The point clouds collected by the Airborne Laser Scanning (ALS) system provide accurate 3D information of urban land covers. By utilizing multi-temporal ALS point clouds, semantic changes in urban area can be captured, demonstrating significant potential in urban planning, emergency management, and infrastructure maintenance. Existing 3D change detection methods struggle to efficiently extract multi-class semantic information and change features, still facing the following challenges: (1) the difficulty of accurately modeling cross-temporal point clouds spatial relationships for effective change feature extraction; (2) class imbalance of change samples which hinders distinguishability of semantic features; (3) the lack of real-world datasets for 3D semantic change detection. To resolve these challenges, we propose the Multi-task Enhanced Cross-temporal Point Transformer (ME-CPT) network. ME-CPT establishes spatiotemporal correspondences between point cloud across different epochs and employs attention mechanisms to jointly extract semantic change features, facilitating information exchange and change comparison. Additionally, we incorporate a semantic segmentation task and through the multi-task training strategy, further enhance the distinguishability of semantic features, reducing the impact of class imbalance in change types. Moreover, we release a 22.5 $km^2$ 3D semantic change detection dataset, offering diverse scenes for comprehensive evaluation. Experiments on multiple datasets show that the proposed MT-CPT achieves superior performance compared to existing state-of-the-art methods. The source code and dataset will be released upon acceptance at \url{https://github.com/zhangluqi0209/ME-CPT}.

Kelp forests, as foundation species, are vital to marine ecosystems, providing essential food and habitat for numerous organisms. This study explores the integration of crowdsourced labels with advanced artificial intelligence models to develop a fast and accurate kelp canopy detection pipeline using Landsat images. Building on the success of a machine learning competition, where this approach ranked third and performed consistently well on both local validation and public and private leaderboards, the research highlights the effectiveness of combining Mixed Vision Transformers (MIT) with ConvNeXt models. Training these models on various image sizes significantly enhanced the accuracy of the ensemble results. U-Net emerged as the best segmentation architecture, with UpperNet also contributing to the final ensemble. Key Landsat bands, such as ShortWave InfraRed (SWIR1) and Near-InfraRed (NIR), were crucial while altitude data was used in postprocessing to eliminate false positives on land. The methodology achieved a high detection rate, accurately identifying about three out of four pixels containing kelp canopy while keeping false positives low. Despite the medium resolution of Landsat satellites, their extensive historical coverage makes them effective for studying kelp forests. This work also underscores the potential of combining machine learning models with crowdsourced data for effective and scalable environmental monitoring. All running code for training all models and inference can be found at https://github.com/IoannisNasios/Kelp_Forests.

Given that disturbances to the stable and normal operation of power systems have grown phenomenally, particularly in terms of unauthorized access to confidential and critical data, injection of malicious software, and exploitation of security vulnerabilities in a poorly patched software among others; then developing, as a countermeasure, an assessment solutions with machine learning capabilities to match up in real-time, with the growth and fast pace of these cyber-attacks, is not only critical to the security, reliability and safe operation of power system, but also germane to guaranteeing advanced monitoring and efficient threat detection. Using the Mississippi State University and Oak Ridge National Laboratory dataset, the study used an XGB Classifier modeling approach in machine learning to diagnose and assess power system disturbances, in terms of Attack Events, Natural Events and No-Events. As test results show, the model, in all the three sub-datasets, generally demonstrates good performance on all metrics, as it relates to accurately identifying and classifying all the three power system events.

Elon Musk is clashing with OpenAI CEO Sam Altman over the Stargate artificial intelligence (AI) infrastructure project touted by President Donald Trump, the latest in a feud between the two tech billionaires that started on OpenAI's board and is now testing Musk's influence with the new president. Trump on Tuesday had talked up a joint venture investing up to 500bn through a new partnership formed by OpenAI, the maker of ChatGPT, alongside Oracle and SoftBank. The new entity, Stargate, is already starting to build out data centres and the electricity generation needed for the further development of fast-evolving AI technology. Trump declared it "a resounding declaration of confidence in America's potential" under his new administration, with an initial private investment of 100bn that could reach five times that sum. But Musk, a close Trump adviser who helped bankroll his campaign and now leads a government cost-cutting initiative, questioned the value of the investment hours later.

Late yesterday afternoon, the president of the United States transformed, very briefly, into the comms guy for a new tech company. At a press conference capping his first full day back in the White House, Donald Trump stood beside three of the most influential executives in the world--Sam Altman of OpenAI, Larry Ellison of Oracle, and Masayoshi Son of SoftBank--and announced the Stargate Project, "the largest AI infrastructure project, by far, in history." Although Trump's rhetoric may seem to suggest otherwise, Stargate is not a new federal program but rather a private venture uniting these three companies with other leaders in the AI race, such as Microsoft and Nvidia. The new company--for which Son will serve as chairman and OpenAI will be in charge of operations--will spend a planned 500 billion over the next four years to build data centers, power plants, and other such digital infrastructure in the United States, all in hopes of developing ever more advanced AI models. Trump presented Stargate as a victory for his "America First" agenda, saying that it may "lead to something that could be the biggest of all"--an apparent reference to superintelligent machines.

Some of the biggest names in tech came together this week to announce "Stargate," a project they say will receive 500 billion in investment for US-based artificial intelligence infrastructure. The joint venture, spearheaded by OpenAI, Oracle, and SoftBank, aims to rapidly build out colossal new data centers crucial to future AI development. It will also prop-up new electricity plants needed to power these notoriously energy-intensive AI models. Stargate already has the blessing of newly-inaugurated president Donald Trump who this week said he has plans to "unleash" the US fossil fuel industry. Looser regulations on oil and gas extraction will make fossil fuels the obvious, cheapest choice to power Stargate's ambitious AI agenda.

The world's addiction to fossil fuels is a "Frankenstein's monster sparing nothing and no one", the UN secretary general, António Guterres, told leaders at the World Economic Forum in Davos on Wednesday. "Our fossil fuel addiction is a Frankenstein's monster, sparing nothing and no one. All around us, we see clear signs that the monster has become master," Guterres said in a speech days after 2024 was revealed to have been the hottest year on record and Donald Trump began his second term as US president by pulling the country out of the Paris climate agreement and pledging to "drill, baby, drill" for more oil and gas. The fossil fuel industry gave 75m ( 60m) to Trump's campaign. Guterres said: "What we are seeing today – sea-level rise, heatwaves, floods, storms, droughts and wildfires – are just a preview of the horror movie to come."

OpenAI kicked off the AI race in 2022 with the launch of its ChatGPT bot, which offered lifelike responses to questions and showcased the rapid advances in the technology. It has prompted a gush of investment, including in the specialised data centres needed to power the computing. But the projected surge in demand for the centres, which will require huge amounts of power to run and money to be built, has raised concerns about the impact on energy supplies and questions about the role of foreign investors. In one of his final acts in the White House, former President Joe Biden put forward rules that would restrict exports of AI-related chips to dozens of countries around the world, saying the move would help the US control the industry. He also issued orders related to the development of data centres on government land, which spotlighted a role for clean energy in powering the centres.

This paper introduces a differentiable ray-tracing based model that incorporates aberrations and distortions to render realistic coded hyperspectral acquisitions using Coded-Aperture Spectral Snapshot Imagers (CASSI). CASSI systems can now be optimized in order to fulfill simultaneously several optical design constraints as well as processing constraints. Four comparable CASSI systems with varying degree of optical aberrations have been designed and modeled. The resulting rendered hyperspectral acquisitions from each of these systems are combined with five state-of-the-art hyperspectral cube reconstruction processes. These reconstruction processes encompass a mapping function created from each system's propagation model to account for distortions and aberrations during the reconstruction process. Our analyses show that if properly modeled, the effects of geometric distortions of the system and misalignments of the dispersive elements have a marginal impact on the overall quality of the reconstructed hyperspectral data cubes. Therefore, relaxing traditional constraints on measurement conformity and fidelity to the scene enables the development of novel imaging instruments, guided by performance metrics applied to the design or the processing of acquisitions. By providing a complete framework for design, simulation and evaluation, this work contributes to the optimization and exploration of new CASSI systems, and more generally to the computational imaging community.

LLMs' sources of knowledge are data snapshots containing factual information about entities collected at different timestamps and from different media types (e.g. wikis, social media, etc.). Such unstructured knowledge is subject to change due to updates through time from past to present. Equally important are the inconsistencies and inaccuracies occurring in different information sources. Consequently, the model's knowledge about an entity may be perturbed while training over the sequence of snapshots or at inference time, resulting in inconsistent and inaccurate model performance. In this work, we study the appropriateness of Large Language Models (LLMs) as repositories of factual knowledge. We consider twenty-four state-of-the-art LLMs that are either closed-, partially (weights), or fully (weight and training data) open-source. We evaluate their reliability in responding to time-sensitive factual questions in terms of accuracy and consistency when prompts are perturbed. We further evaluate the effectiveness of state-of-the-art methods to improve LLMs' accuracy and consistency. We then propose "ENtity-Aware Fine-tuning" (ENAF), a soft neurosymbolic approach aimed at providing a structured representation of entities during fine-tuning to improve the model's performance.