We thank all four reviewers' time, effort, and valuable suggestions. Is the applied sampling strategy the best? Results of different configurations when prune ResNet-32 on CIFAR-10 with one V100 GPU. "#SC" indicates the number of selected channels. Does different channel-wise interpolation (CWI) affect the perfor-17 CWI is a general operation to align feature maps with different sizes.

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics, and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review delves into how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been harnessed to tackle CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

Mathematical algorithmic modelling may seem a rarefied academic pursuit, but scientists at CWI pursuing life sciences and health (LSH) research are committed to designing models with direct applications to challenges in healthcare. From optimizing treatment plans for cancer patients and reducing waiting times for ambulances to improving geriatric care and suicide prevention, their work, says senior researcher Peter Bosman, 'is not window dressing. Our algorithms are actually being used. We go from foundation to application.' The Centrum Wiskunde & Informatica (CWI, the Dutch national research institute for mathematics and computer science) is keen to ensure that the real-life value of its work is more widely understood.

Recent advances in Artificial Intelligence (AI) will create more energy efficient devices that incorporate speech recognition, gesture recognition as well as electrocardiogram (ECG) classifications. As a result, more elaborate AI will be embedded in chips for local devices such as smartphones and smartwatches as opposed to the cloud. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands, working with IMEC/Holst Research Centre from Eindhoven, announced they have achieved a mathematical breakthrough that would leverage AI technology to increase energy efficiency on local devices. This will make the applications running on these devices more robust and even more secure. The results have been published in a research paper (by Bojian Yin, Federico Corradi, and Sander M. Bohté) of the International Conference on Neuromorphic Systems.

Epistemic logic programs (ELPs) are a popular generalization of standard Answer Set Programming (ASP) providing means for reasoning over answer sets within the language. This richer formalism comes at the price of higher computational complexity reaching up to the fourth level of the polynomial hierarchy. However, in contrast to standard ASP, dedicated investigations towards tractability have not been undertaken yet. In this paper, we give first results in this direction and show that central ELP problems can be solved in linear time for ELPs exhibiting structural properties in terms of bounded treewidth. We also provide a full dynamic programming algorithm that adheres to these bounds. Finally, we show that applying treewidth to a novel dependency structure---given in terms of epistemic literals---allows to bound the number of ASP solver calls in typical ELP solving procedures.

Amsterdam Data Science, in which CWI participates as a partner, recently announced that 3 PhD projects will shortly commence, fully funded by Science Foundation Ireland (an award of around 0.5M€). The PhD students will spend their studentships between Insight Centre for Data Analytics (Ireland) and up to 1 year at an Amsterdam Data Science partner. This marks a further step in this already strong collaboration and an excellent opportunity for PhD students to benefit from the joint expertise. The vacancies will be advertised very soon. The awarded project in which CWI will be involved is called "Smart Partnerships" to develop STEM competencies that transform lives to live and thrive in a complex connected global society.

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