The way the UK government has been tagging migrants with GPS trackers is illegal, the country's privacy regulator ruled on Friday, in a rebuke to officials who have been experimenting with migrant-surveillance tech in both the UK and the US. As part of an 18-month pilot that concluded in December, the UK interior ministry, known as the Home Office, forced up to 600 people who arrived in the country without permission to wear ankle tags that continuously tracked their locations. However, that pilot broke UK data protection law because it did not properly assess the privacy intrusion of GPS tracking or give migrants clear information about the data that was being collected, the UK's Information Commissioner's Office (ICO) said today. The ruling means the Home Office has 28 days to update its policies around GPS tracking. Friday's decision also means the ICO could fine the Home Office up to 17.5 million ( 22 million) or 4 percent of its turnover--whichever is higher--if it resumes tagging people who arrive on the UK south coast in small boats from Europe.

A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i.e., row and column sums). Prior approaches to this problem have repurposed the classic iterative proportional fitting (IPF) procedure, also known as Sinkhorn's algorithm, with promising empirical results. However, the statistical foundation for using IPF has not been well understood: under what settings does IPF provide principled estimation of a dynamic network from its marginals, and how well does it estimate the network? In this work, we establish such a setting, by identifying a generative network model whose maximum likelihood estimates are recovered by IPF. Our model both reveals implicit assumptions on the use of IPF in such settings and enables new analyses, such as structure-dependent error bounds on IPF's parameter estimates. When IPF fails to converge on sparse network data, we introduce a principled algorithm that guarantees IPF converges under minimal changes to the network structure. Finally, we conduct experiments with synthetic and real-world data, which demonstrate the practical value of our theoretical and algorithmic contributions.

While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives. This paper delves further into current order-based methods, examining their inefficiencies in utilizing reward values and addressing misalignment issues. Building upon these findings, we propose a novel \textbf{V}alue-based \textbf{C}ali\textbf{B}ration (VCB) method to better align LLMs with human preferences. Experimental results demonstrate that VCB surpasses existing alignment methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and stability in diverse settings.

The direction of the third year of the Russia-Ukraine war will largely depend upon whether Congress can overcome hesitation about continued support as fatigue sets in, experts told Fox News Digital. "America's partnerships and alliances have never been more important than they are right now," Kenneth J Braithwaite, former secretary of the Navy in the Trump administration and former ambassador to Norway, argued. "Communism is alive and well, and we are up against it as Russia wages war against Europe and China seeks to exert more influence on the globe," Braithwaite said. "That means Americans need to look outside our borders at how we can protect ourselves from these looming challenges, starting with one of our greatest force multipliers: Our partnerships and willingness to stand united against authoritarian threats to sovereignty." The second year of the Ukraine invasion proved a truly chaotic one, starting with Russia seeming to suffer catastrophic setbacks when the vital Wagner forces turned traitor and tried to march on Moscow.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russian forces threatened to shoot down a French surveillance aircraft patrolling in international airspace over the Black Sea, a signal of increasingly aggressive behavior from Moscow as its invasion of Ukraine struggles to make headway, French defense officials said Thursday. "A Russian air traffic control system threatened to shoot down French aircraft in the Black Sea when we were in a free international zone where we patrol," the French defense minister, Sébastien Lecornu, said on RTL radio. A French military spokesman, Col. Pierre Gaudillière, said Lecornu was referring to an incident in mid-November that involved one of France's four giant Airborne Warning and Control System, or AWACS, surveillance aircraft that was flying over international waters in the Black Sea.

The pursuit of direct model equation discovery has been an ongoing and significant area of interest in scientific machine learning. The popular sparse identification of nonlinear dynamics (SINDy) framework [1] offers a promising approach to extract parsimonious equations directly from data. SINDy's promotion of parsimony by sparse regression allows for the identification of an interpretable model that balances accuracy with generalizability, while its simplicity leads to a relatively efficient and fast learning process compared to other machine learning techniques. The framework has been successfully applied in a variety of applications, such as model idenficiation in plasma physics [2], control engineering [3, 4], biological transport problems [5], socio-cognitive systems [6], epidemiology [7, 8] and turbulence modelling [9]. Furthermore, its remarkable extendibility has attracted a range of modifications, including the adaptation to discover partial differential equations [10], the extension to libraries of rational functions [11], the integration of ensembling techniques to improve data efficiency [12] and the use of weak formulations [13, 14] to avoid noise amplification when computing derivatives from discrete data. One major difficulty in using scientific machine learning methods in fields such as biophysics, ecology, and microbiology, is that measured data from these fields is often noisy and scarce.

U.S. Central Command announced Thursday that American aircraft and a coalition warship have shot down six Houthi one-way attack drones in the Red Sea. The unmanned aerial vehicles were identified as "likely targeting U.S. and coalition warships and were an imminent threat," it said, noting that the drones were taken out around 4:30 a.m. "Later, between 8:30 a.m. and 9:45 a.m., the Houthis fired two anti-ship ballistic missiles from southern Yemen into the Gulf of Aden," Central Command also wrote in a post on X. "The missiles impacted MV Islander, a Palau-flagged, U.K.-owned, cargo carrier causing one minor injury and damage. The ship is continuing its voyage." The attack comes after the Pentagon earlier this week confirmed that the Houthis shot down a U.S. MQ-9 Reaper drone off the coast of Yemen on Monday.

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existence of a robust correlation between the exact location of P. oceanica and water biogeochemical properties. The model's feature importance, showed that carbon-related variables as net biomass production and downward surface mass flux of carbon dioxide have their values altered in the areas with P. oceanica, which in turn can be used for indirect location of P. oceanica meadows. The study provides the evidence of the plant's ability to exert a global impact on the environment and underscores the crucial role of this plant in sea ecosystems, emphasizing the need for its conservation and management.

The online diffusion of information related to Europe and migration has been little investigated from an external point of view. However, this is a very relevant topic, especially if users have had no direct contact with Europe and its perception depends solely on information retrieved online. In this work we analyse the information circulating online about Europe and migration after retrieving a large amount of data from social media (Twitter), to gain new insights into topics, magnitude, and dynamics of their diffusion. We combine retweets and hashtags network analysis with geolocation of users, linking thus data to geography and allowing analysis from an "outside Europe" perspective, with a special focus on Africa. We also introduce a novel approach based on cross-lingual quotes, i.e. when content in a language is commented and retweeted in another language, assuming these interactions are a proxy for connections between very distant communities. Results show how the majority of online discussions occurs at a national level, especially when discussing migration. Language (English) is pivotal for information to become transnational and reach far. Transnational information flow is strongly unbalanced, with content mainly produced in Europe and amplified outside. Conversely Europe-based accounts tend to be self-referential when they discuss migration-related topics. Football is the most exported topic from Europe worldwide. Moreover, important nodes in the communities discussing migration-related topics include accounts of official institutions and international agencies, together with journalists, news, commentators and activists.

In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the used explainable AI methods and their objectives, findings, and challenges in Remote Sensing applications is still missing. In this paper, we address this issue by performing a systematic review to identify the key trends of how explainable AI is used in Remote Sensing and shed light on novel explainable AI approaches and emerging directions that tackle specific Remote Sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights in Remote Sensing, and reflect on the approaches used for explainable AI methods evaluation. Our review provides a complete summary of the state-of-the-art in the field. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field of explainable AI in Remote Sensing.