Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End users highly value their privacy and control, and want to be included in the service design and decision-making process when creating the daily appliance operation schedules. Furthermore, unless they are financially or environmentally motivated, they are generally not prepared to sacrifice their comfort to help balance the power system. In this paper, we present an inverse-reinforcement-learning-based model that helps create the end users' daily appliance schedules without asking them to explicitly state their needs and wishes. By using their past consumption data, the end consumers will implicitly participate in the creation of those decisions and will thus be motivated to continue participating in the provision of demand response services.

Smart City applications, such as traffic monitoring and disaster response, often use swarms of intelligent and cooperative drones to efficiently collect sensor data over different areas of interest and time spans. However, when the required sensing becomes spatio-temporally large and varying, a collective arrangement of sensing tasks to a large number of battery-restricted and distributed drones is challenging. To address this problem, this paper introduces a scalable and energy-aware model for planning and coordination of spatio-temporal sensing. The coordination model is built upon a decentralized multi-agent collective learning algorithm (EPOS) to ensure scalability, resilience, and flexibility that existing approaches lack of. Experimental results illustrate the outstanding performance of the proposed method compared to state-of-the-art methods. Analytical results contribute a deeper understanding of how coordinated mobility of drones influences sensing performance. This novel coordination solution is applied to traffic monitoring using real-world data to demonstrate a $46.45\%$ more accurate and $2.88\%$ more efficient detection of vehicles as the number of drones become a scarce resource.

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.

Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or simplifying assumptions. Utilizing insights from the development of scalable Bayesian posterior samplers, we present a complete recipe for formulating forward processes in SGMs, ensuring convergence to the desired target distribution. Our approach reveals that several existing SGMs can be seen as specific manifestations of our framework. Building upon this method, we introduce Phase Space Langevin Diffusion (PSLD), which relies on score-based modeling within an augmented space enriched by auxiliary variables akin to physical phase space. Empirical results exhibit the superior sample quality and improved speed-quality trade-off of PSLD compared to various competing approaches on established image synthesis benchmarks. Remarkably, PSLD achieves sample quality akin to state-of-the-art SGMs (FID: 2.10 for unconditional CIFAR-10 generation). Lastly, we demonstrate the applicability of PSLD in conditional synthesis using pre-trained score networks, offering an appealing alternative as an SGM backbone for future advancements. Code and model checkpoints can be accessed at \url{https://github.com/mandt-lab/PSLD}.

While considerable effort has been dedicated to identifying the most effective approaches, with the widely recognized M forecasting competition, initiated in 1982 (Makridakis et al., 1982), serving as a arena for these breakthroughs, quantifying the uncertainty of such predictions has received diminished attention. Indeed, the inclusion of prediction intervals (PIs) began to be considered in forecasting competitions only from the M4 competition (Makridakis et al., 2020) onward. Another example of this discrepancy was observed in the M5 forecasting competitions. The M5 "Accuracy" competition (Makridakis et al., 2022a), which centered on optimizing point predictions, garnered significant interest and participation, with a staggering 7, 092 participants 2 vying for top honors. In stark contrast, the M5 "Uncertainty" competition (Makridakis et al., 2022b), which aimed to assess the quality of estimated conditional quantiles, drew considerably less attention, involving only 1, 137 participants despite offering the same prize incentives.

Fatigue crack growth is one of the most common types of deterioration in metal structures with significant implications on their reliability. Recent advances in Structural Health Monitoring (SHM) have motivated the use of structural response data to predict future crack growth under uncertainty, in order to enable a transition towards predictive maintenance. Accurately representing different sources of uncertainty in stochastic crack growth (SCG) processes is a non-trivial task. The present work builds on previous research on physics-based SCG modeling under both material and load-related uncertainty. The aim here is to construct computationally efficient, probabilistic surrogate models for SCG processes that successfully encode these different sources of uncertainty. An approach inspired by latent variable modeling is employed that utilizes Gaussian Process (GP) regression models to enable the surrogates to be used to generate prior distributions for different Bayesian SHM tasks as the application of interest. Implementation is carried out in a numerical setting and model performance is assessed for two fundamental crack SHM problems; namely crack length monitoring (damage quantification) and crack growth monitoring (damage prognosis).

Amazon's Prime Day sale for October 2023 is in full swing, but Walmart got the jump on its fellow retailer with a sale of its own that it launched yesterday. The Holiday Kickoff sale is like a Walmart Prime Day sale, which we've seen other storefronts host in the past. It's a way for the company to take advantage of the shopping buzz generated by Amazon -- and a way for you to save at more than one outlet. We combed through what Walmart had to offer and found the tech savings that are worth your time. One thing to note is that Walmart carries some devices that Amazon doesn't, like the Google Nest Hub, which is down to $60.

Amid the many debates about the potential dangers of artificial intelligence, some researchers argue that an important concern is being overlooked: the energy used by computers to train and run large AI models. Alex de Vries at the VU Amsterdam School of Business and Economics warns that AI's growth is poised to make it a significant contributor to global carbon emissions. He estimates that if Google switched its whole search business to AI, it would end up using 29.3 terawatt hours per year – equivalent to the electricity consumption of Ireland, and almost double the company's total energy consumption of 15.4 terawatt hours in 2020. On one hand, there is good reason not to panic. Making that sort of switch is practically impossible, as it would require more than 4 million powerful computer chips known as graphics processing units (GPUs) that are currently in huge demand, with limited supply.

Big ticket items may get more attention, but Amazon's October Prime Day sale is a good time to stock up on the smaller accessories and items you may also need. Plenty of less-expensive gadgets are on sale right now, including smart speakers, iPhone accessories, chargers, smart home devices and battery packs -- many of which make great stocking stuffers and nice gifts for the hard-to-shop-for. We've rounded up the tech items we've tested, tried and know to be a good deal. Here are the best Amazon Big Deal Days items under $50. Amazon's Echo Dot combines the typical utility of Alexa with surprisingly decent sound.

It's been two years since Google first debuted Project Green Light, a novel means of addressing the street-level pollution caused by vehicles idling at stop lights. At its Sustainability '23 event on Tuesday, the company discussed some of the early findings from that program and announced another wave of expansions for it. Green Light uses machine learning systems to comb through Maps data to calculate the amount of traffic congestion present at a given light, as well as the average wait times of vehicles stopped there. That information is then used to train AI models that can autonomously optimize the traffic timing at that intersection, reducing idle times as well as the amount of braking and accelerating vehicles have to do there. It's all part of Google's goal to help its partners collectively reduce their carbon emissions by a gigaton by 2030.