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Category: information science – Page 187

Is an academic doctor and medical technology entrepreneur, working in the field of the computational biology of aging.

Dr. Radenkovic is also a Partner at the SALT Bio-Fund, and a co-founder of Hooke, an elite longevity research clinic in London.

Dr. Radenkovic has a dual degree in medicine and physiology from University College London Medical School, and did her residency at St Thomas’ Hospital in London. She later worked as Research Fellow at King’s College London and at Harvard University.

Dr. Radenkovic has led a variety of projects, including a digital therapeutics company for women and an algorithm for cardiac MRI based on fractal geometry, to major industry acquisitions.

Dr. Radenkovic has over 30 academic papers, 7 grants, and over 40 scientific conference presentations. She is fluent in 5 languages and 3 programming languages.

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Circa 2021

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques.

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Learning how to learn is something most humans do well, by leveraging previous experiences to inform the learning processes for new tasks. Endowing AI systems with such abilities however remains challenging, as it requires the machine learners to learn update rules, which typically have been manually tuned for each task.

The field of meta-learning studies how to enable machine learners to learn how to learn, and is a critical research area for improving the efficiency of AI agents. One of the approaches is for learners to learn an update rule by applying it on previous steps and then evaluating the corresponding performance.

To fully unlock the potential of meta-learning, it is necessary to overcome both the meta-optimization problem and myopic meta objectives. To tackle these issues, a research team from DeepMind has proposed an algorithm designed to enable meta-learners to teach themselves.

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On Oct. 7 2015, Perimeter Institute Director Neil Turok opened the 2015/16 season of the PI Public Lecture Series with a talk about the remarkable simplicity that underlies nature. Turok discussed how this simplicity at the largest and tiniest scales of the universe is pointing toward new avenues of physics research and could lead to revolutionary advances in technology.

Perimeter Institute (charitable registration number 88,981 4323 RR0001) is the world’s largest independent research hub devoted to theoretical physics, created to foster breakthroughs in the fundamental understanding of our universe, from the smallest particles to the entire cosmos. The Perimeter Institute Public Lecture Series is made possible in part by the support of donors like you. Be part of the equation: https://perimeterinstitute.ca/inspiring-and-educating-public.

Subscribe for updates on future live webcasts, events, free posters, and more: https://insidetheperimeter.ca/newsletter/

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If travel to distant stars within an individual’s lifetime is going to be possible, a means of faster-than-light propulsion will have to be found. To date, even recent research about superluminal (faster-than-light) transport based on Einstein’s theory of general relativity would require vast amounts of hypothetical particles and states of matter that have “exotic” physical properties such as negative energy density. This type of matter either cannot currently be found or cannot be manufactured in viable quantities. In contrast, new research carried out at the University of Göttingen gets around this problem by constructing a new class of hyper-fast ‘solitons’ using sources with only positive energies that can enable travel at any speed. This reignites debate about the possibility of faster-than-light travel based on conventional physics. The research is published in the journal Classical and Quantum Gravity.

The author of the paper, Dr Erik Lentz, analysed existing research and discovered gaps in previous ‘warp drive’ studies. Lentz noticed that there existed yet-to-be explored configurations of space-time curvature organized into ‘solitons’ that have the potential to solve the puzzle while being physically viable. A soliton — in this context also informally referred to as a ‘warp bubble’ — is a compact wave that maintains its shape and moves at constant velocity. Lentz derived the Einstein equations for unexplored soliton configurations (where the space-time metric’s shift vector components obey a hyperbolic relation), finding that the altered space-time geometries could be formed in a way that worked even with conventional energy sources. In essence, the new method uses the very structure of space and time arranged in a soliton to provide a solution to faster-than-light travel, which — unlike other research — would only need sources with positive energy densities.

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Earlier this year, in June 2,021 the British Ministry of Defence employed Rafael’s DRONE DOME counter-UAV system to protect world leaders during the G7 Summit in Cornwall, England from unmanned aerial threats. Three years ago, Britain’s Defence Ministry purchased several DRONE DOME systems which it has successfully employed in a multitude of operational scenarios, including for protecting both the physical site and participants of this year’s G7 summit. Rafael’s DRONE DOME is an innovative end-to-end, combat-proven counter-Unmanned Aerial System (C-UAS), providing all-weather, 360-degree rapid defence against hostile drones. Fully operational and globally deployed, DRONE DOME offers a modular, robust infrastructure comprised of electronic jammers and sensors and unique artificial intelligence algorithms to effectively secure threatened air space.

Meir Ben Shaya, Rafael EVP for Marketing and Business Development of Air Defence Systems: Rafael today recognizes two new and key trends in the field of counter-UAVs, both of which DRONE DOME can successfully defend against. The first trend is the number of drones employed during an attack, and the operational need to have the ability counter multiple, simultaneous attacks; this is a significant, practical challenge that any successful system must be able to overcome. The second trend is the type of tool being employed. Previously, air defense systems were developed to seek out conventional aircraft, large unmanned aerial vehicles, and missile, but today these defense systems must also tackle smaller, slower, low-flying threats which are becoming more and more autonomous.

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“You may hit the tipping point when you’re 50; it may happen when you’re 80; it may never happen,” Schindler said. “But once you pass the tipping point, you’re going to accumulate high levels of amyloid that are likely to cause dementia. If we know how much amyloid someone has right now, we can calculate how long ago they hit the tipping point and estimate how much longer it will be until they are likely to develop symptoms.”

Summary: A new algorithm uses neuroimaging data of amyloid levels in the brain and takes into account a person’s age to determine when a person with genetic Alzheimer’s risk factors, and with no signs of cognitive decline, will develop the disease.

Source; WUSTL

Researchers at Washington University School of Medicine in St. Louis have developed an approach to estimating when a person who is likely to develop Alzheimer’s disease, but has no cognitive symptoms, will start showing signs of Alzheimer’s dementia.

The algorithm, available online in the journal Neurology, uses data from a kind of brain scan known as amyloid positron emission tomography (PET) to gauge brain levels of the key Alzheimer’s protein amyloid beta.

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Dementia has many faces, and because of the wide range of ways in which it can develop and affect patients, it can be very challenging to treat. Now, however, using supercomputer analysis of big data, researchers from Japan were able to predict that a single protein is a key factor in the damage caused by two very common forms of dementia.

In a study published this month in Communications Biology, researchers from Tokyo Medical and Dental University (TMDU) have revealed that the HMGB1 is a key player in both frontotemporal lobar and Alzheimer , two of the most common causes of dementia.

Frontotemporal lobar degeneration can be caused by mutation of a variety of genes, which means that no one treatment will be right for all patients. However, there are some similarities between frontotemporal lobar degeneration and Alzheimer disease, which led the researchers at Tokyo Medical and Dental University (TMDU) to explore whether these two conditions cause damage to the brain in the same way.

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Every piece of data that travels over the internet — from paragraphs in an email to 3D graphics in a virtual reality environment — can be altered by the noise it encounters along the way, such as electromagnetic interference from a microwave or Bluetooth device. The data are coded so that when they arrive at their destination, a decoding algorithm can undo the negative effects of that noise and retrieve the original data.

Since the 1950s, most error-correcting codes and decoding algorithms have been designed together. Each code had a structure that corresponded with a particular, highly complex decoding algorithm, which often required the use of dedicated hardware.

Researchers at MIT.

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