The authors present DPAD, a deep learning method, for dynamical neural–behavioral modeling. It dissociates behaviorally relevant neural dynamics, better predicts neural–behavioral data and reveals insight into where their nonlinearities can be isolated.
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Remote entanglement is crucial for quantum computing, sensing, and communication. Traditional methods for entanglement generation often depend on direct interactions between quantum bits (qubits) or the exchange of entangled photons. In this study, we demonstrate an alternative approach, where we create and preserve entanglement between two noninteracting qubits through dissipation into a shared waveguide.
While dissipation is typically viewed as detrimental, tailored dissipation can be harnessed to drive a system into complex quantum states while actively protecting it from decoherence. This approach, known as autonomous stabilization, has been previously used to create entanglement. However, entanglement stabilization has been confined to short distances due to the challenge of engineering shared dissipation between remote sites. Our experiment overcomes this challenge by employing an open waveguide as a one-dimensional photonic bath. We demonstrate that, under appropriate conditions, the interference of photons emitted into a waveguide from two qubits can stabilize them in an entangled stationary state when the qubits are strongly driven. Crucially, we can reconstruct the entangled state despite significant waveguide-induced dissipation by measuring the emitted photons. Our demonstration is made possible by precise control over qubit frequencies and efficient qubit-waveguide interfaces in superconducting circuits.
One contract focuses on Canopy’s transpiration-cooled TBS. Under a second contract, Canopy will embed high-temperature sensors in the TPS material.
Denver-based Canopy was founded in 2021 to develop manufacturing processes that rely on software, automation and 3D-printing to supply heat shields for spacecraft and hypersonic vehicles.
Researchers from LMU, the ORIGINS Excellence Cluster, the Max Planck Institute for Extraterrestrial Physics (MPE), and the ORIGINS Data Science Lab (ODSL) have made an important breakthrough in the analysis of exoplanet atmospheres.
As Shumer told VentureBeat over DM: “I’ve been thinking about this idea for months now. LLMs hallucinate, but they can’t course-correct. What would happen if you taught an LLM how to recognize and fix its own mistakes?”
Hence the name, “Reflection” — a model that can reflect on its generated text and assess its accuracy before delivering it as outputs to the user.
The model’s advantage lies in a technique called reflection tuning, which allows it to detect errors in its own reasoning and correct them before finalizing a response.
Researchers from the University of Pisa developed a quantum subroutine to improve matrix multiplication for AI and machine learning applications.
When you multiply two large matrices—this is a common task in fields like machine learning, but it can be time-consuming, even for powerful computers…
In a recent study published in IEEE Access, a team of researchers from the University of Pisa introduced a quantum subroutine designed to streamline matrix multiplication. This subroutine is a new feature in the toolbox of matrix multiplication that could improve computational efficiency, particularly in applications like machine learning and data processing.
Podcaster clones his voice, hooks it up to ChatGPT, & the bot does his phone calls & interviews.
One man secretly hands off more and more of his life to an AI voice clone.
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.
How an alga synchronizes its two flapping cilia to propel itself is revealed in a tabletop experiment with chains of mobile robots.
The freshwater alga Chlamydomonas reinhardtii swims by flapping its two cilia in a motion akin to the breaststroke. Unlike a human, C. reinhardtii lacks a brain to coordinate its limbs. The synchronization is automatic. To uncover its origin, Mingcheng Yang of the Institute of Physics of the Chinese Academy of Sciences and his collaborators built mechanical algae whose cilia are made of chains of cockroach-sized toy robots [1]. By adjusting the cilia’s flapping frequency and other parameters, the researchers reproduced the alga’s swimming gaits and identified the conditions that favor them.
Yang’s mechanical algae each consists of a puck-like base, on the sides of which are attached two chains of four robots. Each robot’s underside bristles with elastic hairs set at an angle. When a mechanical alga is placed on a tabletop and an internal electric motor is switched on, each bristly robot vibrates vertically. On the upstroke, the hairs push the robot toward the base, setting up the possibility that the chains could buckle.
Break it down: How AI can learn from the brain.
In a recent paper titled “A sensory-motor theory of the neocortex” published in the journal Nature Neuroscience, Rao posited that the brain uses active predictive coding (APC) to understand the world and break down complicated problems into simpler…
When you reach out to pet a dog, you expect it to feel soft. If it doesn’t feel like how you expect, your brain uses that feedback to inform your next action — maybe you pull your hand away. Previous models of how the brain works have typically separated perception and action. For Allen School professor Rajesh Rao, those two processes are closely intertwined, and their relationship can be mapped using a computational algorithm.
