From Microsoft MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels.
From Microsoft.
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels.
Since the first microbial genome was sequenced in 1995, scientists have reconstructed the genomic makeup of hundreds of thousands of microorganisms and have even devised methods to take a census of bacterial communities on the skin, in the gut, or in soil, water and elsewhere based on bulk samples, leading to the emergence of a relatively new field of study known as metagenomics.
In a milestone, supermaterials trailblazer Lyten has shipped lithium-sulfur (Li-S) batteries to Stellantis and other US and EU OEMs for testing.
Lyten’s shipment of A samples of its 6.5 Ah Li-S pouch cells is the first major step in the commercial evaluation of lithium-sulfur batteries by leading US and European automakers. Stellantis announced that it had invested in Lyten’s lithium-sulfur battery development in May 2023.
“This milestone is the result of years of dedicated work and innovation from the Lyten team, and we are just at the start of further expanding the capabilities of our lithium-sulfur battery cells,” said Lyten CEO and cofounder Dan Cook.
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**Breakthrough*** Clinical trial shows promising results.
Opal Sandy from Oxfordshire is the first patient treated in a global gene therapy trial, which shows ‘mind-blowing’ results.
A baby girl born deaf can hear unaided for the first time, after receiving gene therapy when she was 11 months old at Addenbrooke’s Hospital in Cambridge.
Continue reading “Baby born deaf can hear after breakthrough gene therapy” »
Graph Neural Networks (GNNs) are crucial in processing data from domains such as e-commerce and social networks because they manage complex structures. Traditionally, GNNs operate on data that fits within a system’s main memory. However, with the growing scale of graph data, many networks now require methods to handle datasets that exceed memory limits, introducing the need for out-of-core solutions where data resides on disk.
Despite their necessity, existing out-of-core GNN systems struggle to balance efficient data access with model accuracy. Current systems face a trade-off: either suffer from slow input/output operations due to small, frequent disk reads or compromise accuracy by handling graph data in disconnected chunks. For instance, while pioneering, these challenges have limited previous solutions like Ginex and MariusGNN, showing significant drawbacks in training speed or accuracy.
The DiskGNN framework, developed by researchers from Southern University of Science and Technology, Shanghai Jiao Tong University, Centre for Perceptual and Interactive Intelligence, AWS Shanghai AI Lab, and New York University, emerges as a transformative solution specifically designed to optimize the speed and accuracy of GNN training on large datasets. This system utilizes an innovative offline sampling technique that prepares data for quick access during training. By preprocessing and arranging graph data based on expected access patterns, DiskGNN reduces unnecessary disk reads, significantly enhancing training efficiency.
Efficient. Fast. Autonomous. And one day it will erase humans: #AI I personally always said there is another perspective to artificial intelligence and the only thing that is super is the outcome for humans. Philosopher Nick Bostom has a new book, and it’s finally acknowledging the potential of a harmonious human-AI relationship and its problem solving capabilities. AI = augmented intelligence #design #ai #problemsolving #innovation #creativeai
A team of researchers, led by Professor Dong Eon Kim from Pohang University of Science and Technology and Professor X. Lai at the Innovation Academy for Precision Measurement Science and Technology, has made significant strides in ultrafast imaging. They have successfully observed two distinct holographic patterns—resembling spider legs and fishbones—within molecules for the first time. […].
The very lowest frequencies of the radio Universe have just been revealed in spectacular clarity.
A team of astronomers has used a new calibration technique to give us the first sharp images of the radio Universe in the frequency range of 16–30 megahertz – an achievement previously thought impossible, due to the turbulent interference generated by Earth’s ionosphere.
“It’s like putting on a pair of glasses for the first time and no longer seeing blurred,” says astronomer Christian Groeneveld of Leiden University in the Netherlands, who led the research.
