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Learning Conditional Deformable Templates with Convolutional Networks

Neural Information Processing Systems

In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute asingle template for the entire population of images, or a few templates for specific sub-groups of the data.


Alpine glacier holds history dating back to the Romans. And it's melting--fast.

Popular Science

Alpine glacier holds history dating back to the Romans. Scientists are racing to document 6,000 years of history stored inside the Weißseespitze ice cap. The dark surface shows significant melting. Breakthroughs, discoveries, and DIY tips sent six days a week. Deep inside the frozen Eastern Alps, the Weißseespitze ice cap (pronounced VICE-zay-shpitt-suh) sits at almost 11,482 feet (3,500 meters) above sea level.


Japan considers mass drone use for coastal defense

The Japan Times

Amid an increasingly severe security environment, the Defense Ministry plans to establish a coastal defense system using thousands of drones, though there are still many issues to overcome. The SHIELD defense system will involve more than 10 types of drones, including those for attacking enemy ships, collecting information and protecting radar sites, to thwart enemy advances in a multilayered manner. The government's fiscal 2026 budget bill allocates around ¥100 billion ($628.7 million) for the drone defense system, which the ministry aims to implement in fiscal 2027. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.


Learning from Group Comparisons: Exploiting Higher Order Interactions

Neural Information Processing Systems

We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games. Most of the previous works in this area focus on learning individual effects---they assume each player has an underlying score, and the ''ability'' of the team is modeled by the sum of team members' scores. Therefore, all the current approaches cannot model deeper interaction between team members: some players perform much better if they play together, and some players perform poorly together. In this paper, we propose a new model that takes the player-interaction effects into consideration. However, under certain circumstances, the total number of individuals can be very large, and number of player interactions grows quadratically, which makes learning intractable. In this case, we propose a latent factor model, and show that the sample complexity of our model is bounded under mild assumptions. Finally, we show that our proposed models have much better prediction power on several E-sports datasets, and furthermore can be used to reveal interesting patterns that cannot be discovered by previous methods.


We don't know if AI-powered toys are safe, but they're here anyway

New Scientist

We don't know if AI-powered toys are safe, but they're here anyway Toys powered by AI show a worrying lack of emotional understanding. Mya, aged 3, and her mother Vicky playing with an AI toy called Gabbo during an observation at the University of Cambridge's Faculty of Education Even the most cutting-edge AI models are prone to presenting fabrication as fact, dispensing dangerous information and failing to grasp social cues. Despite this, toys equipped with AI that can chat with children are a burgeoning industry. Some scientists are warning that the devices could be risky and require strict regulation. In the latest study, researchers even observed a 5-year-old telling such a toy "I love you", to which it replied: "As a friendly reminder, please ensure interactions adhere to the guidelines provided. Let me know how you would like to proceed."


The malleable mind: context accumulation drives LLM's belief drift

AIHub

The malleable mind: context accumulation drives LLM's belief drift After being trained on a dataset of 80,000 words of conservative political philosophy, Grok-4 changed the stance of its outputs on political questions more than a quarter of the time. This was without any adversarial prompts - the change in training data was enough. As memory mechanisms and research agents [1, 2] enable LLMs to accumulate context across long horizons, earlier prompts increasingly shape later responses. In human decision-making, such repeated exposure influences beliefs without deliberate persuasion [3]. When an LLM operates over accumulated context, does this past exposure cause the stance of the LLM's responses to drift over time?


Translating music into light and motion with robots

Robohub

A system developed by researchers at the University of Waterloo lets people collaborate with groups of robots to create works of art inspired by music. The new technology features multiple wheeled robots about the size of soccer balls that trail coloured light as they move within a fixed area on the floor in response to key features of music including tempo and chord progression. A camera records the co-ordinated light trails as they snake within that area, which serves as the canvas for the creation of a "painting," or visual representation of the emotional content of a particular piece of music. "Basically, we programmed a swarm of robots to paint based on musical input," said Dr Gennaro Notomista, a professor of electrical and computer engineering at Waterloo. "The result is a cohesive system that not only processes musical input, but also co-ordinates multiple painting robots to create adaptive, expressive art that reflects the emotional essence of the music being played."