Naturalistic driving studies use devices in participants' own vehicles to record daily driving over many months. Due to diverse and extensive amounts of data recorded, automated processing is necessary. This report describes methods to extract and characterize driver head scans at intersections from data collected from an in-car recording system that logged vehicle speed, GPS location, scene videos, and cabin videos. Custom tools were developed to mark the intersections, synchronize location and video data, and clip the cabin and scene videos for +/-100 meters from the intersection location. A custom-developed head pose detection AI model for wide angle head turns was run on the cabin videos to estimate the driver head pose, from which head scans >20 deg were computed in the horizontal direction. The scene videos were processed using a YOLO object detection model to detect traffic lights, stop signs, pedestrians, and other vehicles on the road. Turning maneuvers were independently detected using vehicle self-motion patterns. Stop lines on the road surface were detected using changing intensity patterns over time as the vehicle moved. The information obtained from processing the scene videos, along with the speed data was used in a rule-based algorithm to infer the intersection type, maneuver, and bounds. We processed 190 intersections from 3 vehicles driven in cities and suburban areas from Massachusetts and California. The automated video processing algorithm correctly detected intersection signage and maneuvers in 100% and 94% of instances, respectively. The median [IQR] error in detecting vehicle entry into the intersection was 1.1[0.4-4.9] meters and 0.2[0.1-0.54] seconds. The median overlap between ground truth and estimated intersection bounds was 0.88[0.82-0.93].

This study addresses the racial biases in facial expression recognition (FER) systems within Large Multimodal Foundation Models (LMFMs). Despite advances in deep learning and the availability of diverse datasets, FER systems often exhibit higher error rates for individuals with darker skin tones. Existing research predominantly focuses on traditional FER models (CNNs, RNNs, ViTs), leaving a gap in understanding racial biases in LMFMs. We benchmark four leading LMFMs: GPT-4o, PaliGemma, Gemini, and CLIP to assess their performance in facial emotion detection across different racial demographics. A linear classifier trained on CLIP embeddings obtains accuracies of 95.9\% for RADIATE, 90.3\% for Tarr, and 99.5\% for Chicago Face. Furthermore, we identify that Anger is misclassified as Disgust 2.1 times more often in Black Females than White Females. This study highlights the need for fairer FER systems and establishes a foundation for developing unbiased, accurate FER technologies. Visit https://kvjvhub.github.io/FERRacialBias/ for further information regarding the biases within facial expression recognition.

Errors or failures in a high-volume manufacturing environment can have significant impact that can result in both the loss of time and money. Identifying such failures early has been a top priority for manufacturing industries and various rule-based algorithms have been developed over the years. However, catching these failures is time consuming and such algorithms cannot adapt well to changes in designs, and sometimes variations in everyday behavior. More importantly, the number of units to monitor in a high-volume manufacturing environment is too big for manual monitoring or for a simple program. Here we develop a novel program that combines both rule-based decisions and machine learning models that can not only learn and adapt to such day-to-day variations or long-term design changes, but also can be applied at scale to the high number of manufacturing units in use today. Using the current state-of-the-art technologies, we then deploy this program at-scale to handle the needs of ever-increasing demand from the manufacturing environment.

Role requiring'No experience data provided' months of experience in San Francisco Pay if you succeed in getting hired and start work at a high-paying job first. Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. At Weights & Biases, our mission is to build the best developer tools for machine learning. Weights & Biases is a series C company with $200 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more. Reporting to the Head of Data Science, the Machine Learning Engineer (MLE) will own the interface between our Data Science Team and our Data Platform Team, while making the results of Data Science into ML Applications for the business.

Neural networks used for image classification tasks in critical applications must be tested with sufficient realistic data to assure their correctness. To effectively test an image classification neural network, one must obtain realistic test data adequate enough to inspire confidence that differences between the implicit requirements and the learned model would be exposed. This raises two challenges: first, an adequate subset of the data points must be carefully chosen to inspire confidence, and second, the implicit requirements must be meaningfully extrapolated to data points beyond those in the explicit training set. This paper proposes a novel framework to address these challenges. Our approach is based on the premise that patterns in a large input data space can be effectively captured in a smaller manifold space, from which similar yet novel test cases---both the input and the label---can be sampled and generated. A variant of Conditional Variational Autoencoder (CVAE) is used for capturing this manifold with a generative function, and a search technique is applied on this manifold space to efficiently find fault-revealing inputs. Experiments show that this approach enables generation of thousands of realistic yet fault-revealing test cases efficiently even for well-trained models.

There is a wide array of existing instruments used to assess childhood behavior and development for the evaluation of social, emotional and behavioral disorders. Many of these instruments either focus on one diagnostic category or encompass a broad set of childhood behaviors. We built an extensive ontology of the questions associated with key features that have diagnostic relevance for child behavioral conditions, such as Autism Spectrum Disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and anxiety, by incorporating a subset of existing child behavioral instruments and categorizing each question into clinical domains. Each existing question and set of question responses were then mapped to a new unique Rosetta question and set of answer codes encompassing the semantic meaning and identified concept(s) of as many existing questions as possible. This resulted in 1274 existing instrument questions mapping to 209 Rosetta questions creating a minimal set of questions that are comprehensive of each topic and subtopic. This resulting ontology can be used to create more concise instruments across various ages and conditions, as well as create more robust overlapping datasets for both clinical and research use.

NEW YORK – It would have been easy to turn on their computers at home over plates of leftover turkey and take advantage of the Black Friday deals most retailers now offer online. But across the country, thousands of shoppers flocked to stores on Thanksgiving or woke up before dawn the next day to take part in this most famous ritual of American consumerism. Shoppers spent their holiday lined up outside the Mall of America in Bloomington, Minnesota, by 4 p.m. Thursday, and the crowd had swelled to 3,000 people by the time doors opened at 5 a.m. In Ohio, a group of women was so determined, they booked a hotel room Thursday night to be closer to the stores. In New York City, one woman went straight from a dance club to a department store in the middle of the night.

Speedrunning charity event Summer Games Done Quick finished up this past Sunday. The recipient of the funds raised druing the event, which saw players rushing as fast as possible through titles like Animaniacs, Pikimin 3 and Rise of the Tomb Raider, went to benefit Doctors Without Borders. Gamers raised $2,122,529.20 for the medical charity that provides emergency aid to anyone afflicted by conflict, disasters, epidemics and more. Last year, the event raised $1.7 million for the same charitable organization; it also raised $1.2 million in 2016 for the Prevent Cancer Foundation. This year's Summer Games Done Quick was held at a hotel in Bloomington, Minnesota, and ran from June 24th through July 1st with more than 2,200 attendees.

At the 2017 Advanced Process Control (APC 2017) conference, several companies presented implementations of deep learning to find transistor defects, align lithography steps, and apply predictive maintenance. The application of neural networks to semiconductor manufacturing was a much-discussed trend at the 2017 APC meeting in Austin, starting out with a keynote speech by Howard Witham, Texas operations manager for Qorvo Inc. Witham said artificial intelligence has brought human beings to "a point in history, for our industry and the world in general, that is more revolutionary than a small, evolutionary step." People in the semiconductor industry "need to take what's out there and figure out how to apply it to your own problems, to figure out where does the machine win, and where does the brain still win?" At Seagate Technology, a small team of engineers stitched together largely packaged or open source software running on a conventional CPU to create a convolution neural network (CNN)-based tool to find low-level device defects. In an APC paper entitled Automated Wafer Image Review using Deep Learning, Sharath Kumar Dhamodaran, an engineer/data scientist based at Seagate's Bloomington, Minn.

Machine learning is the future of marketing, but what can you do today to apply it and get an edge over your competitors? There is a lot of buzz around machine learning and artificial intelligence. It seems everyday writers and experts are publishing articles and talking on the radio about how artificial intelligence and machine learning are going to change the world. Likewise, trending topics – such as self-driving cars and big data– have our piqued our collective interest in the potential of artificial intelligence and machine learning. The truth is, however, that we are in the early inception stages of machine learning and artificial intelligence.