Winter officially arrives on Sunday, December 21. A view of the Geminid meteor shower and stargazing at the Tunnel View of Yosemite National Park on December 14, 2023. Breakthroughs, discoveries, and DIY tips sent every weekday. As one might expect from a month full of long, dark nights, December is a highlight for those with a penchant for looking to the stars . This year, the stargazing on offer promises to be particularly good.

Three meteor showers will peak this month. This delightfully detailed false color image of Saturn is a combination of three images taken in January 1998 by the Hubble Space Telescope and shows the ringed planet in reflected infrared light. Different colors indicated varying heights and compositions of cloud layers generally thought to consist of ammonia ice crystals. The eye-catching rings cast a shadow on Saturn's upper hemisphere, while the bright stripe seen within the left portion of the shadow is infrared sunlight streaming through the large gap in the rings known as the Cassini Division. Breakthroughs, discoveries, and DIY tips sent every weekday.

Comet C/2025 A6 (Lemmon) was only discovered in January 2025. Breakthroughs, discoveries, and DIY tips sent every weekday. Stargazers will be happy to know that October will see the cosmos compensating for a couple of relatively lean months.There will be a whole bunch of celestial bodies to see over the next month, including the year's largest and brightest full moon, the arrival of a brand new comet, two meteor showers and a good chance to see our solar system's favorite big fella in all his glory. October's full moon finds our closest celestial companion at its perigee, i.e. the point at which it's closest to the Earth. This means that this month's full moon will be [drum roll] a supermoon!

The 2019-20 Australia bushfire incurred numerous economic losses and significantly affected the operations of power systems. A power station or transmission line can be significantly affected due to bushfires, leading to an increase in operational costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems subject to bushfires. Considering the stochastic nature of bushfire spread, we develop a model to capture such dynamics based on Moore's neighborhood model. Under a periodic inspection scheme that reveals the in-situ bushfire status, we propose an online optimization modeling framework that sequentially plans the power flows in the electricity network. Our framework assumes that the spread of bushfires is non-stationary over time, and the spread and containment probabilities are unknown. To meet these challenges, we develop a contextual online learning algorithm that treats the in-situ geographical information of the bushfire as a 'spatial context'. The online learning algorithm learns the unknown probabilities sequentially based on the observed data and then makes the OPF decision accordingly. The sequential OPF decisions aim to minimize the regret function, which is defined as the cumulative loss against the clairvoyant strategy that knows the true model parameters. We provide a theoretical guarantee of our algorithm by deriving a bound on the regret function, which outperforms the regret bound achieved by other benchmark algorithms. Our model assumptions are verified by the real bushfire data from NSW, Australia, and we apply our model to two power systems to illustrate its applicability.

We live in frightening times. Wars in Ukraine and Gaza could widen, the polar ice caps are melting, and even some scientists developing artificial intelligence systems are worried about unleashing a monster. But those fears all pale in comparison to what really gives Americans the jitters. The calamity that worries them above all else is a total economic collapse in the US. The Pentagon's four legged robot dogs may offer a glimpse of what killer machines will look like An economic meltdown is the top fear for a third of respondents.

We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing. We propose STORM, a writing system for the Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking. STORM models the pre-writing stage by (1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline. For evaluation, we curate FreshWiki, a dataset of recent high-quality Wikipedia articles, and formulate outline assessments to evaluate the pre-writing stage. We further gather feedback from experienced Wikipedia editors. Compared to articles generated by an outline-driven retrieval-augmented baseline, more of STORM's articles are deemed to be organized (by a 25% absolute increase) and broad in coverage (by 10%). The expert feedback also helps identify new challenges for generating grounded long articles, such as source bias transfer and over-association of unrelated facts.

In this paper we address the challenge of improving Automatic Speech Recognition (ASR) for a low-resource language, Hawaiian, by incorporating large amounts of independent text data into an ASR foundation model, Whisper. To do this, we train an external language model (LM) on ~1.5M words of Hawaiian text. We then use the LM to rescore Whisper and compute word error rates (WERs) on a manually curated test set of labeled Hawaiian data. As a baseline, we use Whisper without an external LM. Experimental results reveal a small but significant improvement in WER when ASR outputs are rescored with a Hawaiian LM. The results support leveraging all available data in the development of ASR systems for underrepresented languages.

Recent advancements in AI have led to the development of large multimodal models (LMMs) capable of processing complex tasks involving joint reasoning over text and visual content in the image (e.g., navigating maps in public places). This paper introduces ConTextual, a novel benchmark comprising instructions designed explicitly to evaluate LMMs' ability to perform context-sensitive text-rich visual reasoning. ConTextual emphasizes diverse real-world scenarios (e.g., time-reading, navigation, shopping and more) demanding a deeper understanding of the interactions between textual and visual elements. Our findings reveal a significant performance gap of 30.8% between the best-performing LMM, GPT-4V(ision), and human capabilities using human evaluation indicating substantial room for improvement in context-sensitive text-rich visual reasoning. Notably, while GPT-4V excelled in abstract categories like meme and quote interpretation, its overall performance still lagged behind humans. In addition to human evaluations, we also employed automatic evaluation metrics using GPT-4, uncovering similar trends in performance disparities. We also perform a fine-grained evaluation across diverse visual contexts and provide qualitative analysis which provides a robust framework for future advancements in the LMM design. https://con-textual.github.io/

While chain-of-thought (CoT) prompting has revolutionized how LLMs perform reasoning tasks, its current methods and variations (e.g, Self-consistency, ReACT, Reflexion, Tree-of-Thoughts (ToT), Cumulative Reasoning (CR)) suffer from limitations like slowness, limited context grounding, hallucination and inconsistent outputs. To overcome these challenges, we introduce Evidence to Generate (E2G), a novel single-agent, two-step prompting framework. Instead of unverified reasoning claims, this innovative approach leverages the power of "evidence for decision making" by first focusing exclusively on the thought sequences (the series of intermediate steps) explicitly mentioned in the context which then serve as extracted evidence, guiding the LLM's output generation process with greater precision and efficiency. This simple yet powerful approach unlocks the true potential of chain-of-thought like prompting, paving the way for faster, more reliable, and more contextually aware reasoning in LLMs. \tool achieves remarkable results robustly across a wide range of knowledge-intensive reasoning and generation tasks, surpassing baseline approaches with state-of-the-art LLMs. For example, (i) on LogiQA benchmark using GPT-4 as backbone model, \tool achieves a new state-of-the Accuracy of 53.8% exceeding CoT by 18%, ToT by 11%, CR by 9% (ii) a variant of E2G with PaLM2 outperforms the variable-shot performance of Gemini Ultra by 0.9 F1 points, reaching an F1 score of 83.3 on a subset of DROP.

Climate change has ratcheted up the risk of explosive wildfire growth in California by 25% and will continue to drive extreme fire behavior for decades to come, even if planet-warming emissions are reduced, a new study has found. "Emissions reductions have a minimal impact on wildfire danger in the near term -- the next several decades," said author Patrick T. Brown, co-director of the climate and energy team at the Breakthrough Institute, a Berkeley-based think tank. "So it's important to look at more direct on-the-ground solutions to the problem like fuel reduction." Although previous studies have looked at the impact of climate change on broader metrics like annual area burned, as well on conditions that are conducive to wildfires, like aridity, the research published Wednesday in Nature drills down on how rising temperatures affected individual fires, and how they might continue to do so in the future. The researchers analyzed nearly 18,000 fires that ignited in California between 2003 and 2020.