运营商网络已成为有希望的深度学习工具，用于近似偏微分方程（PDE）的解决方案。这些网络绘制了描述材料属性，迫使函数和边界数据的输入函数到PDE解决方案。这项工作描述了一种针对操作员网络的新体系结构，该架构模仿了从问题的变异公式或弱公式中获得的数值解决方案的形式。这些想法在通用椭圆的PDE中的应用导致变异模拟操作员网络（Varmion）。像常规的深层操作员网络（DeepOnet）一样，Varmion也由一个子网络组成，该子网络构建了输出的基础函数，另一个构造了这些基础函数系数的基本功能。但是，与deponet相反，在Varmion中，这些网络的体系结构是精确确定的。对Varmion解决方案中误差的分析表明，它包含训练数据中的误差，训练错误，抽样输入中的正交误差和输出功能的贡献，以及测量测试输入功能之间距离的“覆盖错误”以及培训数据集中最近的功能。这也取决于确切网络及其varmion近似的稳定性常数。 Varmion在规范椭圆形PDE中的应用表明，对于大约相同数量的网络参数，平均而言，Varmion的误差比标准DeepOnet较小。此外，其性能对于输入函数的变化，用于采样输入和输出功能的技术，用于构建基本函数的技术以及输入函数的数量更为强大。

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

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 work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another.

Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained huge popularity, their algorithmic bias evaluation is crucial due to the existing association between social determinants of health and delirium risk. In this context, using MIMIC-III and another academic hospital dataset, we present some initial experimental evidence showing how sociodemographic features such as sex and race can impact the model performance across subgroups. With this work, our intent is to initiate a discussion about the intersectionality effects of old age, race and socioeconomic factors on the early-stage detection and prevention of delirium using ML.

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.

2型糖尿病（T2DM）的早期诊断对于及时的治疗干预措施和生活方式改变至关重要。随着医学成像数据在许多患者群体中变得更广泛可用，我们试图研究是否可以在表格学习分类器模型中利用图像衍生的表型数据来预测T2DM的发病率，而无需使用侵入性血液实验室测量。我们表明，使用图像衍生表型的神经网络和决策树模型都可以预测患者T2DM状态的召回评分高达87.6％。我们还提出了与“ Syntha1c编码器”相同的结构的新颖使用，这些结构能够输出模仿血液血红蛋白A1C经验实验室测量值的可解释值。最后，我们证明了T2DM风险预测模型对输入矢量成分中小扰动的敏感性可用于预测从以前看不见的患者人群中取样的协变量的性能。

从有限的资源中获得最大收益可以进步自然语言处理（NLP）研究和实践，同时保守资源。这些资源可能是数据，时间，存储或能源。NLP的最新工作从缩放率产生了有趣的结果。但是，仅使用比例来改善结果意味着资源消耗也会扩展。这种关系激发了对有效方法的研究，这些方法需要更少的资源才能获得相似的结果。这项调查涉及NLP效率的方法和发现，旨在指导该领域的新研究人员并激发新方法的发展。

多实施学习（MIL）被广泛用于对病理整体幻灯片图像（WSIS）的计算机辅助解释，以解决缺乏像素或贴片的注释。通常，这种方法直接应用“自然图像驱动”的MIL算法，该算法忽略了WSIS的多尺度（即金字塔）性质。现成的MIL算法通常部署在单个WSIS（例如20x放大倍率）上，而人类病理学家通常以多尺度的方式汇总全球和局部模式（例如，通过放大不同大型）。在这项研究中，我们提出了一种新型的跨尺度注意机制，以明确地将尺度间相互作用汇总到单个MIL网络的克罗恩病（CD）（CD），这是炎症性肠病的一种形式。本文的贡献是两个方面：（1）提出了一种跨尺度注意机制，以从不同分辨率的多尺度相互作用汇总特征； （2）生成差异多尺度注意的可视化，以定位可解释的病变模式。通过训练来自20名CD患者的约250,000 H＆E染色的上升结肠（AC）斑块，在不同尺度上训练30个健康对照样品，我们的方法在曲线下（AUC）得分为0.8924，与基线模型相比达到0.8924。官方实施可在https://github.com/hrlblab/cs-mil上公开获得。

我们介绍了Proteus，这是一种新型的自设计近似范围滤波器，它根据采样数据进行配置，以便针对给定的空间要求优化其误报率（FPR）。 Proteus统一了最先进的范围过滤器的概率和确定性设计空间，以在较大的用例中实现稳健的性能。 Proteus的核心是我们的上下文前缀FPR（CPFPR）模型 - 在其设计空间中基于前缀过滤器的FPR的正式框架。我们从经验上证明了模型和Proteus在合成工作负载和现实世界数据集上优化的能力的准确性。我们进一步评估了RockSDB中的Proteus，并表明它能够将端到端的性能提高到5.3倍，而不是更脆的先进方法，例如Surf和Rosetta。我们的实验还表明，与端到端的性能增长相比，建模的成本并不显着，并且Proteus对工作负载转移的稳定性。