分子动力学模拟是许多科学领域中的宝贵工具。但是，无处不在的经典力场无法描述反应性系统，量子分子动力学在计算上要求太大，无法处理大型系统或长时间尺度。基于物理或机器学习的反应力场可以在时间和长度尺度上弥合差距，但是这些力场需要大量努力来构建，并且对给定的化学组成和应用高度特异性。机器学习模型的一个重要局限性是使用特定于元素的功能，导致模型随着元素数量而缩小范围很差。这项工作介绍了高斯多极（GMP）特征化方案，该方案利用了原子周围电子密度的物理相关的多极膨胀，以产生特征向量，这些向量在元素类型之间插值并且具有固定尺寸，而不管存在的元素数量。我们将GMP与神经网络相结合，将其直接与MD17数据集的广泛使用的Beller-Parinello对称函数进行比较，从而表明它表现出提高的准确性和计算效率。此外，我们证明了基于GMP的模型可以实现QM9数据集的化学准确性，即使推断到新元素时，它们的准确性仍然是合理的。最后，我们测试了基于GMP的开放式催化项目（OCP）数据集的模型，揭示了与图形卷积深度学习模型相当的性能。结果表明，这种特征方案填补了有效且可转移的机器学习力场的构建方面的关键空白。

化学动力学和反应工程包括解除反应机制的现象学框架，优化反应性能和化学过程的合理设计。这里，我们利用前馈人工神经网络作为基础函数来解决由描述微蓄电图（MKMS）的差分代数方程（DAE）约束的常微分方程（杂物）。我们提出了一种代数框架，用于反应网络，基本反应类型和化学物种的数学描述和分类。在该框架下，我们证明了在正则化的多目标优化设置中同时训练了神经网络和动力学模型参数，通过估计来自合成实验数据的动力学参数来导致逆问题的解决方案。我们分析了一组方案，以确定可以从瞬态动力学数据检索动力学参数的程度，并评估方法的鲁棒性相对于统计噪声。这种反向动力学杂散的方法可以帮助基于瞬态数据阐明反应机制。

近年来，机器学习（ML）在化学信息学和电子结构理论领域中广受欢迎。这些技术通常需要研究人员来设计摘要的“特征”，这些特征将化学概念编码为与机器学习模型的输入兼容的数学形式。但是，没有现有的工具可以将这些抽象功能连接回实际的化学系统，从而使诊断失败并建立有关功能含义的直觉变得困难。我们提出了Electrolens，这是一种新的可视化工具，用于高维空间分辨的特征，以解决此问题。该工具通过一系列链接的3D视图和2D图可视化原子和电子环境特征的高维数据集。该工具能够通过交互式选择在3D中连接不同的派生功能及其相应区域。它的构建是可扩展的，并与现有基础架构集成。

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.

Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts, but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this manuscript, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them to their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find average performance improvements of 4.2% across model complexities and prediction tasks, with substantial performance improvements of up to 16.4% in some cases. Furthermore, we find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Out-of-distribution generalization (OODG) is a longstanding challenge for neural networks. This challenge is quite apparent in tasks with well-defined variables and rules, where explicit use of the rules could solve problems independently of the particular values of the variables, but networks tend to be tied to the range of values sampled in their training data. Large transformer-based language models have pushed the boundaries on how well neural networks can solve previously unseen problems, but their complexity and lack of clarity about the relevant content in their training data obfuscates how they achieve such robustness. As a step toward understanding how transformer-based systems generalize, we explore the question of OODG in small scale transformers trained with examples from a known distribution. Using a reasoning task based on the puzzle Sudoku, we show that OODG can occur on a complex problem if the training set includes examples sampled from the whole distribution of simpler component tasks. Successful generalization depends on carefully managing positional alignment when absolute position encoding is used, but we find that suppressing sensitivity to absolute positions overcomes this limitation. Taken together our results represent a small step toward understanding and promoting systematic generalization in transformers.

Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps. However, the solution sequences are not formally structured and the resulting model-generated sequences may not reflect the kind of systematic reasoning we might expect an expert human to produce. In this paper, we study how to build stronger reasoning capability in language models using the idea of relational abstractions. We introduce new types of sequences that more explicitly provide an abstract characterization of the transitions through intermediate solution steps to the goal state. We find that models that are supplied with such sequences as prompts can solve tasks with a significantly higher accuracy, and models that are trained to produce such sequences solve problems better than those that are trained with previously used human-generated sequences and other baselines. Our work thus takes several steps toward elucidating and improving how language models perform on tasks requiring multi-step mathematical reasoning.

使用相对比心脏磁共振成像（PC-CMR）进行的流量分析可以量化用于评估心血管功能的重要参数。该分析的重要部分是鉴定正确的CMR视图和质量控制（QC），以检测可能影响流量定量的伪像。我们提出了一个新型的基于深度学习的框架，用于对完整CMR扫描的流量进行完全自动化的分析，该框架首先使用两个顺序卷积神经网络进行这些视图选择和QC步骤，然后进行自动主动脉和肺动脉分段，以实现对量化的量化。钥匙流参数。对于观察分类和QC，获得了0.958和0.914的精度值。对于细分，骰子分数为$> $ 0.969，而平淡的altman情节表示手动和自动峰流量值之间的一致性很高。此外，我们在外部验证数据集上测试了管道，结果表明管道的鲁棒性。这项工作是使用由986例病例组成的多生临床数据进行的，表明在临床环境中使用该管道的潜力。

通用数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其集成深度表型所需的资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可用于生物学知识的语义计算表示，并能够整合多种生物医学数据。但是，将EHR数据映射到OBO Foundry本体论需要大量的手动策展和域专业知识。我们介绍了一个框架，用于将观察性医学成果合作伙伴关系（OMOP）标准词汇介绍给OBO铸造本体。使用此框架，我们制作了92,367条条件，8,615种药物成分和10,673个测量结果的映射。域专家验证了映射准确性，并且在24家医院进行检查时，映射覆盖了99％的条件和药物成分和68％的测量结果。最后，我们证明OMOP2OBO映射可以帮助系统地识别可能受益于基因检测的未诊断罕见病患者。