Lifeboat Foundation

Government agencies and universities around the world—not to mention tech giants like IBM and Google—are vying to be the first to answer a trillion-dollar quantum question : How can quantum computers reach their vast potential when they are still unable to consistently produce results that are reliable and free of errors?

Every aspect of these exotic machines—including their fragility and engineering complexity; their preposterously sterile, low-temperature operating environment; complicated mathematics; and their notoriously shy quantum bits (qubits) that flip if an operator so much as winks at them—are all potential sources of errors. It says much for the ingenuity of scientists and engineers that they have found ways to detect and correct these errors and have quantum computers working to the extent that they do: at least long enough to produce limited results before errors accumulate and quantum decoherence of the qubits kicks in.

This paper describes the design, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications (“apps’‘). The techniques in VanarSena are driven by a study of 25 million real-world crash reports of Windows Phone apps reported in 2012. Our analysis indicates that a modest number of root causes are responsible for many observed failures, but that they occur in a wide range of places in an app, requiring a wide coverage of possible execution paths. VanarSena adopts a “greybox’’ testing method, instrumenting the app binary to achieve both coverage and speed. VanarSena runs on cloud servers: the developer uploads the app binary; VanarSena then runs several app “monkeys’’ in parallel to emulate user, network, and sensor data behavior, returning a detailed report of crashes and failures. We have tested VanarSena with 3000 apps from the Windows Phone store, finding that 1108 of them had failures; VanarSena uncovered 2969 distinct bugs in existing apps, including 1227 that were not previously reported. Because we anticipate VanarSena being used in regular regression tests, testing speed is important. VanarSena uses two techniques to improve speed. First, it uses a “hit testing’’ method to quickly emulate an app by identifying which user interface controls map to the same execution handlers in the code. Second, it generates a ProcessingCompleted event to accurately determine when to start the next interaction. These features are key benefits of VanarSena’s greybox philosophy.

2014-06

http://hdl.handle.net/1721.1/110759

Roboticists at the University of California San Diego have developed flexible feet that can help robots walk up to 40 percent faster on uneven terrain such as pebbles and wood chips. The work has applications for search-and-rescue missions as well as space exploration.

“Robots need to be able to walk fast and efficiently on natural, uneven terrain so they can go everywhere humans can go, but maybe shouldn’t,” said Emily Lathrop, the paper’s first author and a Ph.D. student at the Jacobs School of Engineering at UC San Diego.

The researchers will present their findings at the RoboSoft conference which takes place virtually May 15 to July 15, 2020.

Is it possible some instances of artificial intelligence are not as intelligent as we thought?

Call it artificial artificial intelligence.

A team of computer graduate students reports that a closer examination of several dozen information retrieval algorithms hailed as milestones in artificial research were in fact nowhere near as revolutionary as claimed. In fact, AI used in those algorithms were often merely minor tweaks of previously established routines.

Washington State University (WSU) and Pacific Northwest National Laboratory (PNNL) researchers have created a sodium-ion battery that holds as much energy and works as well as some commercial lithium-ion battery chemistries, making for a potentially viable battery technology out of abundant and cheap materials.

The team reports one of the best results to date for a sodium-ion battery. It is able to deliver a capacity similar to some lithium-ion batteries and to recharge successfully, keeping more than 80 percent of its charge after 1,000 cycles. The research, led by Yuehe Lin, professor in WSU’s School of Mechanical and Materials Engineering, and Xiaolin Li, a senior research scientist at PNNL is published in the journal, ACS Energy Letters.

“This is a major development for sodium-ion batteries,” said Dr. Imre Gyuk, director of Energy Storage for the Department of Energy’s Office of Electricity who supported this work at PNNL. “There is great interest around the potential for replacing Li-ion batteries with Na-ion in many applications.”

Inspired by the Japanese art of paper cutting, MIT engineers have designed a friction-boosting material that could be used to coat the bottom of your shoes, giving them a stronger grip on ice and other slippery surfaces.

The researchers drew on kirigami, a variation of origami that involves cutting paper as well as folding it, to create the new coating. Laboratory tests showed that when people wearing kirigami-coated shoes walked on an icy surface, they generated more friction than the uncoated shoes.

Incorporating this coating into shoes could help prevent dangerous falls on ice and other hazardous surfaces, especially among the elderly, the researchers say.

EPFL researchers have developed electronic fibers that, when embedded in textiles, can be used to collect data about our bodies by measuring fabric deformation. Their technology employs flexible transmission lines and offers a host of applications, including in the medical industry.

Professor Fabien Sorin and doctoral assistant Andreas Leber, at the Laboratory of Photonic Materials and Fibre Devices (FIMAP) in EPFL’s School of Engineering, have developed a technology that can be used to detect a body’s movements—and a whole lot more.

“Imagine clothing or hospital bed sheets capable of monitoring your breathing and physical gestures, or AI-powered textiles that allow humans to interact more safely and intuitively with robots” says Leber. “The flexible transmission lines that we’ve developed can do all of this.”

As artificial intelligence (AI) becomes increasingly used for critical applications such as diagnosing and treating diseases, predictions and results regarding medical care that practitioners and patients can trust will require more reliable deep learning models.

In a recent preprint (available through Cornell University’s open access website arXiv), a team led by a Lawrence Livermore National Laboratory (LLNL) computer scientist proposes a novel deep learning approach aimed at improving the reliability of classifier models designed for predicting disease types from diagnostic images, with an additional goal of enabling interpretability by a medical expert without sacrificing accuracy. The approach uses a concept called confidence calibration, which systematically adjusts the model’s predictions to match the human expert’s expectations in the real world.

“Reliability is an important yardstick as AI becomes more commonly used in high-risk applications, where there are real adverse consequences when something goes wrong,” explained lead author and LLNL computational scientist Jay Thiagarajan. “You need a systematic indication of how reliable the model can be in the real setting it will be applied in. If something as simple as changing the diversity of the population can break your system, you need to know that, rather than deploy it and then find out.”

A picture may be worth a thousand words, but apparently one image is worth potentially thousands of headaches for Android users recently.

The noted tech information leaker Ice Universe this weekend posted a warning about an image that if set as wallpaper will soft-brick Samsung and Google Pixel phones. Soft-bricking triggers Android devices to continuously loop an action or freeze the unit. This generally requires a factory reset.

The image, a seemingly innocuous sunset (or dawn) sky above placid waters, may be viewed without harm. But if loaded as wallpaper, the phone will crash.