Parameterizing finger rolling and finger-object contacts in a differentiable manner is important for formulating dexterous manipulation as a trajectory optimization problem. In contrast to previous methods which often assume simplified geometries of the robot and object or do not explicitly model finger rolling, we propose a method to further extend the capabilities of dexterous manipulation by accounting for non-trivial geometries of both the robot and the object. By integrating the object's Signed Distance Field (SDF) with a sampling method, our method estimates contact and rolling-related variables and includes those in a trajectory optimization framework. This formulation naturally allows for the emergence of finger-rolling behaviors, enabling the robot to locally adjust the contact points. Our method is tested in a peg alignment task and a screwdriver turning task, where it outperforms the baselines in terms of achieving desired object configurations and avoiding dropping the object. We also successfully apply our method to a real-world screwdriver turning task, demonstrating its robustness to the sim2real gap.

Research on Life Cycle Assessment (LCA) is being conducted in various sectors, from analyzing building materials and components to comprehensive evaluations of entire structures. However, reviews of the existing literature have been unable to provide a comprehensive overview of research in this field, leaving scholars without a definitive guideline for future investigations. This paper aims to fill this gap, mapping more than twenty years of research. Using an innovative methodology that combines social network analysis and text mining, the paper examined 8024 scientific abstracts. The authors identified seven key thematic groups, building and sustainability clusters (BSCs). To assess their significance in the broader discourse on building and sustainability, the semantic brand score (SBS) indicator was applied. Additionally, building and sustainability trends were tracked, focusing on the LCA concept. The major research topics mainly relate to building materials and energy efficiency. In addition to presenting an innovative approach to reviewing extensive literature domains, the article also provides insights into emerging and underdeveloped themes, outlining crucial future research directions.

This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine Learning and Deep Learning techniques. Our methodology uses time series modeling and makes novel use of power transform normalization and zero-inflated modeling. Various Machine Learning algorithms and Conv2D Long Short-Term Memory model based Deep Learning models are applied to these transformations for precise predictions. Results underscore the effectiveness of our approach, demonstrating enhanced prediction accuracy with Air Quality Index and weather features. We achieved a 0.9691 $R^2$ Score, 0.18 MAE, 0.10 RMSE with Conv2D Long Short-Term Memory model, showcasing the power transform technique's innovation in enhancing time series forecasting for solar energy generation. Such results help our research contribute valuable insights to the synergy between Air Quality Index, weather features, and Deep Learning techniques for solar energy prediction.

Implementing AI algorithms on event-based embedded devices enables real-time processing of data, minimizes latency, and enhances power efficiency in edge computing. This research explores the deployment of a spiking recurrent neural network (SRNN) with liquid time constant neurons for gesture recognition. We focus on the energy efficiency and computational efficacy of NVIDIA Jetson Nano embedded GPU platforms. The embedded GPU showcases a 14-fold increase in power efficiency relative to a conventional GPU, making a compelling argument for its use in energy-constrained applications. The study's empirical findings also highlight that batch processing significantly boosts frame rates across various batch sizes while maintaining accuracy levels well above the baseline. These insights validate the SRNN with liquid time constant neurons as a robust model for interpreting temporal-spatial data in gesture recognition, striking a critical balance between processing speed and power frugality.

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyze the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A gramian angular summation field (GASF) based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional featurescreening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enables dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy's optimal actions. We verified RIVC's effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc.

The k-means clustering is a widely used method in data clustering and unsupervised machine learning, aiming to divide a given dataset into k distinct, non-overlapping clusters. This division seeks to minimize the within-cluster variance. The k-means clustering problem becomes NP-hard when extended beyond a single dimension [3]. Despite this complexity, there are algorithms designed to find sufficiently good solutions within a reasonable amount of time. Among these, Lloyd's algorithm, also referred to as the standard algorithm or batch k-means, is the most renowned [42]. The k-means algorithm is one of the most popular algorithms in data mining [58, 32], mainly due to its simplicity, scalability, and guaranteed termination. However, its performance is highly sensible to the initial placement of the centers [5]. In fact, there is no general approximation expectation for Lloyd's algorithm that applies to all scenarios, i.e., an arbitrary initialization may lead to an arbitrarily bad clustering. Therefore, it is crucial to employ effective initialization methods [24].

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An attempt to use an extendable robot to remove a fragment of melted fuel from a wrecked reactor at Japan's tsunami-hit Fukushima Daiichi nuclear power plant was suspended Thursday due to a technical issue. The collection of a tiny sample of the debris inside the Unit 2 reactor's primary containment vessel would start the fuel debris removal phase, the most challenging part of the decades-long decommissioning of the plant where three reactors were destroyed in the March 11, 2011, magnitude 9.0 earthquake and tsunami disaster. The work was stopped when workers noticed that five 1.5-meter (5-foot) pipes used to maneuver the robot were placed in the wrong order and could not be corrected within the time limit for their radiation exposure, the plant operator Tokyo Electric Power Company Holdings said.

The vast majority of climate policies fail to significantly reduce emissions and so make little difference to stopping climate change, suggesting that governments must work much harder to identify ways to actually shift the needle. Nicolas Koch at the Mercator Research Institute on Global Commons and Climate Change in Berlin and his colleagues discovered this by assessing the impact of 1500 climate policies put into force between 1998 and 2022, covering 41 countries across six continents. They began by using machine learning to identify moments in which a country's emissions dropped significantly, relative to a control group of other nations not included in the analysis. The researchers found 69 of these emissions "breaks" and compared them with a database compiled by the Organisation for Economic Co-operation and Development (OECD) that tracks what types of climate policies were enacted when. While matching policy shifts to emission changes isn't an exact science, the team was able to attribute 63 of these breaks to one or more policy interventions within a two-year interval around the break, in order to allow for lagged or anticipated effects.

Google's new superphone goes all out on battery, camera and smarts, leading a new line of Android devices that can run the company's Gemini AI system with a next-generation conversational voice assistant that is a huge leap forward. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. The Pixel 9 Pro XL is the biggest normal phone Google makes, costing from 1,099 ( 1,199/ 1,099/A 1,849) and is joined for the first time this year by a smaller 9 Pro model with the same specs and camera costing 999 ( 1,099/ 999/A 1,699). The XL is therefore for people who want a huge screen and big battery.