Lifeboat Foundation

As a supplement to optical super-resolution microscopy techniques, computational super-resolution methods have demonstrated remarkable results in alleviating the spatiotemporal imaging trade-off. However, they commonly suffer from low structural fidelity and universality. Therefore, we herein propose a deep-physics-informed sparsity framework designed holistically to synergize the strengths of physical imaging models (image blurring processes), prior knowledge (continuity and sparsity constraints), a back-end optimization algorithm (image deblurring), and deep learning (an unsupervised neural network). Owing to the utilization of a multipronged learning strategy, the trained network can be applied to a variety of imaging modalities and samples to enhance the physical resolution by a factor of at least 1.67 without requiring additional training or parameter tuning.

Just how secure are your passwords? Not very, if an attacker uses a smart-guessing password-cracking algorithm, according to a new Kaspersky analysis.

As information technology is moving toward the era of big data, the traditional Von-Neumann architecture shows limitations in performance. The field of computing has already struggled with the latency and bandwidth required to access memory (“the memory wall”) and energy dissipation (“the power wall”). These challenging issues, such as “the memory bottleneck,” call for significant research investments to develop a new architecture for the next generation of computing systems. Brain-inspired computing is a new computing architecture providing a method of high energy efficiency and high real-time performance for artificial intelligence computing. Brain-inspired neural network system is based on neuron and synapse. The memristive device has been proposed as an artificial synapse for creating neuromorphic computer applications. In this study, post-silicon nano-electronic device and its application in brain-inspired chips are surveyed. First, we introduce the development of neural networks and review the current typical brain-inspired chips, including brain-inspired chips dominated by analog circuit and brain-inspired chips of the full-digital circuit, leading to the design of brain-inspired chips based on post-silicon nano-electronic device. Then, through the analysis of N kinds of post-silicon nano-electronic devices, the research progress of constructing brain-inspired chips using post-silicon nano-electronic device is expounded. Lastly, the future of building brain-inspired chips based on post-silicon nano-electronic device has been prospected.

Keywords: brain-inspired chips; neuron; phase change memory; post-silicon nano-electronic device; resistive memory; synapse.

Copyright © 2022 Lv, Chen, Wang, Li, Xie and Song.

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It reads. It talks. It collates mountains of data and recommends business decisions. Today’s artificial intelligence might seem more human than ever. However, AI still has several critical shortcomings.

Those who rush to leverage AI’s power without adequate preparation face difficult blowback, scandals, and could provoke harsh regulatory measures. However, those who have a balanced, informed view on the risks and benefits of AI, and who, with care and knowledge, avoid either complacent optimism or defeatist pessimism, can harness AI’s potential, and tap into an incredible variety of services of an ever-improving quality.

These are some words from the introduction of the new book, “Taming the machine: ethically harness the power of AI”, whose author, Nell Watson, joins us in this episode.

Continue reading “Taming the Machine, with Nell Watson” »

Lakes and seas of liquid methane exist on Saturn’s largest moon, Titan, due to the moon’s bone-chilling cold temperatures at-290 degrees Fahrenheit (−179 degrees Celsius), whereas it can only exist as a gas on Earth. But do these lakes and seas of liquid methane strewn across Titan’s surface remain static, or do they exhibit wave activity like the lakes and seas of liquid water on Earth? This is what a recent study published in Science Advances hopes to address as a team of researchers have investigated coastal shoreline erosion on Titan’s surface resulting from wave activity. This study holds the potential to help researchers better understand the formation and evolution of planetary surfaces throughout the solar system and how well they relate to Earth.

For the study, the researchers used a combination of shoreline analogs on Earth, orbital images obtained by NASA’s now-retired Cassini spacecraft, coastal evolution models, and several mathematical equations to ascertain the processes responsible for shoreline morphology across Titan’s surface. Through this, the researchers were able to construct coastal erosion models depicting how wave activity could be responsible for changes in shoreline morphology at numerous locations across Titan’s surface.

“We can say, based on our results, that if the coastlines of Titan’s seas have eroded, waves are the most likely culprit,” said Dr. Taylor Perron, who is a Cecil and Ida Green Professor of Earth, Atmospheric and Planetary Sciences at the Massachusetts Institute of Technology and a co-author on the study. “If we could stand at the edge of one of Titan’s seas, we might see waves of liquid methane and ethane lapping on the shore and crashing on the coasts during storms. And they would be capable of eroding the material that the coast is made of.”

The use of AI to streamline drug discovery is exploding. Researchers are deploying machine-learning models to help them identify molecules, among billions of options, that might have the properties they are seeking to develop new medicines.

A connection between time-varying networks and transport theory opens prospects for developing predictive equations of motion for networks.

Many real-world networks change over time. Think, for example, of social interactions, gene activation in a cell, or strategy making in financial markets, where connections and disconnections occur all the time. Understanding and anticipating these microscopic kinetics is an overarching goal of network science, not least because it could enable the early detection and prevention of natural and human-made disasters. A team led by Fragkiskos Papadopoulos of Cyprus University of Technology has gained groundbreaking insights into this problem by recasting the discrete dynamics of a network as a continuous time series [1] (Fig. 1). In doing so, the researchers have discovered that if the breaking and forming of links are represented as a particle moving in a suitable geometric space, then its motion is subdiffusive—that is, slower than it would be if it diffused normally.