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1714 matching published research entries
Science · 2026-10-28

Scientists discover the immune system’s hidden sense of touch

Scientists at McGill University have discovered a surprising new ability of immune cells that could change how we understand long-term immunity. Researchers found that T cells can actually sense how stiff their surrounding tissues are, using these physical signals to help determine whether they develop into long-lasting memory cells that protect against future infections.

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Science · 2026-10-28

Scientists discover a secret glow in toxic fire salamanders

Researchers have discovered a surprising hidden ability in Europe's familiar black-and-yellow fire salamanders: they glow green and cyan under ultraviolet light. The mysterious fluorescence comes mainly from glands in their skin and their toxic secretions, which can continue glowing for more than 24 hours after being released.

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Science · 2026-10-28

The autistic brain processes faces differently than scientists thought

A major new study reveals that autistic children’s brains process faces differently, and those differences may begin earlier than scientists previously thought. Researchers found that brain signals associated with recognizing faces become more specialized as neurotypical children grow older, but show much less change in autistic children.

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Space & Discovery · 2026-10-28

Artemis III SLS Core Stage Joins Boosters at Kennedy

NASA’s Artemis III SLS (Space Launch System) core stage is integrated alongside the twin solid rocket boosters on Oct. 28 inside the Vehicle Assembly Building’s High Bay 3 at NASA’s Kennedy Space Center in Florida. Technicians lifted the 212-foot rocket stage from the facility’s High Bay 2 using a one-of-a kind lifting beam designed […]

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Space & Discovery · 2026-10-28

NASA University Challenge Ignites Trajectory of Drone Entrepreneur

Five years ago, Gavin Gardner was a Michigan State University engineering student researching next-generation drone technology as part of a NASA grant. Garder expected the research to be demanding – but he was surprised that navigating the financial side of the work presented an even greater challenge. Although Gardner was used to leading fellow students […]

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Space & Discovery · 2026-10-28

NASA Selects 19 New Citizen Science Proposals in 2026

So far, 2026 has seen a flood of successes for volunteers who do NASA science, exemplifying NASA’s commitment to gold-standard science—transparent, reproducible and intrinsically collaborative. The Backyard Worlds: Planet 9 project announced that their project volunteers have now discovered most of the known brown dwarfs, celestial objects that are similar to planets. The Zooniverse platform, […]

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Space & Discovery · 2026-10-28

Webb Sees Turbulent Star Nursery

In this Webb image released on Oct. 22, 2026, we see a turbulent cauldron of star formation filled with dense clumps of gas. This region is named N159 and is located 161,000 light-years away in the Large Magellanic Cloud, one of the satellite galaxies orbiting the Milky Way galaxy. This region has a lower metallicity […]

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Space & Discovery · 2026-10-28

Curiosity Blog, Sols 5043-5049: Preparing to Drill at ‘Nazca’

Written by Lucy Lim, Planetary Scientist at NASA’s Goddard Space Flight Center Earth planning date: Friday, Oct. 16, 2026 Following the Basque Lakes drill campaign, Curiosity has returned to a spot just a few meters away from the “Chiu Chiu” block to conduct our next drill campaign. “Chiu Chiu” itself wasn’t a good candidate for […]

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Machine Learning · 2026-10-28

Bridging Exploration and Loss of Plasticity in On-Policy Reinforcement Learning

arXiv:2610.36309v1 Announce Type: new Abstract: Deep reinforcement learning (RL) algorithms have been observed to lose the ability to adaptively learn from new data, a phenomenon termed Loss of Plasticity (LoP). While many metrics have been shown to correlate with it, the causal mechanisms and the geometric conditions from which it can arise have not been clearly identified.

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Machine Learning · 2026-10-28

EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation

arXiv:2610.36280v1 Announce Type: new Abstract: Molecular 3D structures underpin simulations of thermodynamics, kinetics, crystallization, and nucleation, making their accurate prediction central to drug discovery and materials design. However, molecular geometry can change dramatically across environments. Existing models either ignore environmental effects or train separate models for each environment.

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Machine Learning · 2026-10-28

Freeze, Don't Clone: Fine-Tuning with Expressive Policies Without Retaining Offline Data

arXiv:2610.36247v1 Announce Type: new Abstract: Offline-to-online reinforcement learning uses offline pretraining to accelerate subsequent online learning. While many methods assume the offline dataset remains available throughout fine-tuning, there are many settings where this is not the case. Prior work attributes failures in this setting primarily to critic recalibration under distribution shift.

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Machine Learning · 2026-10-28

Base Models Can Detect Hidden Intentions that Prompted Classifiers Miss

arXiv:2610.36202v1 Announce Type: new Abstract: The Persona Selection Model (PSM) posits that post-training selects a persona the model learned during pre-training. Work on subliminal learning has shown that models can impart covert signals (like "love of owls") into semantically unrelated outputs like strings of numbers, and that students trained on these outputs inherit those traits.

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Machine Learning · 2026-10-28

Dynamical coarse-graining of neural-network training

arXiv:2610.36180v1 Announce Type: new Abstract: Dynamical coarse-graining of neural networks gives macroscopic observables and enables training to be predicted, and controlled in a state space far smaller than the parameter space. In this paper, we first define the learning operator following the training law of neural networks and establish a descent theorem for the learning operator, which serves to determine…

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Machine Learning · 2026-10-28

SpatialPC: Deep Computation with Sublinear Local Credit Propagation

arXiv:2610.36139v1 Announce Type: new Abstract: On a chain of $N$ states, any local learning rule with bounded communication range needs $\Theta(N)$ rounds before the loss can influence the earliest parameters, and this latency is usually attributed to depth. We show that it is a property of the communication geometry rather than of depth.

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