Lifeboat Foundation

A wheeled bot rolls across the floor. A soft-bodied robotic star bends its five legs, moving with an awkward shuffle.

Powered by conventional electricity via plug or battery, these simple robotic creations would be unremarkable, but what sets these two robots apart is that they are controlled by a living entity: a king oyster mushroom.

By growing the mushroom’s mycelium, or rootlike threads, into the robot’s hardware, a team led by Cornell University researchers has engineered two types of robots that sense and respond to the environment by harnessing electrical signals made by the fungus and its sensitivity to light.

White House declares BGP security issues a national priority – BGP handles routing for the entire internet.

A Dangerous Network: The Border Gateway Protocol has been the primary routing technology for the internet for at least three decades. Like other fundamental internet protocols developed in the 1980s, BGP was not originally designed with security in mind – and it shows.

After numerous incidents related to traffic routing among different autonomous systems, the White House has decided to address the security issues of the Border Gateway Protocol. The US administration has tasked the White House Office of the National Cyber Director with developing a roadmap to enhance the security of routing procedures managed through BGP.

Continue reading “White House declares BGP security issues a national priority — BGP handles routing for the entire internet” »

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.

Popular Summary.

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.

Continue reading “Matrix Re-Reloaded: Quantum Subroutine Improves Efficiency of Matrix Multiplication for AI and Machine Learning Applications” »