Multipactor is a nonlinear electron avalanche phenomenon that can severely impair the performance of high-power radio frequency (RF) devices and accelerator systems. Accurate prediction of multipactor susceptibility across different materials and operational regimes remains a critical yet computationally intensive challenge in accelerator component design and RF engineering. This study presents the first application of supervised machine learning (ML) for predicting multipactor susceptibility in two-surface planar geometries. A simulation-derived dataset spanning six distinct secondary electron yield (SEY) material profiles is used to train regression models - including Random Forest (RF), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), and funnel-structured Multilayer Perceptrons (MLPs) - to predict the time-averaged electron growth rate, $δ_{avg}$. Performance is evaluated using Intersection over Union (IoU), Structural Similarity Index (SSIM), and Pearson correlation coefficient. Tree-based models consistently outperform MLPs in generalizing across disjoint material domains. MLPs trained using a scalarized objective function that combines IoU and SSIM during Bayesian hyperparameter optimization with 5-fold cross-validation outperform those trained with single-objective loss functions. Principal Component Analysis reveals that performance degradation for certain materials stems from disjoint feature-space distributions, underscoring the need for broader dataset coverage. This study demonstrates both the promise and limitations of ML-based multipactor prediction and lays the groundwork for accelerated, data-driven modeling in advanced RF and accelerator system design.

This content is not available in your location. Watch live coverage from every court on BBC iPlayer. Could you return a professional tennis player's serve? Video, 00:03:11 Could you return a professional tennis player's serve? Watch: Humanoid robots stumble through football match in China.

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial modalities when estimating confidence. Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted. To address the issue, we introduce an intuitive principle for multimodal learning, i.e., the confidence should not increase when one modality is removed. Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness.

Saildrone Explorer unmanned surface vessels (USV) operate with USS Delbert D. Black (DDG-119) on Oct. 7, 2022. The Navy is thinking about artificial intelligence in two ways: the infrastructure to make unmanned systems work and technology meant to enhance how sailor and their commanders make decisions, a panel of technical and policy experts said Tuesday. The output provided by AI is there help the human or supplement manned operations with unmanned assets, said Brett Vaughan, Navy Chief AI Officer, speaking at the U.S. Naval Institute on Tuesday. A human will always be in the loop and play a central role. "By and large, the AI is there to augment and provide a human decision maker a range of options and recommendations," Vaughan said.

Lee Vaughan is a programmer, pop culture enthusiast, educator, and author of Impractical Python Projects (No Starch Press, 2018). As an executive-level scientist at ExxonMobil, he constructed and reviewed computer models, developed and tested software, and trained geoscientists and engineers. He wrote both Impractical Python Projects and Real-World Python to help self-learners hone their Python skills and have fun doing it! You can think of this as your second Python book. It's designed to follow and complement either a complete beginner's book or an introductory class.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A small-town Idaho police department and community have vowed to "leave no stone unturned" in the continued search for a 5-year-old boy who vanished near his home nearly three weeks ago. Since the disappearance of Michael Joseph Vaughan in Fruitland on the evening of July 27, investigators have yet to find any signs of the missing child. "As we head into the weekend, and as people gather to enjoy time together, please take a moment to be grateful for the family and friends around you," the Fruitland Police Department said Friday.

The Navy, through its Office of Naval Research, is pursuing artificial intelligence applications across a broad spectrum of the service's responsibilities to man, train and equip, as well as warfighting, sustainment and readiness. Such a wide range of applications and algorithms come with specific data requirements and data management. Curtis Pelzer, chief information officer at the Office of Naval Research, said ONR's data resides in in several places on their network, and it's the job of the data and analytics team to make sure information is provided and kept in the right sets. AI can help reduce toil across the Navy, give autonomy with unmanned systems, and software codes can increase the speed and quality of human decision-making, according to Brett Vaughan, the Navy's chief artificial intelligence officer and Office of Naval Research portfolio manager. Vaughan said any data could potentially fuel AI, but it depends on what problem one aims to solve.

By no means am I a master, but on my 23rd try, I created the perfect conditions to eradicate the human race. First I zombified humans; another time, apes contracted the simian flu and gained self-awareness. On the 23rd attempt, I went old school with a simple germ. I even gave this ultimate pathogen a name. As Bob spread from person to person, red dots cropped up all over the world map.

Several years ago, Canary Pete's political cartoon flooded email inboxes and social media pages. The humorous illustration showed a middle-aged executive walking into a typical job interview, with the exception that he had to build his own office chair (since he was applying to work at IKEA). Pete's satire might be short-lived, as last week the robotics industry achieved a new milestone – building an IKEA chair in less than 10 minutes! It is unclear how soon bots will be replacing human assemblers like TaskRabbit, but in the the words of Jackie DeChamps, Chief Operating Officer of IKEA USA,"We are always looking at ways we can innovate and help make our customers' lives at home easier." Addressing the elephant in the room, I debated a colleague earlier this month at The Frontier Conference in New Orleans. I expressed that it is critical for mechatronic companies to engage early with organized labor for successful deployments.

Cognoa has gained regulatory recognition for its machine learning software as a class II diagnostic medical device for autism -- meaning the digital health startup is now positioned to submit an application for full FDA clearance. It's a first but important regulatory step for a business that was founded back in 2014, and plays in a still nascent digital health space where untested'wellness' apps are far more plentiful than medical technologies with robust data to prove out the efficacy of their interventions. Discussions with the FDA started in early 2017, says Cognoa CEO Brent Vaughan, adding that it's hoping to gain full FDA clearance this year. He says the ultimate goal for the US startup is to become a standard part of domestic health insurance-covered medical provision -- and for that FDA clearance is essential to opening the doors. We first covered the Cognoa at launch in 2014 and the following year when it was still being careful to describe its technology as a screening rather than a diagnostic system.