Lifeboat Foundation: Safeguarding Humanity
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Category: health – Page 188

As demonstrated by breakthroughs in various fields of artificial intelligence (AI), such as image processing, smart health care, self-driving vehicles and smart cities, this is undoubtedly the golden period of deep learning. In the next decade or so, AI and computing systems will eventually be equipped with the ability to learn and think the way humans do—to process continuous flow of information and interact with the real world.

However, current AI models suffer from a performance loss when they are trained consecutively on new information. This is because every time new data is generated, it is written on top of existing data, thus erasing previous information. This effect is known as “catastrophic forgetting.” A difficulty arises from the stability-plasticity issue, where the AI model needs to update its memory to continuously adjust to the new information, and at the same time, maintain the stability of its current knowledge. This problem prevents state-of-the-art AI from continually learning from real world information.

Edge computing systems allow computing to be moved from the cloud storage and to near the , such as devices connected to the Internet of Things (IoTs). Applying continual learning efficiently on resource limited edge computing systems remains a challenge, although many continual learning models have been proposed to solve this problem. Traditional models require high computing power and large memory capacity.

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A groundbreaking mathematical equation that could transform medical procedures, natural gas extraction, and plastic packaging production in the future has been discovered.

The new equation, developed by scientists at the University of Bristol, indicates that diffusive movement through permeable material can be modeled exactly for the very first time. It comes a century after world-leading physicists Albert Einstein and Marian von Smoluchowski derived the first diffusion equation, and marks important progress in representing motion for a wide range of entities from microscopic particles and natural organisms to man-made devices.

Until now, scientists looking at particle motion through porous materials, such as biological tissues, polymers, various rocks and sponges have had to rely on approximations or incomplete perspectives.

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When one of China’s biggest celebrities, Simon Gong —also known as Gong Jun—released a new music video in June 2022, it quickly attracted 15 million views on the country’s Twitter-like microblogging site Weibo. But the event also stood out for a different reason—one that only eagle-eyed fans might have noticed. The singer in the video was not Gong himself, but a digital replica created by Baidu, a “digital human” powered by artificial intelligence (AI). Likewise, the lyrics and melody were generated by AI, marking the recording as China’s first AI-generated content music video.

Deloitte defines digital humans as AI-powered virtual beings that can produce a whole range of human body language. In recent years, businesses focused on providing round-the-clock services, as well as the media and entertainment industry, are increasingly adopting this nascent technology, aiming to capture a growing market. And as digital humans increasingly populate other sectors like retail, health care, and finance, Emergen Research forecasts that the global market for digital humans will jump to about $530 billion in 2030, from $10 billion in 2020.

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If you are used to getting regular health checkups, you might be familiar with endoscopes. The endoscope is an imaging device consisting of a camera and a light guide attached to a long flexible tube. It is particularly useful for acquiring images of the inside of a human body. For example, stomach and colon endoscopy are widely used for the early detection and diagnosis of diseases such as ulcers and cancers.

In general, an endoscope is manufactured by attaching a camera sensor to the end of a probe or using an optical fiber, which allows for information to be transmitted using light. In the case of an endoscope that uses a camera sensor, the thickness of the probe increases, which makes the endoscopy rather invasive. In the case of an endoscope using an optical fiber bundle, it can be manufactured in a thinner form factor, which minimizes invasiveness and results in much less discomfort to the patients.

However, the downside is that in a conventional fiber-bundle endoscope, it is difficult to perform , because the resolution of the obtained image is limited by the size of the individual fiber cores. Much of the image information is also lost due to reflection from the probe tip. Furthermore, in fiber endoscopy, it is often necessary to label the target with fluorescence, especially in with low reflectivity, due to strong back-reflection noise generated from the tip of the thin probe.

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A study finds that deep brain stimulation to areas of the brain associated with reward and motivation could be used as a potential treatment for depression.

According to researchers at the University of Texas Health Science Center at Houston, deep brain stimulation (DBS) to the superolateral branch of the medial forebrain bundle (MFB), which is linked to motivation and reward, revealed metabolic brain changes over a 12-month period following DBS implantation. This makes it a potent potential therapy for treatment-resistant depression.

The study’s findings, which included 10 patients, were published in the journal Molecular Psychiatry.

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Some of us, when we hear the word quantum (plural quanta, from the German word Quanten), might think of health supplements, a sports car, or even the television show Quantum Leap. More recently, in Marvel Studios movies such as Ant-Man, Doctor Strange, and Avengers: Endgame, “the quantum realm” is presented where time flows differently from our ordinary reality and the Avengers may use the subatomic world “to go back in time”, a world that “is smaller than a single atom” (Woodward, 2019, para.20)

We might have also seen or known the meaning of words such as quantum mechanics, quantum computing, and quantum entanglement, but what is a quantum and how does it relate to our ordinary realm?

A quantum is a word that refers to “how much”; it is a specific amount. For example, if the speed of your car happens to be quantized in increments of 10 mph, then as you accelerate your car from 10 mph, the speed will jump to 20 mph, without passing through any speed between 10 mph and 20 mph. A speed of 12 mph or 19 mph is excluded because the speed of your car can only exist in those increments of 10 mph.

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Basically the United States has alerts for the west Nile as it seems to be spreading across many states.

As temperatures warm, US health officials are braced for rising rates of West Nile virus, a disease transmitted by mosquitoes that can cause meningitis, paralysis, and death.

Oklahoma reported its first West Nile death of the year on Thursday, in a resident who had been hospitalized with the illness.

In 2021, eight people got sick and one died of West Nile virus in Oklahoma, according to the US Centers for Disease Control and Prevention. The virus often infects people without causing symptoms, but can be deadly if it reaches the brain.

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The infrared (IR) spectrum is a vast information landscape that modern IR detectors tap into for diverse applications such as night vision, biochemical spectroscopy, microelectronics design, and climate science. But modern sensors used in these practical areas lack spectral selectivity and must filter out noise, limiting their performance. Advanced IR sensors can achieve ultrasensitive, single-photon level detection, but these sensors must be cryogenically cooled to 4 K (−269 C) and require large, bulky power sources making them too expensive and impractical for everyday Department of Defense or commercial use.

DARPA’s Optomechanical Thermal Imaging (OpTIm) program aims to develop novel, compact, and room-temperature IR sensors with quantum-level performance – bridging the performance gap between limited capability uncooled thermal detectors and high-performance cryogenically cooled photodetectors.

“If researchers can meet the program’s metrics, we will enable IR detection with orders-of-magnitude improvements in sensitivity, spectral control, and response time over current room-temperature IR devices,” said Mukund Vengalattore, OpTIm program manager in DARPA’s Defense Sciences Office. “Achieving quantum-level sensitivity in room-temperature, compact IR sensors would transform battlefield surveillance, night vision, and terrestrial and space imaging. It would also enable a host of commercial applications including infrared spectroscopy for non-invasive cancer diagnosis, highly accurate and immediate pathogen detection from a person’s breath or in the air, and pre-disease detection of threats to agriculture and foliage health.”

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Summary: Sleep age, a projected age that correlates to a person’s sleep health, may be a predictor of overall health and mortality risk.

Source: Stanford.

Numbers tell a story. From your credit score to your age, metrics predict a variety of outcomes, whether it’s your likelihood to get a loan or your risk for heart disease. Now, Stanford Medicine researchers have described another telling metric—one that can predict mortality. It’s called sleep age.

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Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.

The researchers, from the University of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power by suggesting routes and driving patterns that minimize battery degradation and charging times.

The team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.

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