We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy installations from satellite imagery, then deploy them on over 13 trillion pixels covering the world. For each detected feature, we estimate the construction date and the preceding land use type. This dataset offers crucial insights into progress toward sustainable development goals and serves as a valuable resource for policymakers, researchers, and stakeholders aiming to assess and promote effective strategies for renewable energy deployment. Our final spatial dataset includes 375,197 individual wind turbines and 86,410 solar PV installations. We aggregate our predictions to the country level -- estimating total power capacity based on construction date, solar PV area, and number of windmills -- and find an $r^2$ value of $0.96$ and $0.93$ for solar PV and onshore wind respectively compared to IRENA's most recent 2023 country-level capacity estimates.

In the 2023 edition of the White Paper on Information and Communications, it is estimated that the population of social networking services in Japan will exceed 100 million by 2022, and the influence of social networking services in Japan is growing significantly. In addition, marketing using SNS and research on the propagation of emotions and information on SNS are being actively conducted, creating the need for a system for predicting trends in SNS interactions. We have already created a system that simulates the behavior of various communities on SNS by building a virtual SNS environment in which agents post and reply to each other in a chat community created by agents using a LLMs. In this paper, we evaluate the impact of the search extension generation mechanism used to create posts and replies in a virtual SNS environment using a simulation system on the ability to generate posts and replies. As a result of the evaluation, we confirmed that the proposed search extension generation mechanism, which mimics human search behavior, generates the most natural exchange.

Rapid evolution of territories due to climate change and human impact requires prompt and effective updates to geospatial databases maintained by the National Mapping Agency. This paper presents a comprehensive overview of change detection methods tailored for the operational updating of large-scale geographic databases. This review first outlines the fundamental definition of change, emphasizing its multifaceted nature, from temporal to semantic characterization. It categorizes automatic change detection methods into four main families: rule-based, statistical, machine learning, and simulation methods. The strengths, limitations, and applicability of every family are discussed in the context of various input data. Then, key applications for National Mapping Agencies are identified, particularly the optimization of geospatial database updating, change-based phenomena, and dynamics monitoring. Finally, the paper highlights the current challenges for leveraging change detection such as the variability of change definition, the missing of relevant large-scale datasets, the diversity of input data, the unstudied no-change detection, the human in the loop integration and the operational constraints. The discussion underscores the necessity for ongoing innovation in change detection techniques to address the future needs of geographic information systems for national mapping agencies.

Graph Neural Networks (GNNs) have gained attention for their ability to learn representations from graph data. Due to privacy concerns and conflicts of interest that prevent clients from directly sharing graph data with one another, Vertical Graph Federated Learning (VGFL) frameworks have been developed. Recent studies have shown that VGFL is vulnerable to adversarial attacks that degrade performance. However, it is a common problem that client nodes are often unlabeled in the realm of VGFL. Consequently, the existing attacks, which rely on the availability of labeling information to obtain gradients, are inherently constrained in their applicability. This limitation precludes their deployment in practical, real-world environments. To address the above problems, we propose a novel graph adversarial attack against VGFL, referred to as VGFL-SA, to degrade the performance of VGFL by modifying the local clients structure without using labels. Specifically, VGFL-SA uses a contrastive learning method to complete the attack before the local clients are trained. VGFL-SA first accesses the graph structure and node feature information of the poisoned clients, and generates the contrastive views by node-degree-based edge augmentation and feature shuffling augmentation. Then, VGFL-SA uses the shared graph encoder to get the embedding of each view, and the gradients of the adjacency matrices are obtained by the contrastive function. Finally, perturbed edges are generated using gradient modification rules. We validated the performance of VGFL-SA by performing a node classification task on real-world datasets, and the results show that VGFL-SA achieves good attack effectiveness and transferability.

An invitation to a "candlelight" dinner held this past Saturday at President Donald Trump's Mar-a-Lago club asked prospective guests to spend 1 million per seat. Trump attended the dinner along with Elon Musk, according to multiple photographs and videos of the event viewed by WIRED. Elon Musk, wearing his standard uniform of a black sport coat over a black T-shirt, was seen shaking hands and waving to other attendees. He was with a woman wearing a floor length gown who appeared to be Shivon Zilis, according to Instagram Reels posted by multiple guests. Zilis, a Neuralink executive who previously sat on the board of OpenAI, is the mother of four of Musk's 14 known children.

President Joe Biden signs a document with his very own hands.Oliver Contreras/White House/Zuma President Donald Trump has a new hobbyhorse: That his predecessor, President Joe Biden, didn't legally grant pardons to people Trump wants to harass because the pardons were signed with an autopen, a device for replicating a signature, rather than by hand. Trump has identified some signature requirement as the one rule presidents must obey. "The'Pardons' that Sleepy Joe Biden gave to the Unselect Committee of Political Thugs, and many others, are hereby declared VOID, VACANT, AND OF NO FURTHER FORCE OR EFFECT, because of the fact that they were done by Autopen," he ranted on Truth Social just after midnight on Monday. "In other words, Joe Biden did not sign them but, more importantly, he did not know anything about them!" Trump and his MAGA allies have embraced a lawless approach to the presidency. Trump's executive orders, actions, and legal filings all point to an understanding of the president as far more powerful than previously understood, with king-like powers over the entire executive branch.

Charlie Hurt and Griff Jenkins examine a protest of border czar Tom Homan's meeting in Albany with New York lawmakers over their refusal to enforce immigration laws. Department of Homeland Security (DHS) Secretary Kristi Noem announced the building of seven new miles of border wall in Arizona as part of the administration's efforts to "make America safe again." Noem's announcement, coming in a short video posted to her X account, marks the beginning of additional border wall construction along the southern border during the second Trump administration. The DHS said in a press release Friday that U.S. Customs and Border Patrol (CBP) awarded the first contract of President Donald Trump's second term to Granite Construction Co. for more than 70 million, which will result in seven new miles of border wall in the Rio Grande Valley Sector, according to Noem's announcement. "Everybody, I'm here in Arizona, and right at this spot, you can see where the border wall ends," Noem said while standing along the border, donning a CBP hat and jacket.

Turkiye has always placed a premium on its defence, initially buying then developing its own weapons. The owner of NATO's second-largest standing army has also emerged as a notable weapons exporter, with some iconic products on the international market. Turkiye's exports increased year on year to reach 7.1bn in 2024 – from 1.9bn a decade prior – with customers across Europe and the Middle East. And why is it important? Turkiye has sought military self-sufficiency for a while, a gradual process that saw it establish the Defence Industry Development and Support Administration Office (SAGEB) in 1985.

NDERWATER acoustic target recognition (UATR) is crucial for applications such as environmental monitoring, Deep learning models, such as convolutional neural networks exploration, and ship noise characterization, aiding in (CNNs), excel in feature representation and transfer learning, marine resource management and ocean-based technologies to adapting well to underwater acoustics when pre-trained on enhance ocean monitoring [1], [2]. Passive sonar uses external large vision datasets [11]-[13]. Similarly, pre-trained audio acoustic signals to identify underwater objects without emitting neural networks (PANNs) [14], trained on a large audio dataset sound [2]. Spectrograms, generated through signal processing (AudioSet [15]), have proven effective for passive sonar techniques like Short-Time Fourier Transform (STFT) classification where data scarcity is a challenge [16]. Moreover, and Mel-frequency spectrograms, transform signals into visual transformer-based models, including vision transformers representations, facilitating complex pattern extraction from (ViTs) [17] and audio spectrogram transformers (ASTs) [18], acoustic data [3]-[5].

3D activity reasoning and planning has attracted increasing attention in human-robot interaction and embodied AI thanks to the recent advance in multimodal learning. However, most existing works share two constraints: 1) heavy reliance on explicit instructions with little reasoning on implicit user intention; 2) negligence of inter-step route planning on robot moves. To bridge the gaps, we propose 3D activity reasoning and planning, a novel 3D task that reasons the intended activities from implicit instructions and decomposes them into steps with inter-step routes and planning under the guidance of fine-grained 3D object shapes and locations from scene segmentation. We tackle the new 3D task from two perspectives. First, we construct ReasonPlan3D, a large-scale benchmark that covers diverse 3D scenes with rich implicit instructions and detailed annotations for multi-step task planning, inter-step route planning, and fine-grained segmentation. Second, we design a novel framework that introduces progressive plan generation with contextual consistency across multiple steps, as well as a scene graph that is updated dynamically for capturing critical objects and their spatial relations. Extensive experiments demonstrate the effectiveness of our benchmark and framework in reasoning activities from implicit human instructions, producing accurate stepwise task plans, and seamlessly integrating route planning for multi-step moves. The dataset and code will be released.