The ousted staffer said little about Musk’s plans for the company, but he praised his team for the work they did during his seven years at the tech platform.
Researchers at Princeton University have built the world’s smallest mechanically interlocked biological structure, a deceptively simple two-ring chain made from tiny strands of amino acids called peptides.
In a paper published August 23 in Nature Chemistry, the team detailed a library of such structures made in their lab—two interlocked rings, a ring on a dumbbell, a daisy chain and an interlocked double lasso—each around one billionth of a meter in size. The study also demonstrates that some of these structures can toggle between at least two shapes, laying the groundwork for a biomolecular switch.
“We’ve been able to build a bunch of structures that no one’s been able to build before,” said A. James Link, professor of chemical and biological engineering, the study’s principal investigator. “These are the smallest threaded or interlocking structures you can make out of peptides.”
A team of Cornell University engineers developed a new microscopy technique that’s powerful enough to spot an individual atom in three dimensions — and create an image so clear that the only blurriness comes from the movement of that atom itself.
The technique, which according to the study published Thursday in the journal Science relies on an electron microscope coupled with sophisticated 3D reconstruction algorithms, doesn’t just set a new record in atom resolution. The researchers even say this might be as good as microscopy gets.
“This doesn’t just set a new record,” lead author and Cornell engineer David Muller said in a press release. “It’s reached a regime which is effectively going to be an ultimate limit for resolution. We basically can now figure out where the atoms are in a very easy way. This opens up a whole lot of new measurement possibilities of things we’ve wanted to do for a very long time.”
The famed Navier-Stokes equations can lead to cases where more than one result is possible, but only in an extremely narrow set of situations.
Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.
During the training phase of Gato, data from different tasks and modalities are serialised into a flat sequence of tokens, batched, and processed by a transformer neural network similar to a large language model. The loss is masked so that Gato only predicts action and text targets.
When deploying Gato, a prompt, such as a demonstration, is tokenised, forming the initial sequence. Next, the environment yields the first observation, which is also tokenised and appended to the sequence. Gato samples the action vector autoregressively, one token at a time.
Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal gaps remain unanswered, limiting applicability and quality. We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene, (ii) introducing elements t… See more.
Recent text-to-image generation methods provide a simple yet exciting.
Conversion capability between text and image domains. While these methods have.
Continue reading “Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors” »
Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives — two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together.
A receptor that was first identified as necessary for insulin action, that also is located on the neural stem cells found deep in the brains of mice, is pivotal for brain stem cell longevity, according to a Rutgers study, a finding that has important implications for brain health and future therapies for brain disorders.
The study 0, appearing in the journal Stem Cell Reports, pinpoints a specific protein known as the insulin receptor (INSR), which is abundant on the neural stem cells that reside in the brain’s subventricular zone. During development, neural stem cells give rise to the entire nervous system, and they persist into adulthood. Over the lifespan these neural stem cells produce new neurons and non-neuronal cells that maintain the infrastructure and functioning of the brain.
Separately, the scientists made another finding when examining brain tumors: INSR plays a crucial role in sustaining and maintaining a population of specialized brain cancer cells known as glioblastoma (GBM) stem cells. When they inactivated the INSR in the GBM stem cells they inhibited the growth of those primitive tumor forming cells.
NASA and Lockheed Martin’s X-59 is nearing its first flight.
A new video from Lockheed Martin provides a new update on the X-59 aircraft it is developing in collaboration with NASA — and the development seems to be ticking along nicely.
