来自时间序列数据的因果推断的主要挑战是计算可行性和准确性之间的权衡。在具有缓慢均值逆转的自回旋模型中，由滞后协方差的过程基序激励，我们建议通过成对边缘测量（PEM）推断因果关系网络，即可以轻松地从滞后相关矩阵中计算出来。通过过程基序对协方差和滞后方差的贡献，我们制定了两个pem，这些PEM适合混杂因素和反向因果关系。为了证明PEM的性能，我们考虑了线性随机过程的模拟网络干扰，并表明我们的PEM可以准确有效地推断网络。具体而言，对于略有自相关的时间序列数据，我们的方法获得的准确性高于或类似于Granger因果关系，转移熵和收敛的交叉映射 - 但使用这些方法中的任何一种都比计算时间短得多。我们的快速准确的PEM是用于网络推断的易于实现的方法，具有明确的理论基础。它们为当前范式提供了有希望的替代方案，用于从时间序列数据中推断线性模型，包括Granger因果关系，矢量自动进展和稀疏逆协方差估计。

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.

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.

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。

自闭症谱系障碍（ASD）是一种脑部疾病，其特征是幼儿时期出现的各种体征和症状。 ASD还与受影响个体的沟通缺陷和重复行为有关。已经开发了各种ASD检测方法，包括神经影像学和心理测试。在这些方法中，磁共振成像（MRI）成像方式对医生至关重要。临床医生依靠MRI方式准确诊断ASD。 MRI模态是非侵入性方法，包括功能（fMRI）和结构（SMRI）神经影像学方法。但是，用fMRI和SMRI诊断为专家的ASD的过程通常很费力且耗时。因此，已经开发了基于人工智能（AI）的几种计算机辅助设计系统（CAD）来协助专家医生。传统的机器学习（ML）和深度学习（DL）是用于诊断ASD的最受欢迎的AI方案。这项研究旨在使用AI审查对ASD的自动检测。我们回顾了使用ML技术开发的几个CAD，以使用MRI模式自动诊断ASD。在使用DL技术来开发ASD的自动诊断模型方面的工作非常有限。附录中提供了使用DL开发的研究摘要。然后，详细描述了使用MRI和AI技术在自动诊断ASD的自动诊断期间遇到的挑战。此外，讨论了使用ML和DL自动诊断ASD的研究的图形比较。最后，我们提出了使用AI技术和MRI神经影像学检测ASD的未来方法。

因果表示学习是识别基本因果变量及其从高维观察（例如图像）中的关系的任务。最近的工作表明，可以从观测的时间序列中重建因果变量，假设它们之间没有瞬时因果关系。但是，在实际应用中，我们的测量或帧速率可能比许多因果效应要慢。这有效地产生了“瞬时”效果，并使以前的可识别性结果无效。为了解决这个问题，我们提出了ICITRI，这是一种因果表示学习方法，当具有已知干预目标的完美干预措施时，可以在时间序列中处理瞬时效应。 Icitris从时间观察中识别因果因素，同时使用可区分的因果发现方法来学习其因果图。在三个视频数据集的实验中，Icitris准确地识别了因果因素及其因果图。

This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. To investigate the efficacy of this approach, models were evaluated by dissociating the streams and training/testing in the same rigorous manner as the main classifiers. Using an annotated, publicly available dataset of a singly-housed mice, we achieve prediction accuracy of 86.47% using an ensemble of a Inception-based network and an attention-based network, both of which utilize this feature sharing. We also demonstrate through ablation studies that for all models, the feature-sharing architectures consistently perform better than conventional ones having separate streams. The best performing models were further evaluated on other activity datasets, both mouse and human. Future work will investigate the effectiveness of feature sharing to behavioural classification in the unsupervised anomaly detection domain.

从视觉观察中了解动态系统的潜在因果因素被认为是对复杂环境中推理的推理的关键步骤。在本文中，我们提出了Citris，这是一种变异自动编码器框架，从图像的时间序列中学习因果表示，其中潜在的因果因素可能已被干预。与最近的文献相反，Citris利用了时间性和观察干预目标，以鉴定标量和多维因果因素，例如3D旋转角度。此外，通过引入归一化流，可以轻松扩展柑橘，以利用和删除已验证的自动编码器获得的删除表示形式。在标量因果因素上扩展了先前的结果，我们在更一般的环境中证明了可识别性，其中仅因果因素的某些成分受干预措施影响。在对3D渲染图像序列的实验中，柑橘类似于恢复基本因果变量的先前方法。此外，使用预验证的自动编码器，Citris甚至可以概括为因果因素的实例化，从而在SIM到现实的概括中开放了未来的研究领域，以进行因果关系学习。

从连续流体流生成液滴需要精确调谐设备以找到优化的控制参数条件。它在分析上棘手，以计算产生优化液滴的液滴生成设备的必要控制参数值。此外，随着流体流动的长度尺度变化，地层物理和诱导流量分解成液滴的优化条件也会改变。因此，单个比例积分衍生控制器太低，无法优化不同长度尺度或不同控制参数的设备，而分类机学习技术需要数天捕获并要求数百万滴图像。因此，问题提出，可以创建一个单一的方法，该方法普遍优化多个数据点的多个长度液滴，并且比以前的方法更快？在本文中，贝叶斯优化和计算机视觉反馈回路旨在快速可靠地发现在不同长度级设备中生成优化的液滴的控制参数值。该方法被证明在仅2.3小时内仅使用60张图像的最佳参数值会聚到比以前的方法快30倍。两种不同的长度尺度设备演示了模型实现：毫师喷墨设备和MiCof流体设备。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.