We provide the details of the proposed latency-driven fast slimming in Alg. 1. Formulations of the Our major conclusions and speed analysis can be found in Sec. 3 and Figure 1. Compared to non-overlap large-kernel patch embedding (V5 in Tab. MHSA with the global receptive field is an essential contribution to model performance. By comparing V1 and V2 in Tab. 3, we can observe that the GN We explore ReLU and HardSwish (V3 and V4 in Tab. 3) in addition to GeLU We draw a conclusion that the activation function can be selected on a case-by-case basis depending on the specific hardware and compiler. In this work, we use GeLU to provide better performance than ReLU while executing faster.

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design.

If your iPhone or iPad is over five years old, you'll miss out on the wave of new features and applications coming this fall with Apple's upgrade to iOS 17. The iPhone X, the first to feature FaceID, iPhone 8 and iPhone 8 Plus will not support the new operating system. That means the iPhone XS, XS Max, and XR will be the oldest devices capable of running the new operating system when available later this year. The end of having to say'Hey' to summon Siri, a new Journal app for tracking your life goals, improvements to autocorrect, and a'Check In' feature to let friends and loved ones know you've arrived home safely are among iOS 17's new features. But only the most advanced iPhones, from 2021's iPhone 12 and newer, will be able to control their device with hand gestures during a FaceTime call, unlocking fun augmented reality (AR) reaction graphics.

We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. These products always have only few interactions thereby giving rise to a perpetual soft item cold start situation. Modern collaborative filtering methods solve cold start using content attributes and exploit the existing implicit signals from warm start items. This approach fails in our use-case since our entire item set faces cold start issue always. Our Product Graph has over 500 Million nodes and over 5 Billion edges which makes training and inference using modern graph algorithms very compute intensive. To overcome these challenges we propose a system which reduces the dataset size and employs an improved modelling technique to reduce storage and compute without loss in performance. Particularly, we reduce our graph size using a filtering technique and then exploit this reduced product graph using Weighted Averaging of Messages over Layers (WAML) algorithm. WAML simplifies training on large graphs and improves over previous methods by reducing compute time to 1/7 of LightGCN and 1/26 of Graph Attention Network (GAT) and increasing recall$@100$ by 66% over LightGCN and 2.3x over GAT.

Last weekend The New Yorker published an essay by Kyle Chayka with a headline guaranteed to pique my interest and raise my hackles: "Have iPhone Cameras Become Too Smart?" (March 18, 2022). Aside from being a prime example of Betteridge's Law of Headlines, it feeds into the idea that computational photography is a threat to photographers or is somehow ruining photography. The subhead renders the verdict in the way that eye-catching headlines do: "Apple's newest smartphone models use machine learning to make every image look professionally taken. That doesn't mean the photos are good." The implication there, and a thrust of the article, is that machine learning is creating bad images.

Hit play on the player above to hear the podcast and follow along with the transcript below.This transcript was automatically generated, and then edited for clarity in its current form. There may be some differences between the audio and the text. Welcome back to Talking Tech. If you're familiar with the iPhone, especially some of the newer models, then you likely know about Face ID. And one of the big hurdles with using face ID in the last couple years has been when you're wearing a mask.

Apple's transition to its own processors is nearly complete. The company's recent spring event saw the debut of the Mac Studio and its M1 Ultra processor -- its most powerful piece of silicon yet. But it also revealed what the future of Apple's computers could look like. For the first time, all of Apple's chips are on the table. The first crucial takeaway is that Apple is now a force to be reckoned with when it comes to chips (if it wasn't already).

Nearly two years since the Covid pandemic started, Apple is finally letting iPhone users unlock their iPhone with Face ID when wearing a face mask. Face ID, Apple's facial recognition system, has reportedly received the all-important update as part of iOS 15.4, which is now going through beta testing. Florida-based iOS user Brandon Butch, one of the early testers, has posted screenshots of the new feature to Twitter. It seems iPhone users have the option to turn the tool on and off, although Apple says Face ID is more effective while being able to see the entire face. Since the start of the pandemic, getting Face ID to automatically recognise our face to unlock our iPhones has been a bugbear as we've had masks over our faces.

On paper, the iPhone 13 and 13 mini aren't much to get excited about. Apple's subtle refinement on the iPhone 12 models will be familiar if you've paid attention to developments in the Android world. Some of the changes are impressive, like bringing the iPhone 12 Pro Max's excellent camera hardware to smaller phones and lower price points. Others, like a slightly smaller notch, bigger batteries, brighter displays, faster chips and expanded 5G support feel incremental. Still, they add up to make the iPhone 13 mini and iPhone 13 feel like worthwhile upgrades, especially to those looking to upgrade from older iPhones.

Active Noise Cancelling (ANC) headphones are a modern marvel with one major drawback: Many will unintentionally wipe out some of the frequencies present in the music you're listening to. Yamaha says its $299 YH-E700A ANC headphone delivers nearly identical sound whether its active noise cancellation is on, off, or in transparency mode. I'd say Yamaha has at least come closer to that goal than any other headphone I've reviewed, and that should make an extremely appealing set of cans for those who eschew active noise cancellation because they feel the technology has an adverse impact on musical recordings. That said, the YH-E700A aren't without a few other wrinkles that Yamaha will hopefully iron out via future firmware updates. The Yamaha YH-E700A adds several features you won't find in its less-expensive noise-cancelling stablemate, the YH-E500 ($179.95):