Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, millions of smart meters have been deployed worldwide in the last decade, recording the main electricity power consumed in individual households. This data produces valuable information that can help them reduce their electricity footprint; nevertheless, the collected signal aggregates the consumption of the different appliances running simultaneously in the house, making it difficult to apprehend. Non-Intrusive Load Monitoring (NILM) refers to the challenge of estimating the power consumption, pattern, or on/off state activation of individual appliances using the main smart meter signal. Recent methods proposed to tackle this task are based on a fully supervised deep-learning approach that requires both the aggregate signal and the ground truth of individual appliance power. However, such labels are expensive to collect and extremely scarce in practice, as they require conducting intrusive surveys in households to monitor each appliance. In this paper, we introduce CamAL, a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained. CamAL merges an ensemble of deep-learning classifiers combined with an explainable classification method to be able to localize appliance patterns. Our experimental evaluation, conducted on 4 real-world datasets, demonstrates that CamAL significantly outperforms existing weakly supervised baselines and that current SotA fully supervised NILM approaches require significantly more labels to reach CamAL performances. The source of our experiments is available at: https://github.com/adrienpetralia/CamAL. This paper appeared in ICDE 2025.

Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients energy monitoring solutions to provide feedback on the consumption of their individual appliances. Historically, such estimates have relied on statistical methods that use coarse-grained total monthly consumption and static customer data, such as appliance ownership. Non-Intrusive Load Monitoring (NILM) is the problem of disaggregating a household's collected total power consumption to retrieve the consumed power for individual appliances. Current state-of-the-art (SotA) solutions for NILM are based on deep-learning (DL) and operate on subsequences of an entire household consumption reading. However, the non-stationary nature of real-world smart meter data leads to a drift in the data distribution within each segmented window, which significantly affects model performance. This paper introduces NILMFormer, a Transformer-based architecture that incorporates a new subsequence stationarization/de-stationarization scheme to mitigate the distribution drift and that uses a novel positional encoding that relies only on the subsequence's timestamp information. Experiments with 4 real-world datasets show that NILMFormer significantly outperforms the SotA approaches. Our solution has been deployed as the backbone algorithm for EDF's (Electricité De France) consumption monitoring service, delivering detailed insights to millions of customers about their individual appliances' power consumption. This paper appeared in KDD 2025.

Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because computing technologies play such an important role in shaping modern societies, it is worth exploring how scenarios are being used as an anticipatory tool in the field -- and what possible traditional uses of scenarios are not yet covered but have the potential to enrich the field. We address this gap by conducting a systematic literature review on the use of scenario building methods in computer science over the last decade (n = 59). We guide the review along two main questions. First, we aim to uncover how scenarios are used in computing literature, focusing especially on the rationale for why scenarios are used. Second, in following the potential of scenario building to enhance inclusivity in research, we dive deeper into the participatory element of the existing scenario building literature in computer science.

While Transformers excel in language and vision-where inputs are semantically rich and exhibit univariate dependency structures-their architectural complexity leads to diminishing returns in time series forecasting. Time series data is characterized by low per-timestep information density and complex dependencies across channels and covariates, requiring conditioning on structured variable interactions. To address this mismatch and overparameterization, we propose FaCTR, a lightweight spatiotemporal Transformer with an explicitly structural design. FaCTR injects dynamic, symmetric cross-channel interactions-modeled via a low-rank Factorization Machine into temporally contextualized patch embeddings through a learnable gating mechanism. It further encodes static and dynamic covariates for multivariate conditioning. Despite its compact design, FaCTR achieves state-of-the-art performance on eleven public forecasting benchmarks spanning both short-term and long-term horizons, with its largest variant using close to only 400K parameters-on average 50x smaller than competitive spatiotemporal transformer baselines. In addition, its structured design enables interpretability through cross-channel influence scores-an essential requirement for real-world decision-making. Finally, FaCTR supports self-supervised pretraining, positioning it as a compact yet versatile foundation for downstream time series tasks.

A team of researchers at Rice University and Baylor College of Medicine has developed a new strategy for identifying hazardous pollutants in soil, even ones that have never been isolated or studied in a lab. The new approach, described in a study published in Proceedings of the National Academy of Sciences, uses light-based imaging, theoretical predictions of compounds' light signatures and machine learning (ML) algorithms to detect toxic compounds like polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs) in soil. A common by-product of combustion, PAHs and PACs have been linked to cancer, developmental issues and other serious health problems. Identifying pollutants in soil usually requires advanced laboratories and standard physical reference samples of the suspected contaminants. However, for many environmental pollutants that pose a public health risk, there is no experimental data available that can be used to detect them.

"Just because of this shorter distance, we will put down signals that will be approximately a hundred times stronger than the GPS signal," says Tyler Reid, chief technology officer and cofounder of Xona. "That means the reach of jammers will be much smaller against our system, but we will also be able to reach deeper into indoor locations, penetrating through multiple walls." The first GPS system went live in 1993. In the decades since, it has become one of the foundational technologies that the world depends on. The precise positioning, navigation, and timing (PNT) signals beamed by its satellites underpin much more than Google Maps in your phone. They guide drill heads at offshore oil rigs, time-stamp financial transactions, and help sync power grids all over the world.

The United Nations' digital agency says that operational carbon emissions for the world's top tech companies rose an average of 150 percent between 2020 and 2023 as investments in artificial intelligence (AI) and data centres drove up global electricity demand. Operational emissions for Amazon grew 182 percent in 2023 against 2020 levels, while emissions for Microsoft grew 155 percent, Facebook and Instagram owner Meta grew 145 percent, and Google parent company Alphabet grew 138 percent over the same period, according to the UN's International Telecommunication Union (ITU). The figures include the emissions directly created by the companies' operations as well as those from purchased energy consumption. They were included in a new report from ITU assessing the greenhouse gas emissions of the world's top 200 digital companies between 2020 and 2023. The UN agency linked the sharp uptick to recent breakthroughs in AI and the demand for digital services like cloud computing.

Chronoamperometry (CA) is a fundamental electrochemical technique used for quantifying redox-active species. However, in room-temperature ionic liquids (RTILs), the high viscosity and slow mass transport often lead to extended measurement durations. This paper presents a novel mathematical regression approach that reduces CA measurement windows to under 1 second, significantly faster than previously reported methods, which typically require 1-4 seconds or longer. By applying an inference algorithm to the initial transient current response, this method accurately predicts steady-state electrochemical parameters without requiring additional hardware modifications. The approach is validated through comparison with standard chronoamperometric techniques and is demonstrated to maintain reasonable accuracy while dramatically reducing data acquisition time. The implications of this technique are explored in analytical chemistry, sensor technology, and battery science, where rapid electrochemical quantification is critical. Our technique is focused on enabling faster multiplexing of chronoamperometric measurements for rapid olfactory and electrochemical analysis.

This paper introduces Energentic Intelligence, a class of autonomous systems defined not by task performance, but by their capacity to sustain themselves through internal energy regulation. Departing from conventional reward-driven paradigms, these agents treat survival-maintaining functional operation under fluctuating energetic and thermal conditions-as the central objective. We formalize this principle through an energy-based utility function and a viability-constrained survival horizon, and propose a modular architecture that integrates energy harvesting, thermal regulation, and adaptive computation into a closed-loop control system. A simulated environment demonstrates the emergence of stable, resource-aware behavior without external supervision. Together, these contributions provide a theoretical and architectural foundation for deploying autonomous agents in resource-volatile settings where persistence must be self-regulated and infrastructure cannot be assumed.

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. A feature selection process is done for each consumer type including temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for Short-Term Load Forecasting (STLF) and Very Short-Term Load Forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, thereby supporting grid stability.