在本文中，我们提出了一个新颖的对象级映射系统，该系统可以同时在动态场景中分段，跟踪和重建对象。它可以通过对深度输入的重建和类别级别的重建来进一步预测并完成其完整的几何形状，其目的是完成对象几何形状会导致更好的对象重建和跟踪准确性。对于每个传入的RGB-D帧，我们执行实例分割以检测对象并在检测和现有对象图之间构建数据关联。将为每个无与伦比的检测创建一个新的对象映射。对于每个匹配的对象，我们使用几何残差和差分渲染残留物共同优化其姿势和潜在的几何表示形式，并完成其形状之前和完成的几何形状。与使用传统的体积映射或学习形状的先验方法相比，我们的方法显示出更好的跟踪和重建性能。我们通过定量和定性测试合成和现实世界序列来评估其有效性。

当许多机器人必须在狭窄的空间中一起工作时，可以通过向前时间窗口进行精确的协调计划，可以安全，高效的运动，但这通常需要对所有设备的集中控制，这很难扩展。我们演示了GBP计划，这是一种基于高斯信念传播的多机器人计划问题的新型纯粹分布技术，该技术由定义动态和碰撞约束的通用因素图制成。在模拟中，我们表明我们的方法允许极高的性能协作计划，在繁忙，复杂的场景中，机器人能够互相交叉。即使在沟通失败的情况下，它们也比替代分布式计划技术保持更短，更快，更光滑的轨迹。

使用3D神经字段的几何形状，颜色和语义的关节表示使得能够使用手持式RGB-D传感器实时地重建场景的超稀疏交互来精确密集标记。我们的ILABEL系统不需要培训数据，但可以比在大型培训的图像数据集上培训的标准方法更准确地标记场景。此外，它以“开放式”方式工作，使用用户在飞行中定义语义类。 Ilabel的潜在模型是一款从头开始培训的多层的感知者（MLP），以实时地学习联合神经场景表示。场景模型是实时更新和可视化的，允许用户对焦相互作用以实现高效标记。可以将房间或类似的场景精确标记为10+语义类别，只需几十点击即可。定量标签精度使用点击次数强烈缩放，并迅速超越标准的预培训语义分段方法。我们还展示了一个分层标签变体。

We argue the case for Gaussian Belief Propagation (GBP) as a strong algorithmic framework for the distributed, generic and incremental probabilistic estimation we need in Spatial AI as we aim at high performance smart robots and devices which operate within the constraints of real products. Processor hardware is changing rapidly, and GBP has the right character to take advantage of highly distributed processing and storage while estimating global quantities, as well as great flexibility. We present a detailed tutorial on GBP, relating to the standard factor graph formulation used in robotics and computer vision, and give several simulation examples with code which demonstrate its properties.

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。