Cutting-edge NASA imaging technology can detect early signs of a plant virus that, if unaddressed, often proves devastating for wineries and grape growers, new research has found. While the breakthrough is good news for the wine and grape industry, which loses billions of dollars a year to the crop-ruining disease, it could eventually help global agriculture as a whole. Using intricate infrared images captured by airplane over California's Central Valley, researchers were able to distinguish Cabernet Sauvignon grape vines that were infected but not showing symptoms -- before the point at which growers can spot the disease and respond. The technology, coupled with machine learning and on-the-ground analysis, successfully identified infected plants with almost 90% accuracy in some cases, according to two new research papers. "This is the first time we've ever shown the ability to do viral disease detection on the airborne scale," said Katie Gold, an assistant professor of grape pathology at Cornell University and a lead researcher on the project.

They've just ducked out of one event and are headed to another, then another, where a frenzied mob awaits. As they careen through the streets of London--the short hop from Holborn to Bloomsbury--it's as if they're surfing one of civilization's before-and-after moments. The history-making force personified inside this car has captured the attention of the world. Everyone wants a piece of it, from the students who've waited in line to the prime minister. Inside the luxury van, wolfing down a salad, is the neatly coiffed 38-year-old entrepreneur Sam Altman, cofounder of OpenAI; a PR person; a security specialist; and me. Altman is unhappily sporting a blue suit with a tieless pink dress shirt as he whirlwinds through London as part of a monthlong global jaunt through 25 cities on six continents.

Haywood Talcove, CEO of LexisNexis Risk Solutions' Government Group, tells Fox News Digital that criminal groups, mostly in other countries, are advertising on social media to market their AI capabilities for fraud and other crimes. A criminologist recently unearthed a video of a multibillion-dollar, transnational criminal organization that has been stealing from the U.S. government since the pandemic and selling generative artificial intelligence tools to other criminals, an expert says. The 58-second clip, which was meant for the dark web, opens with a person – who goes by "Sanchez" – covered head to toe in black clothing and speaking behind a black skeleton mask with someone else who appears to be digging a grave behind him. "Yes, I sell Chase bank accounts. Yes, I am one of the first people to sell fake bank accounts four years ago," the man who calls himself "Sanchez" said.

OpenAI Chief Executive Officer Sam Altman is the first person to get an Indonesian golden visa as Southeast Asia's largest economy seeks to draw foreign investors. The country's immigration authority issued a 10-year visa for Altman as he "has an international reputation and may bring benefits to Indonesia," said Immigration Director General Silmy Karim in a statement. The co-founder of the ChatGPT creator would enjoy priority security screening at airports, longer stay periods and easier entry and exit processes, among other perks. Introduced last week to boost economic development, the new visa allows foreigners who make substantial investments in the country to remain for between five and 10 years. For example, an individual who invests $350,000 into shares of local public companies, savings accounts or government bonds is eligible for a five-year stay.

Russia's defence ministry said that at least three Ukrainian drones were shot down in the latest attempt by Kyiv to attack sites in Moscow. The ministry said early on Tuesday that air defence systems destroyed two drones over the Kaluga and Tver regions, which border the Moscow region. A third drone was shot down closer to the Russian capital, over the Istra district of the Moscow region, it added. Moscow's Mayor Sergei Sobyanin said the "attack drones which targeted Moscow" were destroyed, according to Russia's TASS news agency. Sobyanin said on the Telegram messaging app that there had been "no casualties", according to initial information.

Russia launched a drone attack on Ukraine's Danube River port of Izmail, leading to widespread damage to infrastructure, according to the region's governor. The attack came hours ahead of talks between Russian President Vladimir Putin and his Turkish counterpart, Recep Tayyip Erdogan, which were expected to touch on ways to revive the Black Sea grain deal that Moscow abandoned in July. Ukraine and Romania disagreed over whether the attack on Izmail hit Romanian territory on the other side of the river. Foreign Minister Dmytro Kuleba said Ukraine had visual evidence of the incident. Romanian Foreign Minister Luminita Odobescu condemned the "cynical" Russian attack on Ukrainian infrastructure but said no Russian drones or debris had fallen on Romanian territory.

Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated their vulnerability to potentially existent adversarial examples on graph-structured data. Existing approaches are either limited to structure attacks or restricted to local information, urging for the design of a more general attack framework on graph classification, which faces significant challenges due to the complexity of generating local-node-level adversarial examples using the global-graph-level information. To address this "global-to-local" attack challenge, we present a novel and general framework to generate adversarial examples via manipulating graph structure and node features. Specifically, we make use of Graph Class Activation Mapping and its variant to produce node-level importance corresponding to the graph classification task. Then through a heuristic design of algorithms, we can perform both feature and structure attacks under unnoticeable perturbation budgets with the help of both node-level and subgraph-level importance. Experiments towards attacking four state-of-the-art graph classification models on six real-world benchmarks verify the flexibility and effectiveness of our framework.

The development of Automated Driving Systems (ADSs) has made significant progress in the last years. To enable the deployment of Automated Vehicles (AVs) equipped with such ADSs, regulations concerning the approval of these systems need to be established. In 2021, the World Forum for Harmonization of Vehicle Regulations has approved a new United Nations regulation concerning the approval of Automated Lane Keeping Systems (ALKSs). An important aspect of this regulation is that "the activated system shall not cause any collisions that are reasonably foreseeable and preventable." The phrasing of "reasonably foreseeable and preventable" might be subjected to different interpretations and, therefore, this might result in disagreements among AV developers and the authorities that are requested to approve AVs. The objective of this work is to propose a method for quantifying what is "reasonably foreseeable and preventable". The proposed method considers the Operational Design Domain (ODD) of the system and can be applied to any ODD. Having a quantitative method for determining what is reasonably foreseeable and preventable provides developers, authorities, and the users of ADSs a better understanding of the residual risks to be expected when deploying these systems in real traffic. Using our proposed method, we can estimate what collisions are reasonably foreseeable and preventable. This will help in setting requirements regarding the safety of ADSs and can lead to stronger justification for design decisions and test coverage for developing ADSs.

Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable motion patterns, leading to a wide range of driving behaviors that warrant in-depth investigation. This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction (GIMTP) framework, designed to probabilistically predict future vehicle trajectories by effectively capturing these interactions. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. To holistically capture both spatial and temporal dependencies embedded in this dynamic adjacency matrix, the methodology incorporates the Diffusion Graph Convolutional Network (DGCN), thereby providing a graph embedding of both historical states and future states. Furthermore, we employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings for enhanced intention recognition and trajectory prediction. This model gives two-dimensional predictions for each mode of longitudinal and lateral driving behaviors and offers probabilistic future paths with corresponding probabilities, addressing the challenges of complex vehicle interactions and multi-modality of driving behaviors. Validation using real-world trajectory datasets demonstrates the efficiency and potential.

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research.