Half of the normal matter in the universe has been hiding since the Big Bang. Astronomers have just detected it for the first time.
Circa 2007
This chapter describes detection of explosives by terahertz Imaging ™. There has been an amplified interest in terahertz (THz) detection for imaging of covered weapons, explosives, chemical and biological agents. THz radiation is readily transmitted through most nonmetallic and nonpolar mediums. This process enables the THz systems to see through concealing barriers, which includes packaging, corrugated cardboard, clothing, shoes, book bags, and such others to find potentially dangerous materials concealed within. Apart from many materials of interest for security applications, which include explosives, chemical agents, and other such biological agents that have characteristic THz spectra which can be used for fingerprint testing and identify concealed materials. The Terahertz radiation poses either no or minimal health risk to either a suspect being scanned by a THz system or the system’s operator. As plastic explosives, fertilizer bombs, and chemical and biological agents increasingly become weapons of war and terrorism, and the trafficking of illegal drugs increasingly develops as a systemic threat, effective means for rapid detection, and an identification of these threats are required. One proposed solution for locating, detecting, and characterizing concealed threats is to use THz electromagnetic waves to spectroscopically detect and identify concealed materials through their characteristic transmission or reflectivity spectra in the range of 0.5–10 THz.
Circa 2019
A full-body scanner that the Transportation Security Administration hopes can speed up airport security checkpoints must go back to the drawing board for software to protect the privacy of travelers being scanned.
The scanner, built by British firm Thruvision, was promoted as being able to simultaneously screen multiple airport passengers from a distance of up to 25 feet away. The TSA began trying out the device last year at an Arlington, Va., testing facility before planning to use it on a trial basis at U.S. airports.
XAG, one of China’s largest makers of agricultural drones, expects increased automation for planting rice in the country’s farmlands as a way to raise efficiency, while mitigating labour shortage and the threat of Covid-19.
The fast and efficient generation of random numbers has long been an important challenge. For centuries, games of chance have relied on the roll of a die, the flip of a coin, or the shuffling of cards to bring some randomness into the proceedings. In the second half of the 20th century, computers started taking over that role, for applications in cryptography, statistics, and artificial intelligence, as well as for various simulations—climatic, epidemiological, financial, and so forth.
Now abandoned bikes are strewn across the streets, the leather-covered massage chairs are empty amid worries over cleanliness and people are ordering more of their daily necessities online and avoiding malls altogether. After weeks of lockdowns and social distancing measures to combat the spread of the virus, many people are asking whether this fabled part of China’s shiny new tech-driven economy will ever recover its former glory.
Experts say that consumer behaviour has changed irrevocably as a result of Covid-19 – and that the sharing economy must adapt.
The most common organism in the oceans, and possibly on the entire planet, is a family of single-celled marine bacteria called SAR11. These drifting organisms look like tiny jelly beans and have evolved to outcompete other bacteria for scarce resources in the oceans.
We now know that this group of organisms thrives despite—or perhaps because of—the ability to host viruses in their DNA. A study published in May in Nature Microbiology could lead to new understanding of viral survival strategies.
University of Washington oceanographers discovered that the bacteria that dominate seawater, known as Pelagibacter or SAR11, hosts a unique virus. The virus is of a type that spends most of its time dormant in the host’s DNA but occasionally erupts to infect other cells, potentially carrying some of its host’s genetic material along with it.
A team of more than 30 OpenAI researchers have released a paper about GPT-3, a language model capable of achieving state-of-the-art results on a set of benchmark and unique natural language processing tasks that range from language translation to generating news articles to answering SAT questions. GPT-3 has a whopping 175 billion parameters. By comparison, the largest version of GPT-2 was 1.5 billion parameters, and the largest Transformer-based language model in the world — introduced by Microsoft earlier this month — is 17 billion parameters.
OpenAI released GPT-2 last year, controversially taking a staggered release approach due to fear that the model could be used for malicious purposes. OpenAI was criticized by some for the staggered approach, while others applauded the company for demonstrating a way to carefully release an AI model with the potential for misuse. GPT-3 made its debut with a preprint arXiv paper Thursday, but no release details are provided. An OpenAI spokesperson declined to comment when VentureBeat asked if a full version of GPT-3 will be released or one of seven smaller versions ranging in size from 125 million to 13 billion parameters.
August 19, 2019 — An international team of researchers developed a new magnetic resonance imaging (MRI) technique that can capture an image of a brain thinking by measuring changes in tissue stiffness. The results show that brain function can be tracked on a time scale of 100 milliseconds – 60 times faster than previous methods. The technique could shed new light on altered neuronal activity in brain diseases.
The human brain responds almost immediately to stimuli, but non-invasive imaging techniques haven’t been able to keep pace with the brain. Currently, several non-invasive brain imaging methods measure brain function, but they all have limitations. Most commonly, clinicians and researchers use functional magnetic resonance imaging (fMRI) to measure brain activity via fluctuations in blood oxygen levels. However, a lot of vital brain activity information is lost using fMRI because blood oxygen levels take about six seconds to respond to a stimulus.
Since the mid-1990s, researchers have been able to generate maps of tissue stiffness using an MRI scanner, with a non-invasive technique called magnetic resonance elastography (MRE). Tissue stiffness can not be measured directly, so instead researchers use MRE to measure the speed at which mechanical vibrations travel through tissue. Vibrations move faster through stiffer tissues, while vibrations travel through softer tissue more slowly; therefore, tissue stiffness can be determined. MRE is most commonly used to detect the hardening of liver tissue but has more recently been applied to other tissues like the brain.
