They say Bitcoin can’t be stopped.Just like there’s no way you can stop two people sending encrypted messages to each other, so — they say — there’s no way you can stop the Bitcoin network.
There’s no CEO to put on trial, no central server to seize, and no organisation to put pressure on. The Bitcoin network is, fundamentally, just people sending messages to each other, peer to peer, and if you knock out 1 node on the network, or even 1,000 nodes, the honey badger don’t give a shit: the other 10,000+ nodes keep going like nothing happened, and more nodes can come online at any time, anywhere in the world.
So there you have it: it’s thousands of people running nodes — running code — and it’s unstoppable… therefore Bitcoin is unstoppable code; Q.E.D.; case closed; no further questions Your Honour. This money is above the law, and governments cannot possibly hope to control it, right?
Wrong. Read More: … Part 1 … Part 2
Daily Archives: March 11, 2021
Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
This paper addresses the challenge of novel view synthe-sis for a human performer from a very sparse set of cameraviews. Some recent works have shown that learning implicitneural representations of 3D scenes achieves remarkableview synthesis quality given dense input views. However,the representation learning will be ill-posed if the views arehighly sparse. To solve this ill-posed problem, our key ideais to integrate observations over video frames. To this end,we propose Neural Body, a new human body representationwhich assumes that the learned neural representations atdifferent frames share the same set of latent codes anchoredto a deformable mesh, so that the observations acrossframes can be naturally integrated. The deformable meshalso provides geometric guidance for the network to learn3D representations more efficiently. Experiments on a newlycollected multi-view dataset show that our approach out-performs prior works by a large margin in terms of the viewsynthesis quality. We also demonstrate the capability of ourapproach to reconstruct a moving person from a monocularvideo on the People-Snapshot dataset. The code and datasetwill be available at https://zju3dv.github.io/neuralbody/. Read More
#image-recognitionAway From Silicon Valley, the Military Is the Ideal Customer
While much has been made of tech’s unwillingness to work with the Pentagon, start-ups are still plumbing the industry’s decades-long ties to the military.
… Though parts of Silicon Valley have kept the Pentagon at arm’s length in recent years, Palmer Luckey’s company, based 400 miles to the south in Irvine, is aggressively courting business from government agencies and the military.
It is one of a number of young tech companies, many of them far from Silicon Valley, that are shrugging off the concerns about the potential militarization of their creations that in recent years have stirred employee revolts at industry giants like Google and Microsoft. Read More
Graph Neural Network and Some of GNN Applications – Everything You Need to Know
The recent success of neural networks has boosted research on pattern recognition and data mining.
Machine learning tasks, like object detection, machine translation, and speech recognition, have been given new life with end-to-end deep learning paradigms like CNN, RNN, or autoencoders.
Deep Learning is good at capturing hidden patterns of Euclidean data (images, text, videos).
But what about applications where data is generated from non-Euclidean domains, represented as graphs with complex relationships and interdependencies between objects?
That’s where Graph Neural Networks (GNN) come in, which we’ll explore in this article. We’ll start with graph theories and basic definitions, move on to GNN forms and principles, and finish with some applications of GNN. Read More
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