Lifeboat Foundation

A maze is a popular device among psychologists to assess the learning capacity of mice or rats. But how about robots? Can they learn to successfully navigate the twists and turns of a labyrinth? Now, researchers at the Eindhoven University of Technology (TU/e) in the Netherlands and the Max Planck Institute for Polymer Research in Mainz, Germany, have proven they can. Their robot bases its decisions on the very system humans use to think and act: the brain. The study, which was published in Science Advances, paves the way to exciting new applications of neuromorphic devices in health and beyond.

Machine learning and neural networks have become all the rage in recent years, and quite understandably so, considering their many successes in image recognition, medical diagnosis, e-commerce and many other fields. Still though, this software-based approach to machine intelligence has its drawbacks, not least because it consumes so.

Using data on electromagnetic (EM) waves and plasma particles measured simultaneously via multiple satellites, an international collaborative research group has discovered the existence of invisible “propagation path” of EM waves and elucidated the mechanism by which EM waves propagate to the ground.

It is known that various kinds of EM waves occur naturally in geospace and cause variations in the plasma environment that surrounds the Earth via a physical process known as wave–particle interaction. In particular, when geospace storms occur due to disturbances of sun and solar wind, EM waves become more active, and variations of geospace environment sometimes, may cause damage to spacecrafts, expose astronauts to radiation, and cause terrestrial power grid failures. To understand variation in the plasma environment caused by EM waves in space, in-situ measurement has been performed in space using spacecrafts, such as the Japanese geospace satellite Arase.

As EM waves in space propagate far away from their origin, to correctly understand the effects of EM waves, it is crucial to understand where in space the EM waves are generated and how they are propagated. However, it has been difficult to unravel the origin of EM waves and the mysteries of how EM waves spread spatially using only single-point observation. “Electromagnetic ion cyclotron waves (EMIC waves),” which are the focus of this study, are an important class of EM wave in geospace that control variations in the geospace plasma environment. The source region of ion mode waves has a finite spatial extent, and generated EMIC waves are considered to propagate north to south along the geomagnetic field lines. The specific spatial size of the EMIC wave source region and the 3D aspect of how the propagation path is formed from space to ground are yet to be elucidated.

The hunt is on for leptoquarks, particles beyond the limits of the standard model of particle physics —the best description we have so far of the physics that governs the forces of the Universe and its particles. These hypothetical particles could prove useful in explaining experimental and theoretical anomalies observed at particle accelerators such as the Large Hadron Collider (LHC) and could help to unify theories of physics beyond the standard model, if researchers could just spot them.

A new paper published in Nuclear Physics B by Anirban Karan, Priyotosh Bandyopadhyay, and Saunak Dutta, of the Indian Institute of Technology Hyderabad, Kandi, together with Mahesh Jakkapu, Graduate University for Advanced Studies (SOKENDAI), Kanagawa, Japan, examines the potential signatures of leptoquarks at the LHC to see how they could arise from proton-proton collisions for the possible mass ranges of these particles.

The main objective of this research is how to distinguish the signatures of different leptoquarks at proton-proton colliders like LHC or its proposed successor, Karan says.

Clip during an interview made by Nicholas Singh, Senior Product Manager at Novos Labs to Kris Verburgh, CSO and Co-Founder of Novos Lab.

The clip shows the answer made by Kris Verburgh to a question made by José Cordeiro, PhD, MBA, Vice-Chairman of Humanity Plus, about the prediction made by Ray Kurzweil on the availability of full body human biological rejuvenation by 2045.

Continue reading “Kris Verburgh — CSO of Novos Labs views on Longevity Escape Velocity (con S/T en Español)” »

Energize the Solar System.

Over the next 15 years, multiple space agencies like NASA and commercial partners like SpaceX intend to mount crewed missions to the Moon and Mars.

The accelerated growth in ecommerce and online marketplaces has led to a surge in fraudulent behavior online perpetrated by bots and bad actors alike. A strategic and effective approach to online fraud detection will be needed in order to tackle increasingly sophisticated threats to online retailers.

These market shifts come at a time of significant regulatory change. Across the globe, new legislation is coming into force that alters the balance of responsibility in fraud prevention between users, brands, and the platforms that promote them digitally. For example, the EU Digital Services Act and US Shop Safe Act will require online platforms to take greater responsibility for the content on their websites, a responsibility that was traditionally the domain of brands and users to monitor and report.

Can AI find what’s hiding in your data? In the search for security vulnerabilities, behavioral analytics software provider Pasabi has seen a sharp rise in interest in its AI analytics platform for online fraud detection, with a number of key wins including the online reviews platform, Trustpilot. Pasabi maintains its AI models based on anonymised sets of data collected from multiple sources.

Continue reading “Why AI is the future of fraud detection” »

A new computational simulator can help predict whether changes to materials or design will improve performance in new photovoltaic cells.

In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without having to actually build each new variation for testing, the process remains slow.

Now, researchers at MIT and Google Brain have developed a system that makes it possible not just to evaluate one proposed design at a time, but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of new, improved configurations.

The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death.

Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient’s risk of death.

Continue reading “Machine-learning system flags remedies that might do more harm than good” »

Strategy accelerates the best algorithmic solvers for large sets of cities.

Waiting for a holiday package to be delivered? There’s a tricky math problem that needs to be solved before the delivery truck pulls up to your door, and MIT researchers have a strategy that could speed up the solution.

The approach applies to vehicle routing problems such as last-mile delivery, where the goal is to deliver goods from a central depot to multiple cities while keeping travel costs down. While there are algorithms designed to solve this problem for a few hundred cities, these solutions become too slow when applied to a larger set of cities.

Continue reading “Machine learning speeds up vehicle routing” »