In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order $\sqrt{K T \ln(N/K)}$ for the worst-case regret, where $K$ is the number of actions, $N>K$ the number of experts, and $T$ the time horizon. This matches a previously known upper bound of the same order and improves upon the best available lower bound of $\sqrt{K T (\ln N) / (\ln K)}$. For the standard feedback model, we prove a new instance-based upper bound that depends on the agreement between the experts and provides a logarithmic improvement compared to prior results.

Rep. Bob Good, R-Va., joins'FOX & Friends' to discuss why he voted to oust Kevin McCarthy as speaker and how Republicans should move forward. Several House Republicans in a key bipartisan group have said they could soon see a mass exodus over their Democratic counterparts' role in Speaker Kevin McCarthy's ouster this week. "I'm really thinking strongly about leaving the Problem Solvers Caucus," Rep. Nicole Malliotakis, R-N.Y., told Fox News Digital. "I think there's a lot of Republicans who are disenchanted with the Democratic members of the Problem Solvers Caucus." McCarthy, R-Calif., became the first speaker of the House in U.S. history to be booted from the job after eight hardliners within his party joined with every Democrat to vote him out of it.

Rescue officials arrive at the scene to assess the damage of Tuesday afternoon's dramatic collapse. The New York City street where a parking garage collapsed, killing one person and resulting in five others pulled from the structure, remained closed a day later Wednesday, as investigators have yet to disclose the suspected cause behind the building reportedly with four active violations suddenly caving in by Lower Manhattan's Financial District. At a press conference Tuesday, NYC Department of Buildings Acting Commissioner Kazimir Vilenchik described how drone footage showed how the four-story building on Ann Street, between Nassau Street and William Street, "all the way pancaked, collapsed all the way to the cellar floor." He acknowledged that an active violation on the building dated to 2003. The buildings commissioner said an application was filed in 2010 but did not indicate whether the violation was corrected.

AI could generate insights into how an individual's ethnicity, age, gender, occupation and other factors could be linked to their health. When researchers trained an artificial intelligence (AI) system to use radiological images to distinguish patients with COVID-19 pneumonia from those with other respiratory diseases, the machine found a logical – but faulty – short cut. A radiologist would weigh up features of the images. "But the AI system learned to read the dates of the scan," says Antonio Esposito, professor of radiology at Vita Salute San Raffaele University in Milan. The computer, he explains, simply put all patients who entered the hospital in 2020 into the COVID-19 category.

What saved the project was competition. Esposito dropped the randomized deck approach in favor of looting enemies for cards, which opened up new possibilities for level design. In "Neon White," each card represents a weapon that, when discarded, activates a movement ability: double jump, dash, grapple and so on. Stringing these together in succession while picking up new cards from fallen foes is the second-to-second objective of any given level. Building levels around specific cards and combos allowed for more interesting challenges for both developer and player, but what really kept Esposito engaged was a message from a friend who demoed the project: "Here's how long it took me to beat this level."

"Figuring out when I needed more space or capacity--it was a mess before. We needed to get information from so many different points when we were planning. We never got the number correct," says Cardoso. "Now, I have an entire view of the infrastructure and visualization from the virtual machines to the final disk in the rack." AIOps brings visibility over the whole environment. Before deploying the technology, Cardoso was where countless other organizations find themselves: snarled in an intricate web of IT systems, with interdependencies between layers of hardware, virtualization, middleware, and finally, applications.

Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning.

Deep Learning (DL) have greatly contributed to bioelectric signals processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from Electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of Transfer Learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards generalizability of DL models applied on bioelectric signals, in particular by retrieving more representative datasets.

Imagine a world where "Alexa, make my wrinkles disappear" really works. Unfortunately we aren't there yet, but artificial intelligence and augmented reality have changed the way we experience beauty and aesthetics, and these new technologies are paving the way. FACE VALUE After the 2018 launch of Neutrogena's first-generation Skin360 app, which required a skin-scanning tool, the team polled its users and went back to the drawing board. One key learning: Consumers wanted a way to analyze their skin without the extra tool, as well as the ability to track the impact of certain products on their skin health. The result is a "180-degree selfie analysis of skin pixels and facial attributes powered by YouCam technology," says Dianne Rossetti, principal scientist for Johnson & Johnson Consumer Health.

Despite years of hype (and plenty of worries) about the all-conquering power of Artificial Intelligence (AI), there still remains a significant gap between the promise of AI and its reality for business. Tech firms have pitched AI's capabilities for years, but for most organisations, the benefits of AI remain elusive. It's hard to gauge the proportion of businesses that are effectively using artificial intelligence today, and to what extent. Adoption rates shown in recent reports fall anywhere between 20% and 30%, with adoption typically loosely defined as "implementing AI in some form". A survey led by KPMG among 30 of the Global 500 companies found that although 30% of respondents reported using AI for a selective range of functions, only 17% of the companies were deploying the technology "at scale" within the enterprise.