We consider the case where each message fails with probability 1 p and each agent i uses the messages it receives from its neighbors with probability pi.This is equivalent to each agent ireceiving messages from its neighbors with probability pip.Let 1{(i,j) 2 Et}be the indicator random variable that takes value 1 if agent i receives reward value and arm id from agent j at time t and 0 otherwise. We start by proving some useful lemmas. Lemma 1. (Restatement of results from [3]) Let k = Thus we have P Ai(t+1) = k,Nik(t) > k P bµi1(t) µ1 Ci1(t) +P bµik(t) µk +Cik(t) This concludes the proof of Lemma 1. Lemma 2. Let (G) is the clique covering number of graph G. Let k = Let C be a non overlapping clique covering of G. Then we have that k |C| < Nik( ik,C) k. From regret results it follows that regret for this case is greater than the regret for the case where ik,C < k,C for some (or all) i. 13 We analyse the expected number of times agents pull suboptimal arm k as follows, X P bµi1(t) µ1 Ci1(t) +P bµik(t) µk +Cik(t), (29) where (a) follows from the fact that clique covering is non overlapping. This concludes the proof of Lemma 2. Lemma 3. Let di(G) be the degree of agent i in graph G.

Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruction. In this work, we introduce the concept of parallel imaging-inspired self-consistency (PISCO), which we incorporate as novel self-supervised k-space regularization enforcing a consistent neighborhood relationship. At no additional data cost, the proposed regularization significantly improves neural implicit k-space reconstructions on simulated data. Abdominal in-vivo reconstructions using PISCO result in enhanced spatio-temporal image quality compared to state-of-the-art methods.

In this work, we propose a novel image reconstruction framework that directly learns a neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic Resonance Imaging (CMR). While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation. We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point. We then learn the subject-specific mapping from these unique coordinates to k-space intensities using a multi-layer perceptron with frequency domain regularization. During inference, we obtain a complete k-space for Cartesian coordinates and an arbitrary temporal resolution. A simple inverse Fourier transform recovers the image, eliminating the need for density compensation and costly non-uniform Fourier transforms for non-Cartesian data. This novel imaging framework was tested on 42 radially sampled datasets from 6 subjects. The proposed method outperforms other techniques qualitatively and quantitatively using data from four and one heartbeat(s) and 30 cardiac phases. Our results for one heartbeat reconstruction of 50 cardiac phases show improved artifact removal and spatio-temporal resolution, leveraging the potential for real-time CMR.

All industries are moving closer to AI, but at varying rates. Nik Dawson has won a prestigious OECD Future of Work Fellowship to continue his research examining the impacts of Artificial Intelligence (AI) on Australia's labour market. A PhD candidate in the UTS The Magic Lab/Centre for Artificial Intelligence, Nik is considering how the rate of AI adoption will determine the impacts of AI on labour markets. All industries are moving closer to AI, but at varying rates. As AI adoption accelerates, labour tasks are more likely to be automated or augmented.