Lifeboat Foundation

Non-rigid point set registration is the process of finding a spatial transformation that aligns two shapes represented as a set of data points. It has extensive applications in areas such as autonomous driving, medical imaging, and robotic manipulation. Now, a method has been developed to speed up this procedure.

In a study published in IEEE Transactions on Pattern Analysis and Machine Intelligence, a researcher from Kanazawa University has demonstrated a technique that reduces the computing time for non-rigid point set registration relative to other approaches.

Previous methods to accelerate this process have been computationally efficient only for shapes described by small point sets (containing fewer than 100000 points). Consequently, the use of such approaches in applications has been limited. This latest research aimed to address this drawback.

In recent years, videogame developers and computer scientists have been trying to devise techniques that can make gaming experiences increasingly immersive, engaging and realistic. These include methods to automatically create videogame characters inspired by real people.

Most existing methods to create and customize videogame characters require players to adjust the features of their character’s face manually, in order to recreate their own face or the faces of other people. More recently, some developers have tried to develop methods that can automatically customize a character’s face by analyzing images of real people’s faces. However, these methods are not always effective and do not always reproduce the faces they analyze in realistic ways.

Researchers at Netease Fuxi AI Lab and University of Michigan have recently created MeInGame, a deep learning technique that can automatically generate character faces by analyzing a single portrait of a person’s face. This technique, presented in a paper pre-published on arXiv, can be easily integrated into most existing 3D videogames.

Algorithms are meaningless without good data. The public can exploit that to demand change.

Hence, while the fast particles are abundant in molecular systems, they are practically lacking in active matter. These fast particle can overcome the attraction forces of the molecular surrounding and give rise to the gaseous phase; the lack of fast particles in active matter results in the lack of the gas, coexisting with the liquid phase. If the particle density is still larger and the repulsive forces are strong, the active matter falls into a solid phase, Fig. 1 d, where the particles mobility is suppressed.

In some range of parameters a novel swirlonic phase is formed, Fig. 1e. It is comprised of swirlons —“super-particles” with many astonishing properties which we address below. Individual active particles in swirlons perform a swirling motion around their common center. As we demonstrate in what follows, the swirlons are formed when local fluctuating force exceeds the critical force, which characterizes the ability of an active particle to move against an applied load.

Finally, for large density and very strong attractive forces a collapsed state is observed, Fig. 1 f. In the collapsed state active particle also move around the common center, however the character of the motion is rather irregular. In the present study we will focus on the gaseous, liquid and especially on the swirlonic phase.

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix (ECM) adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

I would like to propose here a unifying theory of rewritable data storage in animals. This theory is based around the realisation that mechanosensitive proteins, which contain force-dependent binary switches, can store information persistently in a binary format, with the information stored in each molecule able to be written and/or updated via small changes in mechanical force. The protein talin contains 13 of these switches (Yao et al., 2016; Goult et al., 2018; Wang et al., 2019), and, as I argue here, it is my assertion that talin is the memory molecule of animals. These mechanosensitive proteins scaffold each and every synapse (Kilinc, 2018; Lilja and Ivaska, 2018; Dourlen et al., 2019) and have been considered mainly structural. However, these synaptic scaffolds also represent a meshwork of binary switches that I propose form a code, the so-called MeshCODE.

The United States Army has new goggles capable of letting soldiers see through walls of combat vehicles — which enables infantry troops to greatly enhance their situational awareness, according to a press release shared on the Defense Visual Information Distribution Service.

And, in combat, situational awareness is key to survival.

Large language models are already business propositions. Google uses them to improve its search results and language translation; Facebook, Microsoft and Nvidia are among other tech firms that make them. OpenAI keeps GPT-3’s code secret and offers access to it as a commercial service. (OpenAI is legally a non-profit company, but in 2019 it created a for-profit subentity called OpenAI LP and partnered with Microsoft, which invested a reported US$1 billion in the firm.) Developers are now testing GPT-3’s ability to summarize legal documents, suggest answers to customer-service enquiries, propose computer code, run text-based role-playing games or even identify at-risk individuals in a peer-support community by labelling posts as cries for help.

A remarkable AI can write like humans — but with no understanding of what it’s saying.

Four Emerging Technology Areas Impacting Industry 4.0: Advanced Computing, Artificial intelligence, Big Data & Materials Science.