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Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set), and they do not explain why. We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.

In today's fast-paced world, accurately monitoring stress levels is crucial. Sensor-based stress monitoring systems often need large datasets for training effective models. However, individual-specific models are necessary for personalized and interactive scenarios. Traditional methods like Ecological Momentary Assessments (EMAs) assess stress but struggle with efficient data collection without burdening users. The challenge is to timely send EMAs, especially during stress, balancing monitoring efficiency and user convenience. This paper introduces a novel context-aware active reinforcement learning (RL) algorithm for enhanced stress detection using Photoplethysmography (PPG) data from smartwatches and contextual data from smartphones. Our approach dynamically selects optimal times for deploying EMAs, utilizing the user's immediate context to maximize label accuracy and minimize intrusiveness. Initially, the study was executed in an offline environment to refine the label collection process, aiming to increase accuracy while reducing user burden. Later, we integrated a real-time label collection mechanism, transitioning to an online methodology. This shift resulted in an 11% improvement in stress detection efficiency. Incorporating contextual data improved model accuracy by 4%. Personalization studies indicated a 10% enhancement in AUC-ROC scores, demonstrating better stress level differentiation. This research marks a significant move towards personalized, context-driven real-time stress monitoring methods.

Football, like everything else important in life, is about stories. People implant themselves into the narrative: where they were when they saw Maradona's handball, the strangers they hugged when Ole Gunnar Solskjær scored that historic last-minute winner at the 1999 Champions League final. No doubt new tales are already being conjured around Jude Bellingham's scissor kick against Slovakia in the dying seconds of Sunday's Euro 24 match. Sport is a nostalgia machine – and this is as true for video game simulations as it is for the real thing. Every gamer has their favourite footie sim, but for me, and many other players of my … ahem, vintage … it was Pro Evolution Soccer, numbers 3 to 6. This was the early 2000s, the age of the PlayStation 2. I was a writer for hire at Future Publishing, basically hanging out at its office in Bath, working mostly on the Official PlayStation magazine.

Mapping traversal costs in an environment and planning paths based on this map are important for autonomous navigation. We present a neurobotic navigation system that utilizes a Spiking Neural Network Wavefront Planner and E-prop learning to concurrently map and plan paths in a large and complex environment. We incorporate a novel method for mapping which, when combined with the Spiking Wavefront Planner, allows for adaptive planning by selectively considering any combination of costs. The system is tested on a mobile robot platform in an outdoor environment with obstacles and varying terrain. Results indicate that the system is capable of discerning features in the environment using three measures of cost, (1) energy expenditure by the wheels, (2) time spent in the presence of obstacles, and (3) terrain slope. In just twelve hours of online training, E-prop learns and incorporates traversal costs into the path planning maps by updating the delays in the Spiking Wavefront Planner. On simulated paths, the Spiking Wavefront Planner plans significantly shorter and lower cost paths than A* and RRT*. The spiking wavefront planner is compatible with neuromorphic hardware and could be used for applications requiring low size, weight, and power.

Daily monitoring of stress is a critical component of maintaining optimal physical and mental health. Physiological signals and contextual information have recently emerged as promising indicators for detecting instances of heightened stress. Nonetheless, developing a real-time monitoring system that utilizes both physiological and contextual data to anticipate stress levels in everyday settings while also gathering stress labels from participants represents a significant challenge. We present a monitoring system that objectively tracks daily stress levels by utilizing both physiological and contextual data in a daily-life environment. Additionally, we have integrated a smart labeling approach to optimize the ecological momentary assessment (EMA) collection, which is required for building machine learning models for stress detection. We propose a three-tier Internet-of-Things-based system architecture to address the challenges. We utilized a cross-validation technique to accurately estimate the performance of our stress models. We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings. Whereas using PPG data alone, the highest F1-score achieved is approximately 56\%, emphasizing the significance of incorporating both PPG and contextual data in stress detection tasks.

That can be a problem when trying to train models that might benefit from more data, but regulatory issues restrict that data's movements, according to Steve Irvine, co-founder and CEO of integrate.ai. "[For] a lot of industries, like health care, it's prohibited moving the data across jurisdiction, and so some of the most meaningful use cases that you and I would hope could come into the world -- and developers want to bring into the world -- are blocked because the data can't move," Irvine said. This is where federated learning can help. Federated learning allows for the training of AI models by shifting the paradigm to bring the training function to the data, Irvine told The New Stack. "Instead of data having to come to a central location to train the machine learning model, versions of the model gets sent out to the location where the data resides," he explained.

A team of scientists has used GPU-accelerated deep learning to show how color can be brought to night-vision systems. In a paper published this week in the journal PLOS One, a team of researchers at the University of California, Irvine led by Professor Pierre Baldi and Dr. Andrew Browne, describes how they reconstructed color images of photos of faces using an infrared camera. The study is a step toward predicting and reconstructing what humans would see using cameras that collect light using imperceptible near-infrared illumination. The study's authors explain that humans see light in the so-called "visible spectrum," or light with wavelengths of between 400 and 700 nanometers. Typical night vision systems rely on cameras that collect infrared light outside this spectrum that we can't see.

This post contains a list of the AI-related seminars that are scheduled to take place between 13 January 2022 and 28 February 2022. All events detailed here are free and open for anyone to attend virtually. Resource-Efficient Execution of Deep Learning Computations Speaker: Deepak Narayanan (Microsoft Research) Organised by: Stanford MLSys Join the email list to get notified of the speaker and livestream link each week. Accurate privacy accounting for differentially private machine learning Speaker: Antti Honkela (University of Helsinki) Organised by: Finnish Centre for AI The Zoom link is here. Title to be confirmed Speakers: Been Kim (Google Brain) Organised by: Trustworthy ML Join the mailing list for instructions on how to sign up, or check the website a few days beforehand for the Zoom link.

The wildfires raging across BC are destructive and costly, but an advisor for the United Nations (UN) believes there's a solution that lies a little outside of the box. Neil Sahota is an IBM Master Inventor, UN Artificial Intelligence (AI) subject matter expert, and Professor at UC Irvine, a University in Irvine, California. Sahota sat down with MyPGNow and discussed how AI could help predict and fight fires across BC, and some of the utilizations of AI already in place across the world. "A lot of the focus is on early detection. We know that there are different variables in play that could trigger a wildfire. So we look at three things: fuel, oxygen, and energy," said Sahota.