Lifeboat Foundation

A review of syntheticapertureradar image formation algorithms and implementations: a computational perspective.

✍️ Helena Cruz et al.

Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for instance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations.

The field of artificial intelligence (AI) has witnessed extraordinary advancements in recent years, ranging from natural language processing breakthroughs to the development of sophisticated robotics. Among these innovations, multi-agent systems (MAS) have emerged as a transformative approach for solving problems that single agents struggle to address. Multi-agent collaboration harnesses the power of interactions between autonomous entities, or “agents,” to achieve shared or individual objectives. In this article, we explore one specific and impactful technique within multi-agent collaboration: role-based collaboration enhanced by prompt engineering. This approach has proven particularly effective in practical applications, such as developing a software application.

Originally published on Towards AI.

One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Or how can you trust your corporate AI assistant if it makes things up randomly?

That’s a problem, especially given that an LLM can’t be fired or held accountable.

Researchers at MIT are developing innovative agricultural technologies such as stress-signaling plants, microbial fertilizers, and protective seed coatings to adapt farming to climate change and enhance food security.

With global temperatures on the rise, agricultural practices must adapt to new challenges. Climate change is expected to increase the frequency of droughts, and some land may no longer be arable. Additionally, it is becoming increasingly difficult to feed an ever-growing population without expanding the production of fertilizer and other agrochemicals, which have a large carbon footprint that is contributing to global warming.

Continue reading “Glowing Plants and Silk-Coated Seeds: How MIT Is Developing the Future of Farming” »

Freethink spoke to Onion staffers about parodying news from the print era into the digital age.

A research team led by Professor Kim Kyuhyung at the Department of Brain Sciences, DGIST, has discovered a new principle that regulates how food moves through the digestive tract and is swallowed. They found that the Piezo channel proteins sense the pressure generated when food accumulates at the front of the digestive tract, triggering swallowing behavior.

This discovery is expected to provide important clues in developing treatments for digestive and eating disorders. The work is published in the journal Nature Communications.

When we eat, the digestive tract generates various signals that can be linked to important physiological processes. However, our understanding of how the movement and accumulation of food in the digestive tract are sensed and processed to regulate important food intake behaviors like swallowing remains limited.

Eddie Gonzales Jr. – AncientPages.com – Despite awe-inspiring diversity, nearly every lifeform – from bacteria to blue whales – shares the same genetic code. How and when this code came about has been the subject of much scientific controversy.

Image credit: Adobe Stock – Diatomic

Taking a fresh approach at an old problem, Sawsan Wehbi, a doctoral student in the Genetics Graduate Interdisciplinary Program at the University of Arizona, discovered strong evidence that the textbook version of how the universal genetic code evolved needs revision. Wehbi is the first author of a study published in the journal PNAS suggesting the order with which amino acids – the code’s building blocks – were recruited is at odds with what is widely considered the “consensus” of genetic code evolution.

We now have a new idea of what photons look like from a model that predicts how these particles of light interact with their environment.

PlantRNA-FM, an AI model trained on RNA data from over 1,100 plants, decodes genetic patterns to advance plant science, improve crops, and tackle global agricultural challenges.

A groundbreaking Artificial Intelligence (AI) model designed to decode the sequences and structural patterns that form the genetic “language” of plants has been launched by a research collaboration.

Named Plant RNA-FM, this innovative model is the first of its kind and was developed by a partnership between plant researchers at the John Innes Centre and computer scientists at the University of Exeter.