Accurately detecting drowsiness is vital to driving safety. Among all measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how physiological metrics are associated with different drowsiness labels across datasets. Thus, we analyzed key features from electrocardiograms (ECG), electrodermal activity (EDA), and respiratory (RESP) signals across four datasets, where different drowsiness inducers (such as fatigue and low arousal) and assessment methods (subjective vs. objective) were used. Binary logistic regression models were built to identify the physiological metrics that are associated with drowsiness. Findings indicate that distinct different drowsiness inducers can lead to different physiological responses, and objective assessments were more sensitive than subjective ones in detecting drowsiness. Further, the increased heart rate stability, reduced respiratory amplitude, and decreased tonic EDA are robustly associated with increased drowsiness. The results enhance understanding of drowsiness detection and can inform future generalizable monitoring designs.

In this work, we present the first efficient and practical algorithm for estimating the number of triangles in a graph stream using predictions. Our algorithm combines waiting room sampling and reservoir sampling with a predictor for the heaviness of edges, that is, the number of triangles in which an edge is involved. As a result, our algorithm is fast, provides guarantees on the amount of memory used, and exploits the additional information provided by the predictor to produce highly accurate estimates. We also propose a simple and domain-independent predictor, based on the degree of nodes, that can be easily computed with one pass on a stream of edges when the stream is available beforehand. Our analytical results show that, when the predictor provides useful information on the heaviness of edges, it leads to estimates with reduced variance compared to the state-of-the-art, even when the predictions are far from perfect. Our experimental results show that, when analyzing a single graph stream, our algorithm is faster than the state-of-the-art for a given memory budget, while providing significantly more accurate estimates. Even more interestingly, when sequences of hundreds of graph streams are analyzed, our algorithm significantly outperforms the state-of-the-art using our simple degree-based predictor built by analyzing only the first graph of the sequence.

Imagine a world where you own some sort of building whether that's a grocery store, a restaurant, a factory… and you want to know how many people reside in each section of the store, or maybe how long did the average person wait to be seated or how long did it take the average factory worker to complete their assembly task. Currently today these systems are either not using AI and instead use a mix of sensors and buttons to track certain actions or they do use AI but in a way that's highly specific to their use case and hard to easily modify for new use cases that come down the line. This is where BrainFrame comes in. BrainFrame is a tool that connects to all your on-prem cameras and lets you easily leverage AI models and business logic. Alex Thiele is the CTO of Aotu the company that makes BrainFrame and he joins me today to talk about BrainFrame and the vision for a future where computer vision can be run by anyone.

Distributed training has been shown to greatly accelerate the training of RL agents with respect to wall clock time (Mnih et al., 2016; Espeholt et al., 2018). Instead of interacting with a single environment at a time, the agent interacts with a set of differently seeded copies of the environment to diversify experience and increase throughput. For simplicity and to ensure reproducibility, Tonic uses a synchronous training loop illustrated in Figure 3.

In addition to her roles as founder and CEO of fintech start-up Flamingo AI and as adjunct professor at the Australian Graduate School of Management, Dr Catriona Wallace is a campaigner for ethics in the artificial intelligence sector. She also helmed the second ever woman-led company to list on the ASX-listed company. Many people have concerns about our growing reliance on artificial intelligence. How do you feel about it? Artificial intelligence (AI) is the fastest-growing technology sector in the world, likely to replace 40 per cent of jobs in the next five years, especially in industries such as tourism, media, telecommunications and banking.

With the advent of cloud computing, e-commerce, and social media, it's difficult to keep tabs on who has access to our data, and harder still to know how much care they're taking with it -- barely a day goes by without some form of data-breach, lapse, or privacy scandal coming to the fore. But what constitutes "data-misuse" is covered by a broad gamut of scenarios that reach beyond poor security hygiene. Online tracking and profiling is rife -- it turns out there is a heap of money to be made from knowing where you are, what you do, and what you like. It all comes down to personalization: selling things, be it products, playlists, or a political ideology, based on who you are. The Facebook and Cambridge Analytical, which highlighted how social networks armed with vast banks of personal data could be leveraged to profile voters and micro-target with personalized political ads, was something of a watershed moment in terms of elevating the issue of data-privacy and abuse into the public consciousness.

If you've got "Red Dead Redemption 2" we've got tips and advice to help you USA TODAY Out for more than a week now, the video game "Red Dead Redemption 2" has generated blockbuster sales and stellar reviews. Want to know how the West can be won in Rockstar Games' open world adventure? We've gathered some advice and tips from the Rockstar brain trust and video game journalists who reviewed the game to aid your explorations in "Red Dead Redemption 2" ($60, for Microsoft Xbox One and Sony PlayStation 4, rated Mature for ages 17-up). Early in the game, your protagonist, Arthur Morgan, and the rest of the Van der Linde gang set up camp in the mountainous Horseshoe Overlook. As you earn money, you will want to use the ledger to improve the camp, says Matt Bertz, managing editor of Game Informer, who reviewed the game for the site.

We describe a neurobiologically plausible model to implement dynamic routing using the concept of neuronal communication through neuronal coherence. The model has a three-tier architecture: a raw input tier, a routing control tier, and an invariant output tier. The correct mapping between input and output tiers is realized by an appropriate alignment of the phases of their respective background oscillations by the routing control units. We present an example architecture, implemented on a neuromorphic chip, that is able to achieve circular-shift invariance.

We describe a neurobiologically plausible model to implement dynamic routing using the concept of neuronal communication through neuronal coherence. The model has a three-tier architecture: a raw input tier, a routing control tier, and an invariant output tier. The correct mapping between input and output tiers is realized by an appropriate alignment of the phases of their respective background oscillations by the routing control units. We present an example architecture, implemented on a neuromorphic chip, that is able to achieve circular-shift invariance.

We describe a neurobiologically plausible model to implement dynamic routing using the concept of neuronal communication through neuronal coherence. The model has a three-tier architecture: a raw input tier, a routing control tier, and an invariant output tier. The correct mapping between input and output tiers is realized byan appropriate alignment of the phases of their respective background oscillations by the routing control units. We present an example architecture, implemented ona neuromorphic chip, that is able to achieve circular-shift invariance.