We address your questions/suggestions below. If the paper is accepted to NeurIPS 2020, we will have one extra page. There are three players, each with two strategies Head and Tail. Indeed, we create more figures and combine them as a video. We also run with Optimistic MWU.

If you're one of the 8% of Americans who say they're willing to pay for AI, Google has a deal for you -- a 250 per month AI subscription. The company unveiled Google AI Ultra today, a plan with the biggest usage limits for Google's suite of AI tools and access to the highest versions of those tools. Google AI Ultra is intended for filmmakers, developers, and creative professionals and gives users access to tools like Veo, Imagen, Whisk, NotebookLM, and a new tool called Flow. Also: Google's popular AI tool gets its own Android app - how to use NotebookLM on your phone Subscribers also get a massive expansion in storage across Google platforms, plus YouTube Premium ( 13.99 per month on its own). Here's a full breakdown of what the new plan includes: Google said the current AI Premium plan is also getting an upgrade -- to Gemini AP Pro.

It was the first time an electric vehicle led the field at NASCAR's most famous race. Chevrolet made history at the 67th Daytona 500 by introducing the 2025 Blazer EV SS as the official pace car. This marked the first time an electric vehicle led the field at NASCAR's most iconic race, a striking symbol of how the automotive world is shifting toward electrification while still honoring its racing heritage. The Blazer EV SS isn't just any electric SUV; it's the quickest SS model Chevrolet has ever built, and it turned heads both on and off the track. JOIN THE FREE "CYBERGUY REPORT": GET MY EXPERT TECH TIPS, CRITICAL SECURITY ALERTS AND EXCLUSIVE DEALS, PLUS INSTANT ACCESS TO MY FREE "ULTIMATE SCAM SURVIVAL GUIDE" WHEN YOU SIGN UP!

Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors may reveal sensitive linking information in the data such as transaction information in financial network data. Protecting the privacy of graph data is challenging due to its interconnected nature. This work proposes a novel graph diffusion framework with edge-level differential privacy guarantees by using noisy diffusion iterates. The algorithm injects Laplace noise per diffusion iteration and adopts a degree-based thresholding function to mitigate the high sensitivity induced by low-degree nodes. Our privacy loss analysis is based on Privacy Amplification by Iteration (PABI), which to our best knowledge, is the first effort that analyzes PABI with Laplace noise and provides relevant applications. We also introduce a novel -Wasserstein distance tracking method, which tightens the analysis of privacy leakage and makes PABI practically applicable. We evaluate this framework by applying it to Personalized Pagerank computation for ranking tasks. Experiments on real-world network data demonstrate the superiority of our method under stringent privacy conditions.

In the press release for the Method 360 earbuds, Skullcandy called them its "most advanced audio experience to date." In listening to everything from indie rock, video game soundtracks, and podcasts, I can see why. The Skullcandy earbuds had a balance that matched up easily to other impressive budget earbuds I've tested. Whether I was listening to the Final Fantasy VII soundtrack or a live Daft Punk performance, these earbuds punched above a 100 price point. However, when listening to them side by side with the Bose earbuds, the Skullcandy earbuds felt muffled and muddier (though I wouldn't describe them as muddy on their own).

Temporal-difference learning with gradient correction (TDC) is a two time-scale algorithm for policy evaluation in reinforcement learning. This algorithm was initially proposed with linear function approximation, and was later extended to the one with general smooth function approximation. The asymptotic convergence for the on-policy setting with general smooth function approximation was established in [Bhatnagar et al., 2009], however, the non-asymptotic convergence analysis remains unsolved due to challenges in the non-linear and two-time-scale update structure, non-convex objective function and the projection onto a time-varying tangent plane. In this paper, we develop novel techniques to address the above challenges and explicitly characterize the non-asymptotic error bound for the general off-policy setting with i.i.d. or Markovian samples, and show that it converges as fast as O(1/ T) (up to a factor of O(log T)). Our approach can be applied to a wide range of value-based reinforcement learning algorithms with general smooth function approximation.

University of Waterloo researchers have developed new artificial intelligence (AI) technology that can accurately analyze pitcher performance and mechanics using low-resolution video of baseball games. The system, developed for the Baltimore Orioles by the Waterloo team, plugs holes in much more elaborate and expensive technology already installed in most stadiums that host Major League Baseball (MLB), whose teams have increasingly tapped into data analytics in recent years. Waterloo researchers convert video of a pitcher's performance into a two-dimensional model that PitcherNet's AI algorithm can later analyze. Those systems, produced by a company called Hawk-Eye Innovations, use multiple special cameras in each park to catch players in action, but the data they yield is typically available to the home team that owns the stadium those games are played in. To add away games to their analytics operation, as well as use smartphone video taken by scouts in minor league and college games, the Orioles asked video and AI experts at Waterloo for help about three years ago.

In this supplementary material we give more details about the datasets in Section 2, about the experiments in Sections 3 to 5, and we describe the spin-weighted spherical harmonic (SWSH) transform implementation in Section 6.

TL;DR: Windows 11 Pro is down to 14.97 through June 1, its lowest price to date (reg. It's usually an afterthought, and that might be a mistake. Whether you're on Windows 10 or Windows 11 Home, upgrading to Windows 11 Pro may be the difference between an ordinary and an extraordinary gaming experience. Windows 11 Pro introduces DirectX 12 Ultimate, delivering higher frame rates, improved ray tracing, and lower latency for a smoother gaming experience. If you want faster load times and better graphics, this is your chance to optimize your rig.