卵巢癌是最致命的妇科恶性肿瘤。该疾病在早期阶段最常是无症状的，其诊断依赖于经阴道超声图像的专家评估。超声是表征附加质量的一线成像方式，它需要大量的专业知识，其分析是主观的和劳动的，因此易于误差。因此，在临床实践中需要进行自动化的过程，以促进和标准化扫描评估。使用监督的学习，我们证明了附加质量的分割是可能的，但是，患病率和标签不平衡限制了代表性不足的类别的性能。为了减轻这种情况，我们应用了一种新颖的病理学数据合成器。我们通过使用Poisson图像编辑将较少常见的质量整合到其他样品中，从而创建及其相应的地面真实分割的合成医学图像。我们的方法在所有班级中都取得了最佳性能，包括与NNU-NET基线方法相比，提高了多达8％。

医学成像中各种各样的分布和分布数据使通用异常检测成为一项艰巨的任务。最近，已经开发了许多自我监督的方法，这些方法是对健康数据的端到端模型，并具有合成异常的增强。但是，很难比较这些方法，因为尚不清楚绩效的收益是从任务本身还是围绕其培训管道来进行的。也很难评估一项任务是否可以很好地通用通用异常检测，因为它们通常仅在有限的异常范围内进行测试。为了协助这一点，我们开发了NOOD，该框架适应NNU-NET，以比较自我监督的异常定位方法。通过将综合，自我监督的任务隔离在其余培训过程中，我们对任务进行了更忠实的比较，同时还可以快速简便地评估给定数据集的工作流程。使用此功能，我们实施了当前的最新任务，并在具有挑战性的X射线数据集上对其进行了评估。

虽然从图像中回归3D人类的方法迅速发展，但估计的身体形状通常不会捕获真正的人形状。这是有问题的，因为对于许多应用，准确的身体形状与姿势一样重要。身体形状准确性差姿势准确性的关键原因是缺乏数据。尽管人类可以标记2D关节，并且这些约束3D姿势，但“标记” 3D身体形状并不容易。由于配对的数据与图像和3D身体形状很少见，因此我们利用了两个信息来源：（1）我们收集了各种“时尚”模型的互联网图像，以及一系列的人体测量值； （2）我们为3D身体网眼和模型图像收集语言形状属性。综上所述，这些数据集提供了足够的约束来推断密集的3D形状。我们利用几种新型方法来利用人体测量和语言形状属性来训练称为Shapy的神经网络，从而从RGB图像中回归了3D人类的姿势和形状。我们在公共基准测试上评估shapy，但请注意，它们要么缺乏明显的身体形状变化，地面真实形状或衣服变化。因此，我们收集了一个新的数据集，用于评估3D人类形状估计，称为HBW，其中包含“野生人体”的照片，我们为其具有地面3D身体扫描。在这个新的基准测试中，Shapy在3D身体估计的任务上的最先进方法极大地胜过。这是第一次演示，即可以从易于观察的人体测量和语言形状属性中训练来自图像的3D体形回归。我们的模型和数据可在以下网址获得：shapy.is.tue.mpg.de

Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images.

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.