Lithium-ion batteries have become a key component in the rise of electric mobility, but forecasting their health and lifespans is limiting the technology. While they've proven successful, the capacity of lithium-ion batteries degrades over time, and not just because of the ageing process that occurs during charging and discharging -- known as "cycling ageing." Lithium-ion battery cells also suffer degradation from so-called "calendar ageing," which occurs during storage, or simply when the battery is not in use. It's determined by three main factors: the rest state of charge (SOC), the rest temperature, and the duration of the rest time of a battery. Given that an electric vehicle will spend most of its life parked, predicting the cells' capacity degradation from calendar ageing is crucial; it can prolong battery life and pave the way for mechanisms that could even circumvent the phenomenon.

The researchers designed a form of artificial intelligence (AI) based on machine learning that can perform complex calculations within a very …

In a move that could transform manuscript studies, University of Toronto researchers have partnered with a team in the United Kingdom to develop a program that can read and transcribe the handwritten Latin found in 13th-century legal manuscripts. While scholars have been making digital images of these manuscripts for years, transcribing and comparing these texts is painstaking and tedious work that can take years or even decades to complete. That's because medieval handwriting can often look crabbed and unintelligible, with non-standardized spellings, hyphenations, abbreviations, calligraphic flourishes and any number of distinct "hands." But machine-reading software called Transkribus promises to change the field. Using artificial intelligence (AI), the software can theoretically be trained to read any type of handwriting, in any language – and Michael Gervers, a professor of medieval social and economic history at U of T Scarborough, says it could eventually be applied across medieval studies.

What if you could manipulate the facial features of a historical figure, a politician, or a CEO realistically and convincingly using nothing but a webcam and an illustrated or photographic still image? A tool called MarioNETte that was recently developed by researchers at Seoul-based Hyperconnect accomplishes this, thanks in part to cutting-edge machine learning techniques. The researchers claim it outperforms all baselines even where there's "significant" mismatch between the face to be manipulated and the person doing the manipulating. MarioNETte is technically a face reenactment tool, in that it aims to synthesize a reenacted face animated by the movement of a person (a "driver") while preserving the face's (target's) appearance. It's not a new idea, but previous approaches either (1) required a few minutes of training data and could only reenact predefined targets, or (2) would distort the target's features when dealing with large poses.

While Alzheimer's disease affects tens of millions of people worldwide, it remains difficult to detect early on. But researchers exploring whether AI can play a role in detecting Alzheimer's in patients are finding that it may be a valuable tool for helping spot the disease. Researchers in California recently published a study in the journal Radiology, and they demonstrated that, once trained, a neural network was able to accurately diagnose Alzheimer's disease in a small number of patients, and it did so based on brain scans taken years before those patients were actually diagnosed by physicians. The team used brain images -- FDG-PET images -- to train and test their neural network. With this type of imaging, FDG, a radioactive type of glucose, is injected into a person's bloodstream, and then that person's bodily tissue, including brain tissue, takes it up as it would regular glucose.

Researchers at the University of California, Berkeley have created a framework for teaching artificial intelligence systems to learn motion from being shown video clips on YouTube. The framework incorporates computer vision and reinforcement learning to train AI skills from videos. Altogether the team was able to train AI to perform more than 20 acrobatic tasks like cartwheels, handsprings, backflips, and some martial arts. The method does not require the use of motion capture video, the kind often used to transfer human action to digital forms, such as the movement of LeBron James incorporated into NBA 2K18 or the performance of Andy Serkis as Gollum from Lord of the Rings. The framework works by first ingesting the video to understand the poses seen in each video frame; then a simulated character is trained to imitate the movement using reinforcement learning.

You've probably heard of fingerprint scans, iris scans, and perhaps even eye gaze scans, but what about foostep-based biometrics? New research published on the preprint server Arxiv.org Researchers at the Indian Institute of Technology in Delhi describe the system in a paper titled "Person Identification using Seismic Signals generated from Footfalls." It's based on a fog computing architecture, which employs edge devices to carry out much of the computing, storage, and communication involved in data collection. "[With our approach], individuals are only required to walk through the active region of the sensor," they wrote.

Researchers fed the software this picture of actor J.K. Simmons, among others, to teach it to recognize specific faces. Since 1989, Cops has famously aired footage of suspected criminals, many with their faces blurred out to protect their privacy. Ever since then, blurred or pixelated faces have become standard fare for concealing the identity of individuals who prefer not to be recognized in the media. YouTube got in the game a few years ago, offering a facial blurring tool to help protect protestors against retribution from law enforcement or employers. But machine learning researchers at Cornell Tech and the University of Texas at Austin have developed software that makes it possible for users to recognize a person's concealed face in photographs or videos.