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NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific objectives of NOTT include characterizing (i) young planetary systems near the snow line, a critical region for giant planet formation, and (ii) nearby main-sequence stars close to the habitable zone, with a focus on detecting exozodiacal dust that could obscure Earth-like planets. In 2023-2024, the final warm optics have been procured and assembled in a new laboratory at KU Leuven. First fringes and null measurements were obtained using a Gallium Lanthanum Sulfide (GLS) photonic chip that was also tested at cryogenic temperatures. In this paper, we present an overall update of the NOTT project with a particular focus on the cold mechanical design, the first results in the laboratory with the final NOTT warm optics, and the ongoing Asgard integration activities. We also report on other ongoing activities such as the characterization of the photonic chip (GLS, LiNbO3, SiO), the development of the exoplanet science case, the design of the dispersion control module, and the progress with the self-calibration data reduction software., Comment: 11 pages (incl. 5 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging VI

The reactivated Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE-R) serendipitously caught the Type II supernova SN 2023ixf in Messier 101 on the rise, starting day 3.6 through day 10.9, and on the late-time decline from days 211 through 213 and days 370 through 372. We have considered these mid-infrared (mid-IR) data together with observations from the ultraviolet (UV) through the near-IR, when possible. At day 3.6 we approximated the optical emission with a hot, ~26,630 K blackbody, with a notable UV excess likely from strong SN shock interaction with circumstellar matter (CSM). In the IR, however, a clear excess is also obvious, and we fit it with a cooler, ~1,620 K blackbody with radius of ~2.6 x 10^{15} cm, consistent with dust in the progenitor's circumstellar shell likely heated by the UV emission from the CSM interaction. On day 10.8, the light detected was consistent with SN ejecta-dominated emission. At late times we also observed a clear NEOWISE-R excess, which could arise either from newly formed dust in the inner ejecta or in the contact discontinuity between the forward and reverse shocks, or from more distant pre-existing dust grains in the SN environment. Furthermore, the large 4.6 micron excess at late times can also be explained by the emergence of the carbon monoxide 1--0 vibrational band. SN 2023ixf is the best-observed SN IIP in the mid-IR during the first several days after explosion and one of the most luminous such SNe ever seen., Comment: 22 pages, 8 figures, submitted to AAS Journals

We explore large-scale cosmic structure using the spatial distribution of 542 gamma-ray bursts (GRBs) having accurately measured positions and spectroscopic redshifts. Prominent cosmological clusters are identified in both the northern and southern galactic hemispheres (avoiding extinction effects in the plane of the Milky Way) using the Bootstrap Point-Radius method. The Northern Galactic hemisphere contains a significant group of four GRBs in the redshift range 0.59 < z < 0.62 (with a Bootstrap probability of p = 0.012) along with the previously-identified Hercules-Corona Borealis GreatWall (in the revised redshift range 0.9 < z < 2.1, p = 0.017). The Southern Galactic hemisphere contains the previously-identified Giant GRB Ring (p = 0.022) along with another possible cluster of 7 - 9 GRBs at 1.179 < z < 1.444 (p = 0.031). Additionally, both the Hercules-Corona Borealis Great Wall and the Giant GRB Ring have become more prominent as the GRB sample size has grown. The approach used here underscores the potential value of GRB clustering as a probe of large-scale cosmic structure, complementary to galaxy and quasar clustering. Because of the vast scale on which GRB clustering provides valuable insights, it is important that optical GRB monitoring continue so that additional spectroscopic redshift measurements should be obtained., Comment: 11 pages, 15 figures, submitted to MNRAS

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters will prove a vital component to facilitate these forecasting tasks. However, smart meter adoption is low among privacy-conscious consumers that fear intrusion upon their fine-grained consumption data. In this work we propose and explore a federated learning (FL) based approach for training forecasting models in a distributed, collaborative manner whilst retaining the privacy of the underlying data. We compare two approaches: FL, and a clustered variant, FL+HC against a non-private, centralised learning approach and a fully private, localised learning approach. Within these approaches, we measure model performance using RMSE and computational efficiency. In addition, we suggest the FL strategies are followed by a personalisation step and show that model performance can be improved by doing so. We show that FL+HC followed by personalisation can achieve a $\sim$5\% improvement in model performance with a $\sim$10x reduction in computation compared to localised learning. Finally we provide advice on private aggregation of predictions for building a private end-to-end load forecasting application.

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible whilst retaining the privacy of consumers' raw energy consumption data., Comment: Accpeted at the Tackling Climate Change with Machine Learning workshop at NeurIPS 2020

The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud. However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer. We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on IoT applications., Comment: Abstract accepted for publication in a book titled: "Federated Learning Systems: Towards Next Generation AI" in Springer's Series on Studides in Computational Intelligence

Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion -- as is typical in real world situations -- the joint model produced by FL suffers in terms of test set accuracy and/or communication costs compared to training on iid data. We show that learning a single joint model is often not optimal in the presence of certain types of non-iid data. In this work we present a modification to FL by introducing a hierarchical clustering step (FL+HC) to separate clusters of clients by the similarity of their local updates to the global joint model. Once separated, the clusters are trained independently and in parallel on specialised models. We present a robust empirical analysis of the hyperparameters for FL+HC for several iid and non-iid settings. We show how FL+HC allows model training to converge in fewer communication rounds (significantly so under some non-iid settings) compared to FL without clustering. Additionally, FL+HC allows for a greater percentage of clients to reach a target accuracy compared to standard FL. Finally we make suggestions for good default hyperparameters to promote superior performing specialised models without modifying the the underlying federated learning communication protocol., Comment: Accepted to 2020 International Joint Conference on Neural Networks (IJCNN)

Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved, such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases., Comment: 12 pages

Structure entails function and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage, to their dynamics and even their evolution. Advances in the analysis of complex networks provide useful new approaches to understanding structural and functional properties of brain networks. Structural properties of networks recently described allow their characterization as small-world, random (exponential) and scale-free. They complement the set of other properties that have been explored in the context of brain connectivity, such as topology, hodology, clustering, and hierarchical organization. Here we apply new network analysis methods to cortical inter-areal connectivity networks for the cat and macaque brains. We compare these corticocortical fibre networks to benchmark rewired, small-world, scale-free and random networks, using two analysis strategies, in which we measure the effects of the removal of nodes and connections on the structural properties of the cortical networks. The brain networks' structural decay is in most respects similar to that of scale-free networks. The results implicate highly connected hub-nodes and bottleneck connections as structural basis for some of the conditional robustness of brain systems. This informs the understanding of the development of brain networks' connectivity., Comment: submitted to European Journal of Neuroscience (under review)