Lifeboat Foundation

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

The behavior of the cells that make up our blood vessels is crucial to our well-being. Conditions such as inflammation, oxygen deprivation and viral infection can stress these cells and disrupt the formation of new, often pathological, blood vessels. Now a team of researchers led by Jean-Philippe Gratton, chair of the Department of Pharmacology and Physiology at Université de Montréal and a specialist in vascular biology, has discovered a previously unknown pathway leading to the formation of new blood vessels, a process known as angiogenesis.

“Compared with other traditional methods, the proposed has lower computational complexity, faster operation speed, weak influence of light, and strong ability to locate dirt,” the research group said. “The improved path planning algorithm used in this study greatly improves the efficiency of UAV inspection, saves time and resources, reduces operation and maintenance costs, and improves the corresponding operation and maintenance level of photovoltaic power generation.”

The novel approach uses mathematical morphologies for image processing, such as image enhancement, sharpening, filtering, and closing operations. It also uses image histogram equalization and edge detection, among other methods, to find the dusted spot. For path optimization, it uses an improved version of the A (A-star) algorithm.

Vishnu Reddy, Michael S. Kelley, Jessie Dotson, Davide Farnocchia, Nicolas Erasmus, David Polishook, Joseph Masiero, Lance A. M. Benner, James Bauer, Miguel R. Alarcon, David Balam, Daniel Bamberger, David Bell, Fabrizio Barnardi, Terry H. Bressi, Marina Brozovic, Melissa J. Brucker, Luca Buzzi, Juan Cano, David Cantillo, Ramona Cennamo, Serge Chastel, Omarov Chingis, Young-Jun Choi, Eric Christensen, Larry Denneau, Marek Dróżdż, Leonid Elenin, Orhan Erece, Laura Faggioli, Carmelo Falco, Dmitry Glamazda, Filippo Graziani, Aren N. Heinze, Matthew J. Holman, Alexander Ivanov, Cristovao Jacques, Petro Janse van Rensburg, Galina Kaiser, Krzysztof Kamiński, Monika K. Kamińska, Murat Kaplan, Dong-Heun Kim, Myung-Jin Kim, Csaba Kiss, Tatiana Kokina, Eduard Kuznetsov, Jeffrey A. Larsen, Hee-Jae Lee, Robert C.

Recent advances in the field of artificial intelligence (AI) and computing have enabled the development of new tools for creating highly realistic media, virtual reality (VR) environments and video games. Many of these tools are now widely used by graphics designers, animated film creators and videogame developers worldwide.

One aspect of virtual and digitally created environments that can be difficult to realistically reproduce is fabrics. While there are already various computational tools for digitally designing realistic fabric-based items (e.g., scarves, blankets, pillows, clothes, etc.), creating and editing realistic renderings of these fabrics in real-time can be challenging.

Researchers at Shandong University and Nanjing University recently introduced a new lightweight artificial neural network for the real-time rendering of woven fabrics. Their proposed network, introduced in a paper published as part of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ‘24, works by encoding the patterns and parameters of fabrics as a small latent vector, which can later be interpreted by a decoder to produce realistic representations of various fabrics.

As the name suggests, most electronic devices today work through the movement of electrons. But materials that can efficiently conduct protons—the nucleus of the hydrogen atom—could be key to a number of important technologies for combating global climate change.

Most proton-conducting inorganic materials available now require undesirably high temperatures to achieve sufficiently high conductivity. However, lower-temperature alternatives could enable a variety of technologies, such as more efficient and durable fuel cells to produce clean electricity from hydrogen, electrolyzers to make clean fuels such as hydrogen for transportation, solid-state proton batteries, and even new kinds of computing devices based on iono-electronic effects.

In order to advance the development of proton conductors, MIT engineers have identified certain traits of materials that give rise to fast proton conduction. Using those traits quantitatively, the team identified a half-dozen new candidates that show promise as fast proton conductors. Simulations suggest these candidates will perform far better than existing materials, although they still need to be conformed experimentally. In addition to uncovering potential new materials, the research also provides a deeper understanding at the atomic level of how such materials work.

Selection rules play an important role in Darwinian evolution. Now, it has been shown that selective templation enables the purification of oligomer libraries in a coacervate model, and that the oligomer library can reversibly affect the coacervates’ fusion behaviour.

Dive into our latest feature that unveils the seamless melding of the human mind with sophisticated technology. Witness how brain-machine interfaces are setting the stage for a revolution in how we interact with the digital world.

The quantum advantage, a key goal in quantum computation, is achieved when a quantum computer’s computational capability surpasses classical means. A recent study introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, which was challenged by IBM Quantum and IonQ researchers who developed a faster classical simulation algorithm. IQP circuits are beneficial due to their simplicity and moderate hardware requirements, but they also allow for classical simulation. The IQP circuit, known as the HarvardQuEra circuit, is built over n 3m 32k inputs. There are two types of simulation for quantum computations: noiseless weak/direct and noisy.

The quantum advantage is a key goal for the quantum computation community. It is achieved when a quantum computer’s computational capability becomes so complex that it cannot be reproduced by classical means. This ongoing negotiation between classical simulations and quantum computational experiments is a significant focus in the field.

A recent publication by Bluvstein et al. introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, complemented by a 48-qubit logical experimental demonstration using quantum hardware. The authors projected the simulation time to grow rapidly with the number of CNOT layers added. However, researchers from IBM Quantum and IonQ reported a classical simulation algorithm that computes an amplitude for the 48-qubit computation in only 0.00257947 seconds, which is roughly 10³ times faster than that reported by the original authors. This algorithm is not subject to a significant decline in performance due to the additional CNOT layers.