We compared the influence of these approximations. Figure 2(b) illustrates that these approximations are not affecting the convergence rate in the sample efficiency. However, when compared to the wall-clock time (Figure 2(c)), the exact sampler without the factorisation trick is apparently slow to converge. Moreover, the provable recombination algorithm is slower than an LP solver implementation. Thus, the number of samples the provable recombination algorithm per wall time is much smaller than the LP solver. Therefore, our BASQ standard solver delivers solid empirical performance. Qualitative evaluation of posterior inference Figure 3 shows the qualitative evaluation of joint posterior inference after 200 seconds passed against the analytical true posterior. The estimated posterior shape is exactly the same as the ground truth.

Apple's new HomePod will have a display, and it might arrive later this year. This is according to Bloomberg's Mark Gurman (via 9to5Mac), who claims that the device will launch "by the end of this year," though he admits that the timing is uncertain. The new device will be a smart home speaker with an added display (a 7-inch one, previous rumors claim), that should one day become a centerpiece of Apple's smart home tech. Other features, per previous reports, includes support for Apple Intelligence, a camera, smart home controls, and a rechargeable battery. While the device will have a built-in speaker, Apple will likely position it as a smart hub instead of just a home speaker.

Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant energy and contribute to climate change. This makes sustainable data center control a priority. In this paper, we introduce SustainDC, a set of Python environments for benchmarking multiagent reinforcement learning (MARL) algorithms for data centers (DC). SustainDC supports custom DC configurations and tasks such as workload scheduling, cooling optimization, and auxiliary battery management, with multiple agents managing these operations while accounting for the effects of each other. We evaluate various MARL algorithms on SustainDC, showing their performance across diverse DC designs, locations, weather conditions, grid carbon intensity, and workload requirements. Our results highlight significant opportunities to improve data center operations using MARL algorithms. Given the increasing use of DC due to AI, SustainDC provides a crucial platform for developing and benchmarking advanced algorithms essential for achieving sustainable computing and addressing other heterogeneous real-world challenges.

In 2022-2023, AI-powered PCs made quite a splash with their automatic generation and built-in virtual assistants. Those features are cool, sure, but they're a little gimmicky at first blush. That said, amid the hype, the real standout feature emerged: battery life. Thanks to smarter resource management and power-efficient chip architecture, AI PCs became long-lasting devices that didn't need to be plugged in all the time. Let's take flying cross-country with a traditional laptop, for instance.

Every weekend warrior knows the drill -- you sit in front of a computer all week, and when the weekend hits, you bike, hike, and run yourself ragged. Your body feels destroyed on Monday. If this sounds like you -- or even if you're a casual exerciser who wants to walk and bike longer distances without getting tired -- the future has arrived. The world's first-ever outdoor exoskeleton, Hypershell X, can help max out your physical abilities with minimal effort. Hypershell X is causing a buzz among both outdoorsy types and robotics enthusiasts, and it won the Best of Innovation in Robotics award at CES 2025.

Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels). Experts' labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable, requiring careful adjustment of the degree to which any expert is trusted. We introduce the first principled approach that provides two key guarantees.

The selected locations are highlighted, while other U.S. locations are also plotted for comparison. Regions with both high CV and high average carbon intensity are identified as prime targets for DRL agents to maximize their impact on reducing carbon emissions. In the table bellow (7) is the summarizing the selected locations, typical weather values, and carbon emissions characteristics: Considering the data from (9), the U.S. states with the highest number of data centers are summarized in Table 8. The states with the most significant number of data centers tend to be Virginia, Texas, California, and New York. Virginia, especially, is a major hub due to its proximity to Washington D.C. and the abundance of fiber optic cable networks. Texas and California are also prominent due to their size, economic output, and significant tech industries. New York, particularly around New York City, hosts numerous data centers that serve the financial sector and other industries. The selection of these locations is justified by their significant number of data centers, which emphasizes the potential impact of DRL agents in these regions. By targeting areas with both high data center density and favorable carbon intensity characteristics, DRL agents can maximize their effectiveness in reducing carbon emissions.

The 3D microstructure of porous media, such as electrodes in lithium-ion batteries or fiber-based materials, significantly impacts the resulting macroscopic properties, including effective diffusivity or permeability. Consequently, quantitative structure-property relationships, which link structural descriptors of 3D microstructures such as porosity or geodesic tortuosity to effective transport properties, are crucial for further optimizing the performance of porous media. To overcome the limitations of 3D imaging, parametric stochastic 3D microstructure modeling is a powerful tool to generate many virtual but realistic structures at the cost of computer simulations. The present paper uses 90,000 virtually generated 3D microstructures of porous media derived from literature by systematically varying parameters of stochastic 3D microstructure models. Previously, this data set has been used to establish quantitative microstructure-property relationships. The present paper extends these findings by applying a hybrid AI framework to this data set. More precisely, symbolic regression, powered by deep neural networks, genetic algorithms, and graph attention networks, is used to derive precise and robust analytical equations. These equations model the relationships between structural descriptors and effective transport properties without requiring manual specification of the underlying functional relationship. By integrating AI with traditional computational methods, the hybrid AI framework not only generates predictive equations but also enhances conventional modeling approaches by capturing relationships influenced by specific microstructural features traditionally underrepresented. Thus, this paper significantly advances the predictive modeling capabilities in materials science, offering vital insights for designing and optimizing new materials with tailored transport properties.

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately 15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.