which are aggregated using a targeted relation purification with affinity information measuring label consistency and feature similarity. The inter-relation module is motivated by attention mechanisms to regularize the influence of relational features based on the hierarchy of annotation types from the domain knowledge. Extensive experimental results on the curated RLLS dataset confirm the effectiveness of our approach, demonstrating that relations matter in surgical workflow analysis.

In this paper, we propose DimonGen, which aims to generate diverse sentences describing concept relationships in various everyday scenarios. To support this, we create a benchmark dataset for this task by adapting the existing CommonGen dataset and propose a two-stage model called MoREE (Mixture of Retrieval-Enhanced Experts) to generate the target sentences. MoREE consists of a mixture of retriever models that retrieve diverse context sentences related to the given concepts, and a mixture of generator models that generate diverse sentences based on the retrieved contexts. We conduct experiments on the DimonGen task and show that MoREE outperforms strong baselines in terms of both the quality and diversity of the generated sentences. Our results demonstrate that MoREE is able to generate diverse sentences that reflect different relationships between concepts, leading to a comprehensive understanding of concept relationships.

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.

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

DeepMind的游戏理论与多代理团队研究多学科学习的几个方面，从计算近似值到游戏理论中的基本概念，再到在富裕的空间环境中模拟社会困境，并在困难的团队协调任务中培训3-D类人动物。我们小组的一个签名目的是使用DeepMind在DeepMind中提供的资源和专业知识，以深入强化学习来探索复杂环境中的多代理系统，并使用这些基准来提高我们的理解。在这里，我们总结了我们团队的最新工作，并提出了一种分类法，我们认为这重点介绍了多代理研究中许多重要的开放挑战。

蒙版的视觉建模（MVM）最近已被证明对视觉预训练有效。虽然在视频输入（例如，蒙版框架建模）上进行了类似的重建目标，在视频语言（VIDL）预训练中探索了类似的重建目标，但先前研究中的预提取的视频功能在预训练期间无法通过MVM进行完善，因此无法通过MVM进行完善为下游性能不满意。在这项工作中，我们系统地检查了MVM在VIDL学习的背景下的潜力。具体而言，我们的研究基于完全端到端的视频变压器（Violet），该视频变压器（Violet）减轻了固定视频表示与MVM培训之间的断开连接。总共探索了MVM的八个不同的重建目标，从低级像素值和定向梯度到高级深度图，光流，离散的视觉令牌和潜在的视觉特征。我们进行全面的实验，并就导致有效MVM培训的因素提供见解。从经验上讲，我们展示了通过MVM目标预先训练的紫罗兰色，可以在13个VIDL基准测试中取得显着改进，从视频问题回答，视频字幕到文本到视频检索等等。

荧光镜检查是一种使用X射线来获得3D对象内部的实时2D视频，帮助外科医生观察病理结构和组织功能，尤其是在干预过程中。然而，它主要是由于低剂量X射线的临床使用而产生的，因此需要荧光镜检查技术。这种脱牙受到了成像对象与X射线成像系统之间的相对运动的挑战。我们通过提出一个自制的三阶段框架来应对这一挑战，从而利用荧光镜检查的领域知识。 （i）稳定：我们首先基于光流计算构建动态全景，以稳定X射线检测器的运动引起的非平稳背景。 （ii）分解：然后，我们提出了一种新型的基于掩模的鲁棒原理分析（RPCA）分解方法，以将探测器运动的视频分离为低级别背景和稀疏前景。这样的分解可容纳专家的阅读习惯。 （iii）denoise：我们终于通过自我监督的学习策略分别降低了背景和前景，并通过双侧时空滤波器将deno的部分融合到最终输出中。为了评估我们工作的有效性，我们策划了27个视频（1,568帧）和相应的地面真相的专用荧光镜数据集。我们的实验表明，与标准方法相比，它在降解和增强效果方面取得了重大改进。最后，专家评级确认了这种功效。

对于医疗保健提供者提供适当的患者护理的准确和详细说明，包括患者时​​间表中的药物变化，至关重要。医疗保健提供者或患者本身可能会引发患者药物的改变。用药更改采用多种形式，包括处方药和相关剂量修饰。这些更改提供了有关患者整体健康以及导致当前护理的理由的信息。然后，未来的护理可以基于患者的最终状态。这项工作探讨了从自由文本临床注释中自动提取药物变化信息。上下文药物事件数据集（CMED）是临床注释的语料库，其注释可以通过多种变化相关的属性来表征药物变化，包括更改的类型（启动，停止，增加等），更改，时间性，时间性，时间性，时间性，时间性，时间。改变可能性和否定。使用CMED，我们确定了临床文本中的药物提及，并提出了三个新型的基于BERT的新型基于BERT的系统，以解决注释的药物变化特征。我们证明，我们建议的体系结构改善了对CMED的初始工作改善药物变更分类的性能。我们确定了0.959 F1的高性能的药物提及，我们提出的系统将药物变化及其属性分类为0.827 F1。