Like many of you, I receive a variety of notifications by various means. Postal letters, email reminders, pop-ups on my laptop, audio signals on my mobile, highlighted chat application entries, text messages, phone calls, taps on the shoulder--the list is long! Thinking a bit more about this, one of the purposes of notification is to resynchronize otherwise asynchronous processes. You tell Google Assistant to set a timer for 15 minutes and go off to do something else. After 15 minutes, you get an audible reminder that the 15 minutes are up, and you should turn off the spaghetti before it turns to mush.

As such, indicators in the options market, such as options prices, implied volatility, and the Greeks, are seen as "smarter" compared to indicators in the securities market. Numerous studies have confirmed this perspective and have explored the discovery function of options implied volatility on securities prices. For instance, Ni et al. (2020) found that the degree of skewness in implied volatility smiles has a significant predictive ability for stock market returns, while Han and Li (2021) discovered that the difference between call and put implied volatility has significant predictive power for stock market returns. Additionally, there is more research on the predictive ability of options implied volatility on realized volatility, dating back to Latane and Rendleman (1976-05) reverse use of the BS formula to derive the implied standard deviation of options and constructing a weighted implied standard deviation (WISD) using delta-neutral weighting, which was found to predict actual volatility significantly better than methods based on historical volatility. In recent years, numerous studies have incorporated the VIX index and the HAR method proposed by Corsi (2009), achieving notable results in predicting stock market volatility Byun and Kim (2013); Zhang (2020); Wan and Tian (2023). Preprint submitted to Elsarticle May 18, 2024 However, indicators in the options market should not be treated as the gold standard.

This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.

Millions of people who enrolled in Medicaid during the COVID-19 pandemic risk losing coverage in the spring of 2023, leaving many worried about their healthcare coverage and many healthcare providers struggling to redetermine coverage. It's an anxiety-inducing situation, but robotic process automation (RPA) is on hand to help. States were required to keep people enrolled in Medicaid throughout the pandemic due to a decision made by the HHS declaring COVID-19 as a Public Health Emergency (PHE). However, PHE is set to end on April 11, 2023. And while the HHS has extended the PHE in the past, it's unlikely to do so again.

Santiago Atitlan, Guatemala – On a recent afternoon, the COVID-19 vaccination centre in the heart of the Indigenous Mayan town of Santiago Atitlan was quiet. The health centre had a vaccine supply, but demand was low. The lack of coordination of a Guatemalan government-led campaign to overcome vaccine hesitancy has resulted in the expiration of millions of doses across the country this year, critics have said, as more than half of the population remains unvaccinated. According to Juan Manuel Ramirez, an evangelical preacher in Santiago Atitlan, some community members have taken the vaccine, knowing it helps to protect against severe disease. But others have subscribed to conspiracy theories about its potential dangers.

Today's AI technology, much like humans, learns from examples. AI systems are developed on datasets containing text, images, audio, and other information that serve as a ground truth. By figuring out the relationships between these examples, AI systems gradually "learn" to make predictions, like which word is likely to come next in a sentence or whether objects in a picture are inanimate. The technique holds up remarkably well in the language domain, for example, where systems like OpenAI's GPT-3 can write content from essays to advertisements in human-like ways. But similar in character to humans, AI that isn't supplied fresh, new data eventually grows stale in its predictions -- a phenomenon known as model drift.

Whilst debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), are rapidly increasing in prevalence, we witness a continued integration of artificial intelligence into healthcare. While this promises improved detection and monitoring of breathing disorders, AI techniques are "data hungry" which highlights the importance of generating physically meaningful surrogate data. Such domain knowledge aware surrogates would enable both an improved understanding of respiratory waveform changes with different breathing disorders and different severities, and enhance the training of machine learning algorithms. To this end, we introduce an apparatus comprising of PVC tubes and 3D printed parts as a simple yet effective method of simulating both obstructive and restrictive respiratory waveforms in healthy subjects. Independent control over both inspiratory and expiratory resistances allows for the simulation of obstructive breathing disorders through the whole spectrum of FEV1/FVC spirometry ratios (used to classify COPD), ranging from healthy values to values seen in severe chronic obstructive pulmonary disease. Moreover, waveform characteristics of breathing disorders, such as a change in inspiratory duty cycle or peak flow are also observed in the waveforms resulting from use of the artificial breathing disorder simulation apparatus. Overall, the proposed apparatus provides us with a simple, effective and physically meaningful way to generate surrogate breathing disorder waveforms, a prerequisite for the use of artificial intelligence in respiratory health.

Do you ever wonder what goes behind bringing various levels of autonomy to vehicles? What the vehicle sees (perception) and how the vehicle predicts the actions of different agents in the scene (behavior prediction) are the first two steps in autonomous systems. In order for these steps to be successful, large-scale driving datasets are key. Driving datasets typically comprise of data captured using multiple sensors such as cameras, LIDARs, radars, and GPS, in a variety of traffic scenarios during different times of the day under varied weather conditions and locations. The Amazon Machine Learning Solutions Lab is collaborating with the Laboratory of Intelligent and Safe Automobiles (LISA Lab) at the University of California, San Diego (UCSD) to build a large, richly annotated, real-world driving dataset with fine-grained vehicle, pedestrian, and scene attributes. This post describes the dataset label taxonomy and labeling architecture for 2D bounding boxes using Amazon SageMaker Ground Truth. Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning (ML) workflows. These workflows support a variety of use cases, including 3D point clouds, video, images, and text.

In this blog, we'll thoroughly discuss the several use-cases available for WhatsApp Chatbots for Insurance. Let's begin with some context. In the 2009 American sports drama The Blind Side, protagonist Sandra Bullock delivers an oddly specific analogy. She talks about why defenders are an integral part of any sport. As every housewife knows, the first check you write is for the mortgage, but the second is for the insurance.

We've quickly come to accept that brands know as much about us as we know about ourselves. Facebook serves you ads for cat food after you talk about getting a cat. Target knows you're pregnant before you tell your friends and family. Even Instagram knows about your shameful predilection for Hallmark Christmas movies. So it stands to reason that fewer pics of you with your significant other on Instagram could signal to apps and brands that your relationship may be coming to an end.