Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes. Traditional phytoplankton monitoring methods are often complex and lack timely analysis. Therefore, deep learning algorithms offer a promising approach for automated phytoplankton monitoring. However, the lack of large-scale, high-quality training samples has become a major bottleneck in advancing phytoplankton tracking. In this paper, we propose a challenging benchmark dataset, Multiple Phytoplankton Tracking (MPT), which covers diverse background information and variations in motion during observation. The dataset includes 27 species of phytoplankton and zooplankton, 14 different backgrounds to simulate diverse and complex underwater environments, and a total of 140 videos. To enable accurate real-time observation of phytoplankton, we introduce a multi-object tracking method, Deviation-Corrected Multi-Scale Feature Fusion Tracker(DSFT), which addresses issues such as focus shifts during tracking and the loss of small target information when computing frame-to-frame similarity. Specifically, we introduce an additional feature extractor to predict the residuals of the standard feature extractor's output, and compute multi-scale frame-to-frame similarity based on features from different layers of the extractor. Extensive experiments on the MPT have demonstrated the validity of the dataset and the superiority of DSFT in tracking phytoplankton, providing an effective solution for phytoplankton monitoring.

Russia's gradual, grinding advance in parts of Ukraine's eastern region of Donetsk succeeded in wresting away 4,168 sq km (1,609 square miles) of fields and abandoned villages in 2024 – equivalent to 0.69 percent of the country. That was the assessment of the Institute for the Study of War, a Washington-based think-tank, based on satellite imagery and geolocated video footage. "Russian forces have seized four mid-sized settlements – Avdiivka, Selydove, Vuhledar, and Kurakhove – in all of 2024, the largest of which had a pre-war population of just over 31,000 people," said the ISW. Russian forces spent four months taking Avdiivka, and two months each for Selydove and Kurakhove. "Seizing these settlements has not allowed Russian forces to threaten any notable Ukrainian defensive nodes," said the ISW, adding that Moscow's troops failed to conduct the kind of rapid, mechanised manoeuvre necessary to convert these "tactical gains into deep penetrations of Ukraine's rear".

Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in visual language models (VLMs) have pushed this enthusiasm to new heights. Differring from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they addressed. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods. A project associated with this review has been created at https://github.com/taolijie11111/VLMs-in-RS-review.

When it comes to new horror games, there are times of feast and famine, and this past year we gorged until our bellies bulged and our mouths dripped with gruesome grease. In 2024, we received a rich spread of dark experiences from solo creators, indie teams, AA developers and AAA studios in a vast array of genres and visual styles. There was a fantastic Silent Hill 2 remake and beefy updates to contemporary classics like Phasmophobia, Alan Wake 2 and The Outlast Trials, and there was also a steady cadence of brand-new horror franchises expanding the genre in unexpected ways. First, let's take a moment to celebrate a sampling of the year's fresh horror universes. This is not a comprehensive list of new horror franchises in 2024, but it's a suitable demonstration of how vast and varied the offerings were this year.

So much of this year felt like a fever dream: The attempted assassination of Donald Trump. Which is why, this year, I'm leaning into my nerdish tendencies and rounding up some good, interesting, or inspiring news stories from the science world--promising discoveries, exciting new data, historic events, and unsung heroes. In the hope of providing relief from the hell that has been 2024, here's a non-comprehensive list of the year's coolest science stories, both big and small: Wildlife filmmaker Carlos Gauna and University of California, Riverside, PhD student Phillip Sternes spotted what appears to be a baby great white shark off the coast of California last year. In January, the team published the photos in the journal Environmental Biology of Fishes. "Where white sharks give birth is one of the holy grails of shark science. No one has ever been able to pinpoint where they are born, nor has anyone seen a newborn baby shark alive," Gauna said in a UC Riverside press release.

This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning due to their short-sighted nature, missing crucial data collection opportunities and increasing exposure to hazards. To address these limitations, we introduce WAITR (Weighted Aggregate Inter-Temporal Reward), a novel path-planning framework that integrates a knowledge graph with pathlet-based planning, segmenting the environment into dynamic, speed-adjusted sub-regions (pathlets). This structure enables coordinated, adaptive planning, as agents can operate within time-bound regions while dynamically responding to environmental changes. WAITR's cumulative scoring mechanism balances immediate data collection with long-term optimization of Points of Interest (POIs), ensuring safer navigation and comprehensive data coverage. Experimental results show that WAITR substantially improves POI coverage and reduces exposure to hazards, achieving up to 27.1\% greater event coverage than traditional greedy methods.

Russia's Foreign Ministry accused NATO of trying to turn Moldova into a logistical centre to supply the Ukrainian army and of seeking to bring the Western alliance's military infrastructure closer to Russia. Arto Pahkin, the head of operations of the Finnish electricity grid, told the country's public broadcaster Yle that "the possibility of sabotage cannot be ruled out" after an undersea power cable linking Finland and Estonia broke down. It is the latest in a series of incidents involving telecom cables and energy pipelines in the Baltic Sea. A "terrorist act" sank the Russian cargo ship that went down in international waters in the Mediterranean this week, the Russian state-owned company that owns the vessel said. The Oboronlogistika company said it "thinks a targeted terrorist attack was committed on December 23, 2024, against the Ursa Major", without indicating who may have been behind the act or why. The Azerbaijan Airlines passenger jet that crashed near the city of Aktau in Kazakhstan, killing 38 people, was earlier diverting from an area of Russia that Moscow has recently defended against Ukrainian drone attacks.

This year may not have been as jam packed as 2023 was for gaming, but there were still plenty of amazing new releases. Whether you love a good indie or a big-budget production, this year had you covered. All you needed to do was look a bit deeper than you might have in 2023. The core of Animal Well isn't that structurally complicated: It's a lock-and-key Metroidvania. You go to places to unlock other places and abilities. Beating the core "story" opens up a couple layers of admirably elaborate and increasingly meta secrets, but let's be real, most people interested in those are just going to look up the answers online. And yet, you play it, and you can't help but think there isn't much like it nowadays. It's the fact that you never learn what your little blob guy is. It's giving you a map to mark up yourself instead of providing any instructions.

PS4/5, Xbox, PC, Nintendo Switch Taiwanese studio Red Candle Games broke through in 2019 with the first-person horror game, Devotion. Its follow-up, Nine Sols, is less grungy but no less distinct, a robust 2D action-platformer with an exquisite "taopunk" aesthetic. This vivid sci-fi world feels as if it is constructed as much from bamboo and jade as steel and microchips. Alongside absorbing exploration and blistering combat, you study and grow various strains of alien flora found aboard a labyrinthine spaceship. The ultimate goal is escape, but you may never actually want to leave the strange, bioluminescent garden you come to cultivate.

Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the Earth system. In recent years, geoscience and climate scientists have benefited from rapid progress in deep learning to advance the estimation of ECV products with improved accuracy. However, the quantification of uncertainties associated with the output of such deep learning models has yet to be thoroughly adopted. This survey explores the types of uncertainties associated with ECVs estimated from deep learning and the techniques to quantify them. The focus is on highlighting the importance of quantifying uncertainties inherent in ECV estimates, considering the dynamic and multifaceted nature of climate data. The survey starts by clarifying the definition of aleatoric and epistemic uncertainties and their roles in a typical satellite observation processing workflow, followed by bridging the gap between conventional statistical and deep learning views on uncertainties. Then, we comprehensively review the existing techniques for quantifying uncertainties associated with deep learning algorithms, focusing on their application in ECV studies. The specific need for modification to fit the requirements from both the Earth observation side and the deep learning side in such interdisciplinary tasks is discussed. Finally, we demonstrate our findings with two ECV examples, snow cover and terrestrial water storage, and provide our perspectives for future research.