But the new Kryo 485 core includes something unusual: a "prime core." Typically, a Snapdragon chip includes four "performance" cores and four "efficiency" cores, the latter optimized for lower power. The Snapdragon 845 uses four ARM A75 cores at 2.8GHz and four A55 cores running at 1.8GHz. Qualcomm says the Kryo 485 within the Snapdragon 855 is 45 percent more powerful. Here are the speeds of each of the Snapdragon 855's Kryo cores. But there are some interesting differences between the 845 and the 855.

BT is removing Huawei technology from the phone networks amid fears that the Chinese government could be using its infrastructure to spy on citizens. The company says it will no longer use Huawei's equipment in its existing 3G and 4G networks, and that it would not use it to build the 5G ones that it is building at the moment. It is just the latest operator to announce that it would not use the Chinese companies equipment amid fears that phone messages they are relaying could be intercepted. New Zealand and Australia have stopped telecom operators using Huawei's equipment in new 5G networks because they are concerned about possible Chinese government involvement in their communications infrastructure. Huawei, the world's biggest network equipment maker ahead of Ericsson and Nokia, has said Beijing has no influence over its operations.

This morning, we have a very important message for you from Tom Cruise and news about the hardware that will be inside many of 2019's most popular phones. It's not a 5G world yet, but we're getting ready, and that might mean adjusting to some very strange notch placements. And a new fingerprint sensor that works from within the display.Qualcomm's Snapdragon 855 chipset will power your next flagship phone It's been clear for a while now that 2019 will be the year of 5G, and it's little surprise that the Snapdragon 855 will support "multi-gigabit" data speeds on 5G networks as they light up around the country. SVP Alex Katouzian also pointed out that the 855 was designed to trounce last-generation chipsets when it comes to AI performance -- we can expect up to three-fold performance gains when it comes to these complex computations. He even detailed a new way to sense fingerprints from inside an all-screen phone: with Qualcomm's new ultrasonic sensor.

Qualcomm SVP Alex Katouzian didn't dwell on the chipset for very long after announcing it at the company's Tech Summit in Hawaii -- the big details will apparently drop tomorrow -- but we now have a better sense of what the company wanted to focus on as we barrel into 2019. It's been clear for a while now that 2019 will be the year of 5G, and it's little surprise at this point that the Snapdragon 855 will support "multi-gigabit" data speeds on 5G networks as they light up around the country. The rapid development and rigorous work that went into next year's round of 5G network deployments were a big theme here at Tech Summit day 1, and it's not hard to see how insanely fast data speeds stand to change what we expect from our smartphones. Beyond that, Katouzian also pointed out that the 855 was designed to trounce last-generation chipsets when it comes to AI performance -- we can expect up to 3x performance gains when it comes to these complex computations. It's still relatively early days for software and services fueled by machine learning, but the shift in the industry seems almost palpable at this point. With these AI-focused updates, Qualcomm is clearly gearing up to compete with rivals like Apple as the fundamental nature of our software continues to change.

Deaf children face challenges learning to read. As their parents and teachers often don't know sign language, young ones can't always make the connection between words on the page and their own life experiences. Huawei aims to fix that with its StorySign app for Android. Point your phone at certain children's books and the app will use AI to translate individual words on the page to sign language performed by an avatar (created by Wallace and Gromit's Aardman Animations, no less). This not only helps children read, but can teach parents the sign language they'd need to tell the story later. The app is free on both Google Play and Huawei's own AppGallery, and it doesn't require a Huawei phone.

Qualcomm Ventures, the corporate venture capital arm of the chipmaker, has plans to invest up to $100 million in artificial intelligence. Specifically, Qualcomm says it will provide capital to startups building on-device AI, which is AI that runs on the end device, like a smartphone or vehicle, rather than in the cloud. The fund's leader, Qualcomm investment director Albert Wang (pictured), says on-device AI is the future. "Today's AI processing is very computationally intensive," Wang told TechCrunch. "When you're talking to Alexa, nothing is processed on your device, it gets taken to the cloud and gets scrunched there. There are a few problems with that -- performance deteriorates, it consumes a lot of bandwidth and there are privacy issues. Imagine you have an Alexa that is more private and user-friendly, you ask the questions and can get the answers instantly. It doesn't take the round trip all the way to the cloud."

Micron said in October it plans to invest up to $100 million in startups focusing on artificial intelligence, while Intel's venture capital arm has funneled more than $1 billion in recent years into the technology.

Data collection often involves the partial measurement of a larger system. A common example arises in the process collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with a measurement of the network consisting of these core nodes together with a potentially much larger set of fringe nodes that have links to the core. Given the ubiquity of this process for assembling network data, it becomes crucial to understand the role of such a core-fringe structure. Here we study how the inclusion of fringe nodes affects the standard task of network link prediction. One might initially think the inclusion of any additional data is useful, and hence that it should be beneficial to include all fringe nodes that are available. However, we find that this is not true; in fact, there is substantial variability in the value of the fringe nodes for prediction. In some datasets, once an algorithm is selected, including any additional data from the fringe can actually hurt prediction performance; in other datasets, including some amount of fringe information is useful before prediction performance saturates or even declines; and in further cases, including the entire fringe leads to the best performance. While such variety might seem surprising, we show that these behaviors are exhibited by simple random graph models.

Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches have great potentials to automate and optimize network configuration. Learning-based approaches face the challenges of learning a highly complex function for each base station and balancing the fundamental exploration-exploitation tradeoff while minimizing the exploration cost. Fortunately, in cellular networks, base stations (BSs) often have similarities even though they are not identical. To leverage such similarities, we propose kernel-based multi-BS contextual bandit algorithm based on multi-task learning. In the algorithm, we leverage the similarity among different BSs defined by conditional kernel embedding. We present theoretical analysis of the proposed algorithm in terms of regret and multi-task-learning efficiency. We evaluate the effectiveness of our algorithm based on a simulator built by real traces.

The widespread mobile devices facilitated the emergence of many new applications and services. Among them are location-based services (LBS) that provide services based on user's location. Several techniques have been presented to enable LBS even in indoor environments where Global Positioning System (GPS) has low localization accuracy. These methods use some environment measurements (like Channel State Information (CSI) or Received Signal Strength (RSS)) for user localization. In this paper, we will use CSI and a novel deep learning algorithm to design a robust and efficient system for indoor localization. More precisely, we use supervised autoencoder (SAE) to model the environment using the data collected during the training phase. Then, during the testing phase, we use the trained model and estimate the coordinates of the unknown point by checking different possible labels. Unlike the previous fingerprinting approaches, in this work, we do not store the {CSI/RSS} of fingerprints and instead we model the environment only with a single SAE. The performance of the proposed scheme is then evaluated in two indoor environments and compared with that of similar approaches.