In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies. We propose an approach for integrating multiple sources of such prior information into penalised regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application. The proposed method is implemented in the R package `transreg' (https://github.com/lcsb-bds/transreg).

在高维预测设置中，可靠地估计测试性能仍然具有挑战性。为了应对这一挑战，提出了一个新颖的性能估计框架。该框架称为Learn2Evaluate，是基于学习曲线的，它通过拟合平滑的单调曲线将测试性能描绘为样本量的函数。与常用的性能估计方法相比，Learn2esvaluate具有多个优势。首先，学习曲线提供了学习者的图形概述。该概述有助于评估添加培训样本的潜在优势，并且比固定子样本大小的性能估计值更完整地比较学习者。其次，学习曲线促进在总样本量而不是子样本大小的情况下估计性能。第三，LEALLE2ALE2允许计算理论上合理且有用的较低置信度结合。此外，可以通过执行偏置校正来拧紧这种结合。通过模拟研究和对OMICS数据的应用来说明LEAL2评论的好处。

胰腺癌是与癌症相关死亡的全球主要原因之一。尽管深度学习在计算机辅助诊断和检测方法（CAD）方法中取得了成功，但很少关注胰腺癌的检测。我们提出了一种检测胰腺肿瘤的方法，该方法在周围的解剖结构中利用临床上的特征，从而更好地旨在利用放射科医生的知识，而不是其他常规的深度学习方法。为此，我们收集了一个新的数据集，该数据集由99例胰腺导管腺癌（PDAC）和97例没有胰腺肿瘤的对照病例组成。由于胰腺癌的生长模式，肿瘤可能总是可见为低音病变，因此，专家指的是二次外部特征的可见性，这些特征可能表明肿瘤的存在。我们提出了一种基于U-NET样深的CNN的方法，该方法利用以下外部次要特征：胰管，常见的胆管和胰腺以及处理后的CT扫描。使用这些功能，该模型如果存在胰腺肿瘤。这种用于分类和本地化方法的细分实现了99％的敏感性（一个案例）和99％的特异性，这比以前的最新方法的灵敏度增加了5％。与以前的PDAC检测方法相比，该模型还以合理的精度和较短的推理时间提供位置信息。这些结果提供了显着的性能改善，并强调了在开发新型CAD方法时纳入临床专家知识的重要性。

束缚的四型旋转器用于扩展飞行操作，其中通过连接到外部电源的系绳提供了系统的电源。在这项工作中，我们考虑了由单个系绳动力的多个四轮驱动器组成的系统。我们研究影响功率需求的设计因素，例如系绳的电阻，输入电压和四轮驱动器的位置。我们提出了一项分析，以预测为每个Quadcopter的推力提供的一系列n系二极管的所需功率，以确保电气安全性并有助于设计优化。我们发现，由于基本电气限制，推力的临界边界无法超过。我们比较了一个束缚的四轮驱动器和两个束缚四轮驱动器的功耗，并表明，对于距锚点足够远的大型四轮驱动器，一个二极管系统会消耗较小的功率。我们表明，对于一种代表性的消防用例，一个具有两个四轮驱动器的束缚系统比带有一个四轮驱动器的相应系统少消耗26％的功率。最后，我们提出了实验，证明了在混乱的环境中与一四分之一的束缚系统相比，使用两四分之一的束缚系统，例如穿过窗户并将物体抓住障碍物。

逻辑回归是一种用于二进制分类的常用方法。研究人员通常具有多个二进制响应变量，并且同时分析是有益的，因为它可以深入了解响应变量之间的依赖性以及预测变量和响应之间的依赖性。此外，在这样的同时分析中，方程式可以相互借出强度，这可能会提高预测精度。在本文中，我们提出了同时二进制逻辑回归建模的旋律家族。在这个家族中，基于距离规则，在降低维度的欧几里得空间中定义了回归模型。该模型可以用逻辑回归系数或双皮子来解释。我们讨论了用于参数估计的快速迭代术（或MM）算法。详细显示了两种应用：一种将人格特征与药物消费概况有关的应用，以及一个与抑郁症和焦虑症有关的人格特征。我们将旋律家族与多元二元数据的替代方法进行了详尽的比较。

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.

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.

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.

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。

该文档概述了Prospero预先注册的方案，用于对口腔或口腔或肉桂癌治疗后语音变化的系统审查进行系统审查。口腔中肿瘤的治疗可能会导致生理变化，这可能导致发音困难。由于疤痕组织和/或潜在的（术后）放射治疗，舌头变得不那么流动。此外，组织损失可能会为气流或极限收缩可能性创造旁路。为了更好地了解语音问题的性质，需要有关枢纽运动的信息，因为感知信息或声学信息仅提供了间接的关节变化证据。因此，这项系统的综述将回顾研究，该研究直接测量口腔或口咽癌治疗后舌，下巴和嘴唇的关节运动。