Multiple local steps are key to communication-efficient federated learning. However, theoretical guarantees for such algorithms, without data heterogeneity-bounding assumptions, have been lacking in general non-smooth convex problems. A typical FL algorithm consists of two main phases: local training and aggregation. Scaffold (Karimireddy et al., 2020) and Scaffnew (Mishchenko et al., 2022) stand out as notable We explore the following natural question in this work: Can multiple local steps provably reduce communication rounds in the non-smooth convex setting? Authors made an equal contribution to this work.

Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require very large labeled datasets. However, most medical imaging datasets are small, with a limited number of annotated samples. The reason they are small is usually because delineating medical images is time-consuming and demanding for oncologists. There are various techniques that can be used to augment a dataset, for example, to apply affine transformations or elastic transformations to available images, or to add synthetic images generated by a Generative Adversarial Network (GAN). In this work, we have developed a novel conditional variant of a current GAN method, the StyleGAN2, to generate multi-modal high-resolution medical images with the purpose to augment small medical imaging datasets with these synthetic images. We use the synthetic and real images from six datasets to train models for the downstream task of semantic segmentation. The quality of the generated medical images and the effect of this augmentation on the segmentation performance were evaluated afterward. Finally, the results indicate that the downstream segmentation models did not benefit from the generated images. Further work and analyses are required to establish how this augmentation affects the segmentation performance.

Advancements in science rely on data sharing. In medicine, where personal data are often involved, synthetic tabular data generated by generative adversarial networks (GANs) offer a promising avenue. However, existing GANs struggle to capture the complexities of real-world tabular data, which often contain a mix of continuous and categorical variables with potential imbalances and dependencies. We propose a novel correlation- and mean-aware loss function designed to address these challenges as a regularizer for GANs. To ensure a rigorous evaluation, we establish a comprehensive benchmarking framework using ten real-world datasets and eight established tabular GAN baselines. The proposed loss function demonstrates statistically significant improvements over existing methods in capturing the true data distribution, significantly enhancing the quality of synthetic data generated with GANs. The benchmarking framework shows that the enhanced synthetic data quality leads to improved performance in downstream machine learning (ML) tasks, ultimately paving the way for easier data sharing.

Multi-object grasping is a challenging task. It is important for energy and cost-efficient operation of industrial crane manipulators, such as those used to collect tree logs off the forest floor and onto forest machines. In this work, we used synthetic data from physics simulations to explore how data-driven modeling can be used to infer multi-object grasp poses from images. We showed that convolutional neural networks can be trained specifically for synthesizing multi-object grasps. Using RGB-Depth images and instance segmentation masks as input, a U-Net model outputs grasp maps with corresponding grapple orientation and opening width. Given an observation of a pile of logs, the model can be used to synthesize and rate the possible grasp poses and select the most suitable one, with the possibility to respect changing operational constraints such as lift capacity and reach. When tested on previously unseen data, the proposed model found successful grasp poses with an accuracy of 95%.

Convolutional neural networks have shown to be widely applicable to a large number of fields when large amounts of labelled data are available. The recent trend has been to use models with increasingly larger sets of tunable parameters to increase model accuracy, reduce model loss, or create more adversarially robust models -- goals that are often at odds with one another. In particular, recent theoretical work raises questions about the ability for even larger models to generalize to data outside of the controlled train and test sets. As such, we examine the role of the number of hidden layers in the ResNet model, demonstrated on the MNIST, CIFAR10, CIFAR100 datasets. We test a variety of parameters including the size of the model, the floating point precision, and the noise level of both the training data and the model output. To encapsulate the model's predictive power and computational cost, we provide a method that uses induced failures to model the probability of failure as a function of time and relate that to a novel metric that allows us to quickly determine whether or not the cost of training a model outweighs the cost of attacking it. Using this approach, we are able to approximate the expected failure rate using a small number of specially crafted samples rather than increasingly larger benchmark datasets. We demonstrate the efficacy of this technique on both the MNIST and CIFAR10 datasets using 8-, 16-, 32-, and 64-bit floating-point numbers, various data pre-processing techniques, and several attacks on five configurations of the ResNet model. Then, using empirical measurements, we examine the various trade-offs between cost, robustness, latency, and reliability to find that larger models do not significantly aid in adversarial robustness despite costing significantly more to train.

Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard DA produces synthetic data for augmentation with limited diversity. Generative Adversarial Networks (GANs) may unlock additional information in a dataset by generating synthetic samples having the appearance of real images. However, these models struggle to simultaneously address three key requirements: fidelity and high-quality samples; diversity and mode coverage; and fast sampling. Indeed, GANs generate high-quality samples rapidly, but have poor mode coverage, limiting their adoption in DA applications. We propose LatentAugment, a DA strategy that overcomes the low diversity of GANs, opening up for use in DA applications. Without external supervision, LatentAugment modifies latent vectors and moves them into latent space regions to maximise the synthetic images' diversity and fidelity. It is also agnostic to the dataset and the downstream task. A wide set of experiments shows that LatentAugment improves the generalisation of a deep model translating from MRI-to-CT beating both standard DA as well GAN-based sampling. Moreover, still in comparison with GAN-based sampling, LatentAugment synthetic samples show superior mode coverage and diversity. Code is available at: https://github.com/ltronchin/LatentAugment.

Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets--collectively denoted TuNet--utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a fivefold cross-validation on the 210 patients available in the data. 1 Introduction Kidney cancer has an annual worldwide prevalence of over 400 000 new cases, with over 175 000 deaths in 2018 [1]. The most common type of kidney cancer is renal cell carcinoma (RCC) [10].

Glioma is one of the most common types of brain tumors arising in the glial cells in the human brain and spinal cord. In addition to the threat of death, glioma treatment is also very costly. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of health care. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 90.34, 81.12, and 78.42 and Hausdorff Distances (95th percentile) of 4.32, 6.28, and 3.70 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online validation set.

Regularised canonical correlation analysis was recently extended to more than two sets of variables by the multiblock method Regularised generalised canonical correlation analysis (RGCCA). Further, Sparse GCCA (SGCCA) was proposed to address the issue of variable selection. However, for technical reasons, the variable selection offered by SGCCA was restricted to a covariance link between the blocks (i.e., with $\tau=1$). One of the main contributions of this paper is to go beyond the covariance link and to propose an extension of SGCCA for the full RGCCA model (i.e., with $\tau\in[0, 1]$). In addition, we propose an extension of SGCCA that exploits structural relationships between variables within blocks. Specifically, we propose an algorithm that allows structured and sparsity-inducing penalties to be included in the RGCCA optimisation problem. The proposed multiblock method is illustrated on a real three-block high-grade glioma data set, where the aim is to predict the location of the brain tumours, and on a simulated data set, where the aim is to illustrate the method's ability to reconstruct the true underlying weight vectors.