Abstract-- We present a computational framework for simulating filaments interacting with rigid bodies through contact. Filaments are challenging to simulate due to their codimen-sionality, i.e., they are one-dimensional structures embedded in three-dimensional space. Existing methods often assume that filaments remain permanently attached to rigid bodies. Our framework unifies discrete elastic rod (DER) modeling, a pressure field patch contact model, and a convex contact formulation to accurately simulate frictional interactions between slender filaments and rigid bodies - capabilities not previously achievable. Owing to the convex formulation of contact, each time step can be solved to global optimality, guaranteeing complementarity between contact velocity and impulse. Finally, we demonstrate its applicability in both soft robotics, such as a stochastic filament-based gripper, and deformable object manipulation, such as shoelace tying, providing a versatile simulator for systems involving complex filament-filament and filament-rigid body interactions.

Entities and events are crucial to natural language reasoning and common in procedural texts. Existing work has focused either exclusively on entity state tracking (e.g., whether a pan is hot) or on event reasoning (e.g., whether one would burn themselves by touching the pan), while these two tasks are often causally related. We propose CREPE, the first benchmark on causal reasoning of event plausibility and entity states. We show that most language models, including GPT-3, perform close to chance at .35 F1, lagging far behind human at .87 F1. We boost model performance to .59 F1 by creatively representing events as programming languages while prompting language models pretrained on code. By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to .67 F1. Our findings indicate not only the challenge that CREPE brings for language models, but also the efficacy of code-like prompting combined with chain-of-thought prompting for multihop event reasoning.

Machine learning has been making plenty of headlines in the past few years. Rightfully so, even though headlines tend to oversell. Advances in computing power, algorithmic complexity, data handling capacities, and models of learning mean that machine learning/AI is increasingly being used in many fields. In previous posts, I have written about machine learning/AI in general science and art, but also more specifically in (warning, link fest) historical research, genetic enhancement, mental health, aging research (including the development of'aging clocks'), video game ecology, Hollywood, astrobiology, epidemiology, stock markets, and the job market. Plenty of AI to go around, it seems.

Google sees local services as a path to peer-to-peer conversations to gain more data about people, places and things. Some think the company has added a social layer to local services like Google My Business and Google Maps. Blumenthal, the proprietor at Understanding Google My Business & Local Search, a blog that focuses on local and businesses, said it's all about the data. Focusing on local just gives them much more. To back his hypothesis, he points to some recent changes such as Google adding the ability for people to follow businesses on Maps, and giving businesses the ability to respond to followers with special deal.

How can I help you?" "So, how do we get to the restaurant? I want to have breakfast" "The elevators to the rooms are in 100 meters on the right-hand side. Just € 139 if you decide right now …" Ten years ago, let's say, it still sounded like something from Frankenstein. Today, up and down the country we are dreaming of the infinite expanses that AI will open up for us. To me, this cocktail tastes like a shot of limitlessness and a hefty dose of "Anything is possible". We're seeing the first critical voices, who want to see regulations for the use of AI and clearer monitoring.

People with six fingers on each hand may have trouble buying gloves, but new research shows they are better at many tasks than those with just five. Researchers found makers of robots should consider giving their creations six fingers. In a study, two people, a German mother and son, both with six fingers on each hand, were given a variety of physical tasks to carry out. They found that they could carry out many tasks, such as tying a shoelace, with just one hand, rather than two. In a study, two people, a German mother and son, both with six fingers on each hand, were given a variety of physical tasks to carry out.

Fret no longer children of planet Earth, as new research from the University of California, Berkeley, has figured out the physics behind why shoelace knots fail and why some shoelaces are more prone to the mistake. No matter how tight you tug, it feels like some shoelaces are doomed to come untied. Fret no longer, as new research from the University of California, Berkeley, has figured out the physics behind why the knots fail and why some shoelaces are more prone to the mistake. While the poetic inevitability of the slipup may provide comfort to those afflicted by wayward shoelaces, the research published Tuesday in the Proceedings of the Royal Society of London A may also provide clues for building soft, lifelike robots. Mechanical engineer Oliver O'Reilly began looking into this telltale problem three years ago, after trying to teach his young daughter to tie her shoes.