Lifeboat Foundation

Researchers at the SketchX, University of Surrey have recently developed a meta learning-based model that allows users to retrieve images of specific items simply by sketching them on a tablet, smartphone, or on other smart devices. This framework was outlined in a paper set to be presented at the European Conference on Computer Vision (ECCV), one of the top three flagship computer vision conferences along with CVPR and ICCV.

“This is the latest along the line of work on ‘fine-grained image retrieval,’ a problem that my research lab (SketchX, which I direct and founded back in 2012) pioneered back in 2015, with a paper published in CVPR 2015 titled ‘Sketch Me That Shoe,’” Yi-Zhe Song, one of the researchers who carried out the study, told TechXplore. “The idea behind our paper is that it is often hard or impossible to conduct image retrieval at a fine-grained level, (e.g., finding a particular type of shoe at Christmas, but not any shoe).”

In the past, some researchers tried to devise models that can retrieve images based on text or voice descriptions. Text might be easier for users to produce, yet it was found only to work at a coarse level. In other words, it can become ambiguous and ineffective when trying to describe details.

Early detection and identification of pathogenic bacteria in food and water samples are essential to public health. Bacterial infections cause millions of deaths worldwide and bring a heavy economic burden, costing more than 4 billion dollars annually in the United States alone. Among pathogenic bacteria, Escherichia coli (E. coli) and other coliform bacteria are among the most common ones, and they indicate fecal contamination in food and water samples. The most conventional and frequently used method for detecting these bacteria involves culturing of the samples, which usually takes 24 hours for the final read-out and needs expert visual examination. Although some methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, they cannot differentiate live and dead bacteria and present low sensitivity at low concentrations of bacteria. That is why the U.S. Environmental Protection Agency (EPA) approves no nucleic acid-based bacteria sensing method for screening water samples.

In an article recently published in ACS Photonics, a journal of the American Chemical Society (ACS), a team of scientists, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), and co-workers have developed an AI-powered smart bacterial colony detection system using a thin-film transistor (TFT) array, which is a widely used technology in mobile phones and other displays.

The ultra-large imaging area of the TFT array (27 mm × 26 mm) manufactured by researchers at Japan Display Inc. enabled the system to rapidly capture the growth patterns of bacterial colonies without the need for scanning, which significantly simplified both the hardware and software design. This system achieved ~12-hour time savings compared to gold-standard culture-based methods approved by EPA. By analyzing the microscopic images captured by the TFT array as a function of time, the AI-based system could rapidly and automatically detect colony growth with a deep neural network. Following the detection of each colony, a second neural network is used to classify the bacteria species.

By Subscription? – In California, You Can and it’s a Tesla Model 3 EV.

A Santa Monica, California-based company can put you into a Tesla Model 3 using its cellphone app which is now available for both Android and iPhones. The company offering the Car-as-a-service (CaaS) model is Autonomy. Although currently available only in California, the future plans include rolling it out to other U.S. states.

Until the outset of the global pandemic, owning a car was on a dramatic decline. Ride-sharing was exploding, and because cars were becoming pricier, young people entering the workforce were less inclined to join their parents’ generation of car owners.

Continue reading “Want to Get Your Next Car” »

At the heart of every resonator—be it a cello, a gravitational wave detector, or the antenna in your cell phone—there is a beautiful bit of mathematics that has been heretofore unacknowledged.

Yale physicists Jack Harris and Nicholas Read know this because they started finding knots in their data.

In a new study in the journal Nature, Harris, Read, and their co-authors describe a previously unknown characteristic of resonators. A resonator is any object that vibrates only at a specific set of frequencies. They are ubiquitous in sensors, electronics, musical instruments, and other devices, where they are used to produce, amplify, or detect vibrations at specific frequencies.

A bubbling waterfall and plants that purify indoor air, no cellphones, and a pebbled path — every element in this garden has been built to boost mental well-being and reduce stress.

EPFL researchers have used swarms of drones to measure city traffic with unprecedented accuracy and precision. Algorithms are then used to identify sources of traffic jams and recommend solutions to alleviate traffic problems.

Given the wealth of modern technology available—roadside cameras, big-data algorithms, Bluetooth and RFID connections, and smartphones in every pocket—transportation engineers should be able to accurately measure and forecast city traffic. However, current tools advance towards the direction of showing the symptom but systematically fail to find the root cause, let alone fix it. Researchers at EPFL utilize a monitoring tool that overcomes many problems using drones.

“They provide excellent visibility, can cover large areas and are relatively affordable. What’s more, they offer greater precision than GPS technology and eliminate the behavioral biases that occur when people know they’re being watched. And we use drones in a way that protects people’s identities,” says Manos Barmpounakis, a post-doc researcher at EPFL’s Urban Transport Systems Laboratory (LUTS).

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations.

This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speaker’s voice and reduce background noise, “ClearBuds” use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone.

Continue reading “First wireless earbuds that clear up calls using deep learning” »

For the first time, an augmented reality contact lens was worn on the eye of a human subject. It has about 30x the pixel density of an iPhone.

A cutting-edge AI development that could boost smartphone battery life by 30 percent and shave countless kilowatts from energy bills will be unveiled to technology giants. The ground-breaking University of Essex-developed work has been rolled into an app called EOptomizer—which will be demonstrated to expert researchers and designers as well as major manufacturing companies like Nokia and Huawei.

It is hoped the EOptomizer app will be adapted across the industry and help drive down carbon emissions, by making consumers’ goods last longer.

It will do this by using software to dramatically increasing efficiency and reliability in phones, tablets, cars, smart fridges and computers’ batteries—delaying when consumers need to buy carbon-footprint-producing replacements. The event—which takes place in Robinson College, in Cambridge, on 11July—will showcase the impact EOptomizer could have across the globe.