Algorithms process this data to pinpoint exact coordinates[11][12]. Geo-localization is important for organizing and analyzing large volumes of imagery data, as demonstrated by systems like the US Geological Survey (USGS), which classify and locate satellite and drone images to streamline data collection and analysis. Social media platforms like Instagram use geo-localization to tag photos with specific locations, enabling users to explore location-based content[11]. Despite its significance, many images and videos lack geo-localization data, particularly those collected in the past or by devices without GPS capabilities[12].

Vision-language models (VLMs) have achieved significant strides in recent times specially in multimodal tasks, yet they remain susceptible to adversarial attacks on their vision components. To address this, we propose Sim-CLIP, an unsupervised adversarial fine-tuning method that enhances the robustness of the widely-used CLIP vision encoder against such attacks while maintaining semantic richness and specificity. By employing a Siamese architecture with cosine similarity loss, Sim-CLIP learns semantically meaningful and attack-resilient visual representations without requiring large batch sizes or momentum encoders. Our results demonstrate that VLMs enhanced with Sim-CLIP's fine-tuned CLIP encoder exhibit significantly enhanced robustness against adversarial attacks, while preserving semantic meaning of the perturbed images. Notably, Sim-CLIP does not require additional training or fine-tuning of the VLM itself; replacing the original vision encoder with our fine-tuned Sim-CLIP suffices to provide robustness. This work underscores the significance of reinforcing foundational models like CLIP to safeguard the reliability of downstream VLM applications, paving the way for more secure and effective multimodal systems.

Israel's defense minister struck an ominous tone Friday after an Iranian-made drone fired by Houthi rebels in Yemen struck Tel Aviv, telling Israeli media that Jerusalem would "settle the score." "I held an operational situation assessment this morning to review the steps required to strengthen our defense arrays in light of events overnight, as well as the intelligence and operational activities required against those responsible for the attack," Israeli Minister of Defense Yoav Gallant said in a statement. "The year 2024 is marked by war. We must be prepared for every scenario and every arena." Israeli Minister of Defense Yoav Gallant sits with defense officials after a Yemen-based Houthi drone strike on Tel Aviv July 19, 2024.

Yemen's Houthi group has claimed responsibility for the drone that struck overnight in Tel Aviv, Israel, killing one person and injuring eight. Israeli media identified the dead man as 50-year-old Yevgeny Ferder, who had moved to Israel from Belarus at the beginning of the Russia-Ukraine war. Last night's strike is unique -- it's the first time the group is known to have hit Tel Aviv, though the Houthi have waged a continued campaign against targets they claim are linked to Israel since the ongoing devastating war on Gaza broke out in October. The drone struck in central Tel Aviv in the early hours of Friday morning. The site itself is thought to be close to a number of hotels, many hosting those displaced from Israel's northern border with Lebanon. A US embassy office is also close to the site of the attack.

First responders are on scene in Tel Aviv after a large explosion rocked the city in the middle of the night. The blast happened approximately one block from a U.S. embassy branch office. An explosion that rocked Tel Aviv overnight Thursday has left one person dead and several others wounded. Military officials say they believe the source of the explosion was a deadly drone attack, and Yemen's Houthi rebels have already claimed responsibility for a drone strike in the area near the U.S. embassy, the Associated Press reported. The drone was not intercepted despite it being identified prior to the explosion due to human error.

Yemen's Houthi fighters have claimed responsibility following a suspected drone attack on Israel's Tel Aviv, which killed one person and injured at least 10, according to reports. A spokesperson for the Houthi armed forces said in a post on social media on Friday that the Yemen-based group had "targeted'Tel Aviv' in occupied Palestine". The Israeli military said it had opened an investigation into the large explosion near the United States Embassy office in the city and would determine why the country's air defence systems were not activated to intercept the "aerial target". Israel's air force has increased patrols to "protect the country's skies", the military added in a post on social media. Israeli police said the body of a man was found in an apartment close to the explosion and that the circumstances were being investigated.

The Israeli military says it is investigating an apparent drone attack that hit central Tel Aviv in the early hours of Friday. In a statement it said an initial inquiry indicated the explosion had been caused by the falling of an "aerial target" and announced it was increasing air patrols. Israeli emergency services say the explosion left one person dead and several lightly injured. Yemen's Houthi militants, which are backed by Iran, announced on social media that they would reveal details about a military operation that had targeted Tel Aviv. The incident also came after the Israeli military confirmed it had killed a senior commander of the Hezbollah militia in southern Lebanon.

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements.

High-resolution climate simulations are very valuable for understanding climate change impacts and planning adaptation measures. This has motivated use of regional climate models at sufficiently fine resolution to capture important small-scale atmospheric processes, such as convective storms. However, these regional models have very high computational costs, limiting their applicability. We present CPMGEM, a novel application of a generative machine learning model, a diffusion model, to skilfully emulate precipitation simulations from such a high-resolution model over England and Wales at much lower cost. This emulator enables stochastic generation of high-resolution (8.8km), daily-mean precipitation samples conditioned on coarse-resolution (60km) weather states from a global climate model. The output is fine enough for use in applications such as flood inundation modelling. The emulator produces precipitation predictions with realistic intensities and spatial structures and captures most of the 21st century climate change signal. We show evidence that the emulator has skill for extreme events up to and including 1-in-100 year intensities. Potential applications include producing high-resolution precipitation predictions for large-ensemble climate simulations and downscaling different climate models and climate change scenarios to better sample uncertainty in climate changes at local-scale.

Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education. Its main task is to estimate treatment effects and make intervention policies. Traditionally, most of the previous works typically focus on the binary treatment setting that there is only one treatment for a unit to adopt or not. However, in practice, the treatment can be much more complex, encompassing multi-valued, continuous, or bundle options. In this paper, we refer to these as complex treatments and systematically and comprehensively review the causal inference methods for addressing them. First, we formally revisit the problem definition, the basic assumptions, and their possible variations under specific conditions. Second, we sequentially review the related methods for multi-valued, continuous, and bundled treatment settings. In each situation, we tentatively divide the methods into two categories: those conforming to the unconfoundedness assumption and those violating it. Subsequently, we discuss the available datasets and open-source codes. Finally, we provide a brief summary of these works and suggest potential directions for future research.