Experiment in one of the world's quietest rooms reveals the hairs detect airborne sounds--like predators. Breakthroughs, discoveries, and DIY tips sent six days a week. Have you ever walked into a room full of caterpillars? While the answer for most people is probably no, those of us who have may have noticed the insects reacting to the sound of your voice. That's what happened to Carol Miles, a biologist at Binghamton University in New York.

Macroeconomic conditions influence the environments in which health systems operate, yet their value as leading signals of health system capacity has not been systematically evaluated. In this study, we examine whether selected macroeconomic indicators contain predictive information for several capacity-related public health targets, including employment in the health and social assistance workforce, new business applications in the sector, and health care construction spending. Using monthly U.S. time series data, we evaluate multiple forecasting approaches, including neural network models with different optimization strategies, generalized additive models, random forests, and time series models with exogenous macroeconomic indicators, under alternative model fitting designs. Across evaluation settings, we find that macroeconomic indicators provide a consistent and reproducible predictive signal for some public health targets, particularly workforce and infrastructure measures, while other targets exhibit weaker or less stable predictability. Models emphasizing stability and implicit regularization tend to perform more reliably during periods of economic volatility. These findings suggest that macroeconomic indicators may serve as useful upstream signals for digital public health monitoring, while underscoring the need for careful model selection and validation when translating economic trends into health system forecasting tools.

Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across different experimental settings. However, it is not always practical or desirable to estimate a causal model at the granularity of given features in a particular dataset. There is a growing body of research on causal abstraction to address such problems. We contribute to this line of research by (i) providing novel graphical identifiability results for practically-relevant interventional settings, (ii) proposing an efficient, provably consistent algorithm for directly learning abstract causal graphs from interventional data with unknown intervention targets, and (iii) uncovering theoretical insights about the lattice structure of the underlying search space, with connections to the field of causal discovery more generally. As proof of concept, we apply our algorithm on synthetic and real datasets with known ground truths, including measurements from a controlled physical system with interacting light intensity and polarization.

Science Archaeology 3D map of Easter Island takes you places visitors aren't allowed One of the world's most isolated islands is open to virtual tourists. Breakthroughs, discoveries, and DIY tips sent every weekday. Nestled in the South Pacific Ocean, some 6,000 people live on the most isolated, inhabited island in the world: Rapa Nui. Known to many as Easter Island, a name Dutch explorer Jacob Roggeveen coined after landing on the island on Easter Sunday 1722, Rapa Nui is roughly double the size of Disney World, or 63.2 square miles. And every year, some 100,000 people visit the remote island to see the famed 13-foot-tall moai statues or Easter Island heads .

The line between human and machine authorship is blurring, particularly as it's become increasingly difficult to tell whether something was written by a person or AI. Now, in what may seem like a tipping point, the digital marketing firm Graphite recently published a study showing that more than 50% of articles on the web are being generated by artificial intelligence. As a scholar who explores how AI is built, how people are using it in their everyday lives, and how it's affecting culture, I've thought a lot about what this technology can do and where it falls short. If you're more likely to read something written by AI than by a human on the internet, is it only a matter of time before human writing becomes obsolete? Or is this simply another technological development that humans will adapt to?

Domain-specific question answering (QA) systems for services face unique challenges in integrating heterogeneous knowledge sources while ensuring both accuracy and safety. Existing large language models often struggle with factual consistency and context alignment in sensitive domains such as healthcare policies and government welfare. In this work, we introduce Knowledge-Aware Reasoning and Memory-Augmented Adaptation (KARMA), a novel framework designed to enhance QA performance in care scenarios. KARMA incorporates a dual-encoder architecture to fuse structured and unstructured knowledge sources, a gated memory unit to dynamically regulate external knowledge integration, and a safety-aware controllable decoder that mitigates unsafe outputs using safety classification and guided generation techniques. Extensive experiments on a proprietary QA dataset demonstrate that KARMA outperforms strong baselines in both answer quality and safety. This study offers a comprehensive solution for building trustworthy and adaptive QA systems in service contexts.

Karoline Leavitt's family member was swarmed by ICE agents while picking up son from school as child's father tell her to'self deport' Deaths from highly infectious virus are growing... as states brace for widespread outbreaks My book on the Kennedys was used as a'mistress manual' by Olivia Nuzzi... then this wannabe Carolyn Bessette had the nerve to hound me with these outrageous texts: MAUREEN CALLAHAN Katy Perry's legal victory as judge orders disabled veteran to pay singer nearly $2m over Montecito mansion Trump reveals next DC renovation project to remove'Biden filth' after White House ballroom Cracker Barrel CEO whines that she got'fired by America' for woke redesign Kroger employee reveals shocking amount laundry products have increased by... 'biggest price jump I've seen in a single week' Hollywood heir, 23, whose mom Anne Heche died in horror car fireball has secret LOVE CHILD with 43-year-old... now she's telling all Missing Melodee Buzzard's mom'left her daughter with strangers she met at the zoo' Rachel Zoe reveals why she dumped husband of 26 years... and if she has started dating again Horrific moment cops found body of Cowboys star Marshawn Kneeland after he shot himself at end of 145 mph chase'This is pretty lurid' Jenny McCarthy, 53, reveals health emergency that involved NINE surgeries, her'teeth falling out' and'growth' on her eyeballs Maryland grandma, 58, dragged across floor after being deported to country she'has never even visited' READ MORE: New'stone head' statue mysteriously appears on Easter Island One of the biggest mysteries surrounding Easter Island may finally be solved - as scientists pinpoint who built the iconic stone heads over 900 years ago. In the past, researchers assumed that the 12 to 80-ton statues would have required the combined efforts of hundreds of labourers to build and move. However, new archaeological evidence shows that the statues, known as moai, were not carved by a single powerful chiefdom. Instead, each moai was carved by a small clan or by an individual family, with as few as four to six people working on a single statue. Using a new 3D model of the island's main moai quarry, which you can explore below, archaeologists identified 30 unique'workshops' where the statues were produced.

Test-time thinking (that is, generating explicit intermediate reasoning chains) is known to boost performance in large language models and has recently shown strong gains for large vision language models (LVLMs). However, despite these promising results, there is still no systematic analysis of how thinking actually affects visual reasoning. We provide the first such analysis with a large scale, controlled comparison of thinking for LVLMs, evaluating ten variants from the InternVL3.5 and Qwen3-VL families on MMMU-val under generous token budgets and multi pass decoding. We show that more thinking is not always better; long chains often yield long wrong trajectories that ignore the image and underperform the same models run in standard instruct mode. A deeper analysis reveals that certain short lookback phrases, which explicitly refer back to the image, are strongly enriched in successful trajectories and correlate with better visual grounding. Building on this insight, we propose uncertainty guided lookback, a training free decoding strategy that combines an uncertainty signal with adaptive lookback prompts and breadth search. Our method improves overall MMMU performance, delivers the largest gains in categories where standard thinking is weak, and outperforms several strong decoding baselines, setting a new state of the art under fixed model families and token budgets. We further show that this decoding strategy generalizes, yielding consistent improvements on five additional benchmarks, including two broad multimodal suites and math focused visual reasoning datasets.

Large language models (LLMs) have raised hopes for automated end-to-end fact-checking, but prior studies report mixed results. As mainstream chatbots increasingly ship with reasoning capabilities and web search tools -- and millions of users already rely on them for verification -- rigorous evaluation is urgent. We evaluate 15 recent LLMs from OpenAI, Google, Meta, and DeepSeek on more than 6,000 claims fact-checked by PolitiFact, comparing standard models with reasoning- and web-search variants. Standard models perform poorly, reasoning offers minimal benefits, and web search provides only moderate gains, despite fact-checks being available on the web. In contrast, a curated RAG system using PolitiFact summaries improved macro F1 by 233% on average across model variants. These findings suggest that giving models access to curated high-quality context is a promising path for automated fact-checking.

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