Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime. Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

最近的图像匿名工作表明，生成的对抗性网络（GANS）可以生成近乎光容化的面对匿名的个人。但是，将这些网络扩展到整个人体仍然是一个具有挑战性和未解决的任务。我们提出了一种新的匿名化方法，用于为野外图像产生近乎光容化的人类。我们的设计的关键部分是通过在图像和规范3D表面之间的密集像素对应关系来引导对抗性网。我们介绍了各种表面自适应调制（V-SAM），该调制（V-SAM）在整个发电机中嵌入表面信息。通过我们的新型鉴别器表面监控损失，发电机可以合成高质量的人类，在复杂和不同的场景中具有多样化的外观。我们展示了这种表面指导显着提高了样本的图像质量和多样性，产生了高度实用的发电机。最后，我们证明了表面引导的匿名化保留了未来计算机视觉开发数据的可用性

目的：自动化肺肿瘤定位和放射性图像分割等任务可以为放射科和其他临床人员提供宝贵的时间。卷积神经网络可能适用于这样的任务，但需要大量标记的数据训练。获得标记数据是一个挑战，尤其是在医学领域。方法：本文调查了教师学生设计的使用，利用具有不同类型监督的数据集来训练在计算机断层摄影图像上进行肺肿瘤分割的自动模型。该框架由两种型号组成：执行端到端的自动肿瘤细分的学生和在培训期间提供学生额外的伪注释数据的教师。结果：仅使用小比例的语义标记数据和大量边界框注释数据，我们使用教师学生设计实现了竞争性能。培训的型号培训的大量语义注释并没有比教师注释数据所培训的模型更好。结论：我们的结果展示了利用教师学生设计的潜力来减少注释负荷，因为可以执行较少的监督注释方案，而没有分割精度的任何实际降级。

We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic Inference (A-NeSI): a new framework for PNL that uses neural networks for scalable approximate inference. A-NeSI 1) performs approximate inference in polynomial time without changing the semantics of probabilistic logics; 2) is trained using data generated by the background knowledge; 3) can generate symbolic explanations of predictions; and 4) can guarantee the satisfaction of logical constraints at test time, which is vital in safety-critical applications. Our experiments show that A-NeSI is the first end-to-end method to scale the Multi-digit MNISTAdd benchmark to sums of 15 MNIST digits, up from 4 in competing systems. Finally, our experiments show that A-NeSI achieves explainability and safety without a penalty in performance.

Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became the main benchmark. In this work, we are going to show how two simple methods: pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.

The Makespan Scheduling problem is an extensively studied NP-hard problem, and its simplest version looks for an allocation approach for a set of jobs with deterministic processing times to two identical machines such that the makespan is minimized. However, in real life scenarios, the actual processing time of each job may be stochastic around the expected value with a variance, under the influence of external factors, and the actual processing times of these jobs may be correlated with covariances. Thus within this paper, we propose a chance-constrained version of the Makespan Scheduling problem and investigate the theoretical performance of the classical Randomized Local Search and (1+1) EA for it. More specifically, we first study two variants of the Chance-constrained Makespan Scheduling problem and their computational complexities, then separately analyze the expected runtime of the two algorithms to obtain an optimal solution or almost optimal solution to the instances of the two variants. In addition, we investigate the experimental performance of the two algorithms for the two variants.

To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. The operation of sustainable power grids with a high penetration of renewable energies requires new methods to analyze the dynamic stability. We provide new datasets of dynamic stability of synthetic power grids and find that graph neural networks (GNNs) are surprisingly effective at predicting the highly non-linear target from topological information only. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Force modulation of robotic manipulators has been extensively studied for several decades. However, it is not yet commonly used in safety-critical applications due to a lack of accurate interaction contact modeling and weak performance guarantees - a large proportion of them concerning the modulation of interaction forces. This study presents a high-level framework for simultaneous trajectory optimization and force control of the interaction between a manipulator and soft environments, which is prone to external disturbances. Sliding friction and normal contact force are taken into account. The dynamics of the soft contact model and the manipulator are simultaneously incorporated in a trajectory optimizer to generate desired motion and force profiles. A constrained optimization framework based on Alternative Direction Method of Multipliers (ADMM) has been employed to efficiently generate real-time optimal control inputs and high-dimensional state trajectories in a Model Predictive Control fashion. Experimental validation of the model performance is conducted on a soft substrate with known material properties using a Cartesian space force control mode. Results show a comparison of ground truth and real-time model-based contact force and motion tracking for multiple Cartesian motions in the valid range of the friction model. It is shown that a contact model-based motion planner can compensate for frictional forces and motion disturbances and improve the overall motion and force tracking accuracy. The proposed high-level planner has the potential to facilitate the automation of medical tasks involving the manipulation of compliant, delicate, and deformable tissues.

Vision and language models (VL) are known to exploit unrobust indicators in individual modalities (e.g., introduced by distributional biases), instead of focusing on relevant information in each modality. A small drop in accuracy obtained on a VL task with a unimodal model suggests that so-called unimodal collapse occurred. But how to quantify the amount of unimodal collapse reliably, at dataset and instance-level, to diagnose and combat unimodal collapse in a targeted way? We present MM-SHAP, a performance-agnostic multimodality score that quantifies the proportion by which a model uses individual modalities in multimodal tasks. MM-SHAP is based on Shapley values and will be applied in two ways: (1) to compare models for their degree of multimodality, and (2) to measure the contribution of individual modalities for a given task and dataset. Experiments with 6 VL models -- LXMERT, CLIP and four ALBEF variants -- on four VL tasks highlight that unimodal collapse can occur to different degrees and in different directions, contradicting the wide-spread assumption that unimodal collapse is one-sided. We recommend MM-SHAP for analysing multimodal tasks, to diagnose and guide progress towards multimodal integration. Code available at: https://github.com/Heidelberg-NLP/MM-SHAP