几种慢性肺疾病，例如特发性肺纤维化（IPF）的特征是气道异常扩张。计算机断层扫描（CT）上气道特征的定量可以帮助表征疾病进展。已经开发了基于物理的气道测量算法，但由于在临床实践中看到的气道形态多样性，因此取得了有限的成功。由于获得精确的气道注释的高成本，监督学习方法也不可行。我们建议使用感知损失通过样式转移进行综合气道，以训练我们的模型气道转移网络（ATN）。我们使用a）定性评估将ATN模型与最先进的GAN网络（SIMGAN）进行比较； b）评估基于ATN和SIMGAN的CT气道指标预测113例IPF患者死亡率的能力。与Simgan相比，ATN被证明更快，更容易训练。还发现基于ATN的气道测量值始终比IPF CTS上的SIMGAN衍生气道指标更强大。通过转化网络使用感知损失来完善合成数据的转化网络是基于GAN的方法的现实替代方法，用于用于特发性肺纤维化的临床CT分析。我们的源代码可以在https://github.com/ashkanpakzad/atn上找到，该源代码与Airquant的现有开放源气道分析框架兼容。

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.

We present the Group Propagation Vision Transformer (GPViT): a novel nonhierarchical (i.e. non-pyramidal) transformer model designed for general visual recognition with high-resolution features. High-resolution features (or tokens) are a natural fit for tasks that involve perceiving fine-grained details such as detection and segmentation, but exchanging global information between these features is expensive in memory and computation because of the way self-attention scales. We provide a highly efficient alternative Group Propagation Block (GP Block) to exchange global information. In each GP Block, features are first grouped together by a fixed number of learnable group tokens; we then perform Group Propagation where global information is exchanged between the grouped features; finally, global information in the updated grouped features is returned back to the image features through a transformer decoder. We evaluate GPViT on a variety of visual recognition tasks including image classification, semantic segmentation, object detection, and instance segmentation. Our method achieves significant performance gains over previous works across all tasks, especially on tasks that require high-resolution outputs, for example, our GPViT-L3 outperforms Swin Transformer-B by 2.0 mIoU on ADE20K semantic segmentation with only half as many parameters. Code and pre-trained models are available at https://github.com/ChenhongyiYang/GPViT .

As aerial robots are tasked to navigate environments of increased complexity, embedding collision tolerance in their design becomes important. In this survey we review the current state-of-the-art within the niche field of collision-tolerant micro aerial vehicles and present different design approaches identified in the literature, as well as methods that have focused on autonomy functionalities that exploit collision resilience. Subsequently, we discuss the relevance to biological systems and provide our view on key directions of future fruitful research.

With the goal of increasing the speed and efficiency in robotic dual-arm manipulation, a novel control approach is presented that utilizes intentional simultaneous impacts to rapidly grasp objects. This approach uses the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact reference vector fields are used. These vector fields are coupled via the impact dynamics in proximity of the expected impact area, minimizing the otherwise large velocity errors after the impact and the corresponding large control efforts. A purely spatial task is introduced to strongly encourage the synchronization of impact times of the two arms. An interim-impact control phase provides robustness in the execution against the inevitable lack of exact impact simultaneity and the corresponding unreliable velocity error. In this interim phase, a position feedback signal is derived from the ante-impact velocity reference, which is used to enforce sustained contact in all contact points without using velocity error feedback. With an eye towards real-life implementation, the approach is formulated using a QP control framework, and is validated using numerical simulations on a realistic robot model with flexible joints and low-level torque control.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

本文提出了一种以非零速度的效果友好型捕捉对象的混合优化和学习方法。通过受约束的二次编程问题，该方法生成最佳轨迹，直至机器人和对象之间的接触点，以最小化其相对速度并减少初始影响力。接下来，生成的轨迹是由基于人类的捕捉演示的旋风动作原始词更新的，以确保围绕接口点的平稳过渡。此外，学习的人类可变刚度（HVS）被发送到机器人的笛卡尔阻抗控制器，以吸收后影响力并稳定捕获位置。进行了三个实验，以将我们的方法与固定位置阻抗控制器（FP-IC）进行比较。结果表明，所提出的方法的表现优于FP-IC，同时添加HVS可以更好地吸收影响后力。

我们提出了连续表示的时间扩展变化，我们称其为t-SR。 T-SR通过在原始动作重复序列上构造后继表示，捕获了时间扩展动作的预期状态过渡动力学。这种时间抽象的这种形式不能学习相关任务结构的自上而下的层次结构，而是对耦合动作和动作重复的自下而上的组成。这减少了在没有学习层次政策的情况下控制中所需的决策数量。因此，T-SR直接考虑了时间扩展的动作序列的时间范围，而无需预定义或域特异性选项。我们表明，在具有动态奖励结构的环境中，T-SR能够利用后继表示的灵活性和时间扩展的动作提供的抽象。因此，在一系列稀疏的网格世界环境中，T-SR最佳地适应策略远比基于可比的无模型的强化学习方法快得多。我们还表明，T-SR学到的解决这些任务的方式要求学习的策略的始终如一的频率比非临时扩展的策略少。

在科学计算的许多领域越来越流行的人工神经网络（ANN）的大量使用迅速增加了现代高性能计算系统的能源消耗。新型的神经形态范式提供了一种吸引人的替代方案，它直接在硬件中实施了ANN。但是，对于科学计算中用例使用ANN在神经形态硬件上运行ANN的实际好处知之甚少。在这里，我们提出了一种方法，用于测量使用常规硬件的ANN来计算推理任务的时间。此外，我们为这些任务设计了一个体系结构，并根据最先进的模拟内存计算（AIMC）平台估算了相同的指标，这是神经形态计算中的关键范例之一。在二维凝结物质系统中的量子多体物理学中的用例比较两种方法，并在粒子物理学中大型强子对撞机上以40 MHz的速率以40 MHz的速率进行异常检测。我们发现，与传统硬件相比，AIMC最多可以达到一个较短的计算时间，最高三个数量级的能源成本。这表明使用神经形态硬件进行更快，更可持续的科学计算的潜力。

癌症护理中的治疗决策受到随机对照试验（RCT）的治疗效应估计的指导。 RCT估计在某个人群中，一种治疗与另一种治疗的平均效应。但是，治疗可能对人群中的每个患者都不同样有效。了解针对特定患者和肿瘤特征量身定制的治疗的有效性将实现个性化的治疗决策。通过平均RCT中不同患者亚组的结果来获得量身定制的治疗效果，需要大量的患者在所有相关亚组中具有足够的统计能力，以实现所有可能的治疗。美国癌症联合委员会（AJCC）建议研究人员开发结果预测模型（OPMS），以实现个性化治疗决策。 OPM有时称为风险模型或预后模型，使用患者和肿瘤特征来预测患者的结局，例如总体生存。假设这些预测对于使用“只有在OPM预测患者具有高复发风险的情况下开出化学疗法的规则”之类的规则，对治疗决策有用。 AJCC认识到可靠预测的重要性，发布了OPM的清单，以确保设计OPM设计的患者群体的可靠OPM预测准确性。但是，准确的结果预测并不意味着这些预测会产生良好的治疗决策。从这个角度来看，我们表明OPM依靠固定的治疗政策，这意味着被发现可以准确预测验证研究结果的OPM在用于治疗决策的情况下仍会导致患者伤害。然后，我们提供有关如何开发对个性化治疗决策有用的模型以及如何评估模型是否具有决策价值的指导。