Access to smart meter data is essential to rapid and successful transitions to electrified grids, underpinned by flexibility delivered by low carbon technologies, such as electric vehicles (EV) and heat pumps, and powered by renewable energy. Yet little of this data is available for research and modelling purposes due consumer privacy protections. Whilst many are calling for raw datasets to be unlocked through regulatory changes, we believe this approach will take too long. Synthetic data addresses these challenges directly by overcoming privacy issues. In this paper, we present Faraday, a Variational Auto-encoder (VAE)-based model trained over 300 million smart meter data readings from an energy supplier in the UK, with information such as property type and low carbon technologies (LCTs) ownership. The model produces household-level synthetic load profiles conditioned on these labels, and we compare its outputs against actual substation readings to show how the model can be used for real-world applications by grid modellers interested in modelling energy grids of the future.

Autonomous mobile agents (e.g., UAVs and UGVs) are typically expected to incur low power/energy consumption for solving machine learning tasks (such as object recognition), as these mobile agents are usually powered by portable batteries. These requirements can be fulfilled by Spiking Neural Networks (SNNs), since their bio-inspired spike-based operations offer high accuracy and ultra low-power/energy computation. Currently, most of the SNN architectures are derived from Artificial Neural Networks whose neurons' architectures and operations are different from SNNs, or developed without considering memory budgets from the underlying processing hardware of autonomous mobile agents. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose SpikeNAS, a novel fast memory-aware neural architecture search (NAS) framework for SNNs that quickly finds an appropriate SNN architecture with high accuracy under the given memory budgets from autonomous mobile agents. To do this, our SpikeNAS employs several key steps: analyzing the impacts of network operations on the accuracy, enhancing the network architecture to improve the learning quality, and developing a fast memory-aware search algorithm. The experimental results show that our SpikeNAS improves the searching time and maintains high accuracy as compared to state-of-the-art while meeting the given memory budgets (e.g., 4.4x faster search with 1.3% accuracy improvement for CIFAR100, using an Nvidia RTX 6000 Ada GPU machine), thereby quickly providing the appropriate SNN architecture for the memory-constrained autonomous mobile agents.

Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task-finding molecules with a large dipole moment-our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring.

The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations and adversarial attacks, as well as the stability and safety of systems with neural network controllers. Therefore, estimation of tight bounds on the Lipschitz constant of neural networks is a well-studied topic. However, typical approaches involve solving a large matrix verification problem, the computational cost of which grows significantly for deeper networks. In this letter, we provide a compositional approach to estimate Lipschitz constants for deep feedforward neural networks by obtaining an exact decomposition of the large matrix verification problem into smaller sub-problems. We further obtain a closed-form solution that applies to most common neural network activation functions, which will enable rapid robustness and stability certificates for neural networks deployed in online control settings. Finally, we demonstrate through numerical experiments that our approach provides a steep reduction in computation time while yielding Lipschitz bounds that are very close to those achieved by state-of-the-art approaches.

Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique "fingerprint" of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy makes the testing of this assumption timely. In this paper, we pose the determination of molecular structures from rotational spectra as an inverse problem. Within this framework, we adopt a funnel-based approach to search for molecular twins, which are two or more molecules, which have similar rotational spectra but distinctly different molecular structures. We demonstrate that there are twins within standard levels of computational accuracy by generating rotational constants for many molecules from several large molecular databases, indicating the inverse problem is ill-posed. However, some twins can be distinguished by increasing the accuracy of the theoretical methods or by performing additional experiments.

This paper presents an interactive artwork, "Holon", a collection of 130 autonomous, cybernetic organisms that listen and make sound in collaboration with the natural environment. The work was developed for installation on water at a heritage-listed dock in Melbourne, Australia. Conceptual issues informing the work are presented, along with a detailed technical overview of the implementation. Individual holons are of three types, inspired by biological models of animal communication: composer/generators, collector/critics and disruptors. Collectively, Holon integrates and occupies elements of the acoustic spectrum in collaboration with human and non-human agents.

Launched by Elon Musk and its Optimus, we are witnessing a new race in which many companies have already engaged. The objective it to put at work a new generation of humanoid robots in demanding industrial environments within 2 or 3 years. Is this objective realistic ? The aim of this document and its main contributions is to provide some hints by covering the following topics: First an analysis of 12 companies based on eight criteria that will help us to distinguish companies based on their maturity and approach to the market; second as these humanoids are very complex systems we will provide an overview of the technological challenges to be addressed; third when humanoids are deployed at scale, Operation and Maintenance become critical and the we will explore what is new with these complex machines; Finally Pilots are the last step to test the feasibility of a new system before mass deployment. This is an important step to test the maturity of a product and the strategy of the humanoid supplier to address a market and two pragmatic approaches will be discussed.

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these data and the information that can be inferred from them are highly sensitive. Re-pseudonymization, i.e., the frequent replacement of random identifiers over time, is widely used to share smart meter data while mitigating the risk of re-identification. We here show how, in spite of re-pseudonymization, households' consumption records can be pieced together with high accuracy in large-scale datasets. We propose the first deep learning-based profiling attack against re-pseudonymized smart meter data. Our attack combines neural network embeddings, which are used to extract features from weekly consumption records and are tailored to the smart meter identification task, with a nearest neighbor classifier. We evaluate six neural networks architectures as the embedding model. Our results suggest that the Transformer and CNN-LSTM architectures vastly outperform previous methods as well as other architectures, successfully identifying the correct household 73.4% of the time among 5139 households based on electricity and gas consumption records (54.5% for electricity only). We further show that the features extracted by the embedding model maintain their effectiveness when transferred to a set of users disjoint from the one used to train the model. Finally, we extensively evaluate the robustness of our results. Taken together, our results strongly suggest that even frequent re-pseudonymization strategies can be reversed, strongly limiting their ability to prevent re-identification in practice.

Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as "neuromorphic artificial intelligence (AI)". However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.