In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from the training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke-Schumann model, the chemical equilibrium model and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed $n$-heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes.

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers -- long short-term memory and gated recurrent unit -- within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.

This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 256 pixels for the Sentinel-2 L2A, Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications.

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data. Like other ML problems, VAEs require hyperparameter tuning, e.g., balancing the Kullback Leibler (KL) and reconstruction terms. However, the training process and resulting manifold topology and connectivity depend not only on hyperparameters, but also their evolution during training. Because of the inefficiency of exhaustive search in a high-dimensional hyperparameter space for the expensive to train models, here we explored a latent Bayesian optimization (zBO) approach for the hyperparameter trajectory optimization for the unsupervised and semi-supervised ML and demonstrate for joint-VAE with rotational invariances. We demonstrate an application of this method for finding joint discrete and continuous rotationally invariant representations for MNIST and experimental data of a plasmonic nanoparticles material system. The performance of the proposed approach has been discussed extensively, where it allows for any high dimensional hyperparameter tuning or trajectory optimization of other ML models.

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. Recent studies have proposed many novel NILM frameworks based on federated deep learning (FL). However, there lacks comprehensive research exploring the utility optimization schemes and the privacy-preserving schemes in different FL-based NILM application scenarios. In this paper, we make the first attempt to conduct FL-based NILM focusing on both the utility optimization and the privacy-preserving by developing a distributed and privacy-preserving NILM (DP2-NILM) framework and carrying out comparative experiments on practical NILM scenarios based on real-world smart meter datasets. Specifically, two alternative federated learning strategies are examined in the utility optimization schemes, i.e., the FedAvg and the FedProx. Moreover, different levels of privacy guarantees, i.e., the local differential privacy federated learning and the global differential privacy federated learning are provided in the DP2-NILM. Extensive comparison experiments are conducted on three real-world datasets to evaluate the proposed framework.

Artificial intelligence will be the common theme in the top 10 technology trends in the next few years, and these are expected to quicken breakthroughs across key economic sectors and society, the Alibaba Damo Academy says. The global research arm of Chinese technology major Alibaba Group says innovation will be extended from the physical world to a mixed reality, as more innovation finds its way to industrial applications and digital technology drives a green and sustainable future. "Digital technologies are growing faster than ever," Jeff Zhang, president of Alibaba Cloud Intelligence and head of Alibaba Damo, said in a report released on Monday. "The advancements in digitisation, 'internetisation' and intelligence are redefining a digital world that is characterised by the prevalence of mixed reality. "Digital technology plays an important role in powering a green and sustainable future, whether it is applied in industries such as green data centres and energy-efficient manufacturing, or in day-to-day activities like paperless office."

There are several motivations for Shell to revolutionise their company via the use of AI and data. The oil and gas business is at a crossroads because to rising energy needs, disconnected environments, and pressure to combat climate change. Shell and other energy firms have a choice between maintaining the current quo and embracing a low-carbon energy future. End-to-end processes must be optimised and kept up at scale as we move toward a more dispersed, diversified, and decentralised energy system. Therefore, it is essential to find solutions that can be quickly and universally implemented.

As the world fights climate change, will the increasingly widespread use of artificial intelligence (AI) be a help or a hindrance? In a paper published this week in Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions, and suggest ways to better align AI with climate change goals. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," said David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila – Quebec AI Institute, who co-authored the paper. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." The paper divides the impacts of AI on greenhouse gas emissions into three categories: 1) Impacts from the computational energy and hardware used to develop, train, and run AI algorithms, 2) immediate impacts caused by the applications of AI - such as optimizing energy use in buildings (which decreases emissions) or accelerating fossil fuel exploration (which increases emissions), and 3) system-level impacts caused by the ways in which AI applications affect behaviour patterns and society more broadly, such as via advertising systems and self-driving cars.

The growth of smart technology is one of the most beneficial trends brought on by advances in AI. It is projected that there will be over 77 million smart homes in the United States by 2025. Smart technology is also being used by businesses and government institutions around the world. Many factors are driving the demand for smart technology. The quest for efficient and sustainable energy usage is one of the defining technological challenges of the modern age -- especially as we find ourselves in the throes of the world's first truly global energy crisis.

"AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," said David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila -- Quebec AI Institute, who co-authored the paper. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." The paper divides the impacts of AI on greenhouse gas emissions into three categories: 1) Impacts from the computational energy and hardware used to develop, train, and run AI algorithms, 2) immediate impacts caused by the applications of AI -- such as optimizing energy use in buildings (which decreases emissions) or accelerating fossil fuel exploration (which increases emissions), and 3) system-level impacts caused by the ways in which AI applications affect behaviour patterns and society more broadly, such as via advertising systems and self-driving cars.