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.

This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariate extremes is transformed to one of modelling an angular density, and the tail of a univariate radial variable conditioned on angle. In the SPAR approach, the tail of the radial variable is modelled using a generalised Pareto (GP) distribution, providing a natural extension of univariate extreme value theory to the multivariate setting. In this work, we show how the method can be applied in higher dimensions, using a case study for five metocean variables: wind speed, wind direction, wave height, wave period and wave direction. The angular variable is modelled empirically, while the parameters of the GP model are approximated using fully-connected deep neural networks. Our data-driven approach provides great flexibility in the dependence structures that can be represented, together with computationally efficient routines for training the model. Furthermore, the application of the method requires fewer assumptions about the underlying distribution(s) compared to existing approaches, and an asymptotically justified means for extrapolating outside the range of observations. Using various diagnostic plots, we show that the fitted models provide a good description of the joint extremes of the metocean variables considered.

The police chief of a small Nebraska city has come forward with a warning for New Jersey after his community was terrorized by mystery drones. Ord, Nebraska Police Chief Chris Grooms revealed to DailyMail.com Across nearly three weeks of nighttime encounters, typically between 7pm and 11pm, these inexplicable SUV-sized drones operated'with impunity,' Chief Grooms said, and sometimes seemed to be'toying with law enforcement.' 'A lot of reports by ranchers stated that these objects were harassing their horses or cattle on a nightly basis,' he added. Some of the drones reached speeds of 120mph.

Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overlook effective alignment with human preferences. In the alignment process, reward models (RMs) act as a crucial proxy for human values to guide optimization. However, it remains unclear how to evaluate and select a reliable RM for preference alignment in RALMs. To this end, we propose RAG-RewardBench, the first benchmark for evaluating RMs in RAG settings. First, we design four crucial and challenging RAG-specific scenarios to assess RMs, including multi-hop reasoning, fine-grained citation, appropriate abstain, and conflict robustness. Then, we incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase the diversity of data sources. Finally, we adopt an LLM-as-a-judge approach to improve preference annotation efficiency and effectiveness, exhibiting a strong correlation with human annotations. Based on the RAG-RewardBench, we conduct a comprehensive evaluation of 45 RMs and uncover their limitations in RAG scenarios. Additionally, we also reveal that existing trained RALMs show almost no improvement in preference alignment, highlighting the need for a shift towards preference-aligned training.We release our benchmark and code publicly at https://huggingface.co/datasets/jinzhuoran/RAG-RewardBench/ for future work.

Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast and accurate Transformer which postprocesses each ensemble member individually while allowing information exchange across variables, spatial dimensions and lead times by means of multi-headed self-attention. Weather foreacasts are postprocessed over 20 lead times simultaneously while including up to twelve meteorological predictors. We use the EUPPBench dataset for training which contains ensemble predictions from the European Center for Medium-range Weather Forecasts' integrated forecasting system alongside corresponding observations. The work presented here is the first to postprocess the ten and one hundred-meter wind speed forecasts within this benchmark dataset, while also correcting the two-meter temperature. Our approach significantly improves the original forecasts, as measured by the CRPS, with 17.5 % for two-meter temperature, nearly 5% for ten-meter wind speed and 5.3 % for one hundred-meter wind speed, outperforming a classical member-by-member approach employed as competitive benchmark. Furthermore, being up to 75 times faster, it fulfills the demand for rapid operational weather forecasts in various downstream applications, including renewable energy forecasting.