Google turned out three new phones this past year, and we were particularly fond of the $459 Pixel 4a 5G. It has nearly all the same features as the budget-friendly Pixel 4a, but packs in a bigger screen, a second wide-angle camera and a 5G radio, making it more future-proof than its predecessor. It doesn't offer wireless charging or water resistance like the $700 Pixel 5, but then again, it costs $200 less. Here, we've recommended the best and most useful accessories for what's sure to be one of the most popular phones of the year. One of the first accessories you'll want to get for your Pixel 4a 5G (or any smartphone, really) is a case to prevent it from getting too damaged from the occasional drop.
When the new M1 Macs came out in November, we were impressed with their performance specs but also worried that the new ARM-based processors would have compatibility issues with many older, x86 based apps that users have come to love on MacOS. We thankfully saw many companies (such as Google, Adobe, and Blizzard) rushing to release M1 versions of their software right at launch, and native support has only gotten better since. For the stragglers still running on x86 architecture, Apple's new Rosetta2 emulator does a fantastic job of providing a seamless experience for users--most people won't even notice that apps like Steam aren't running natively on the M1 Macs. The M1 MacBook Pro 13 is undoubtedly the best MacBook Pro 13 we've seen in a long time. Apart from its blazing speeds in single and multi-core performance, its integrated graphics are actually a bit ahead of both AMD- and Intel-based machines (although the graphics performance is still a far cry from that seen from discrete GPUs like the AMD Radeon and Nvidia GeForce RTX cards).
One of the latest collaborations between artificial intelligence and humans is further evidence of how machines and humans can create better results when working together. Artificial intelligence (AI) is now on the job to combat the spread of misinformation on the internet and social platforms thanks to the efforts of start-ups such as Logically. While AI is able to analyze the enormous amounts of info generated daily on a scale that's impossible for humans, ultimately, humans need to be part of the process of fact-checking to ensure credibility. As Lyric Jain, founder and CEO of Logically, said, toxic news travels faster than the truth. Our world desperately needs a way to discern truth from fiction in our news and public, political and economic discussions, and artificial intelligence will help us do that.
People have long used Tasker to take care of repetitive tasks on their Android device, or to customize its features based on things like whether they're at home or at the office. Now the app's features are a little easier to use since you can trigger them via Google Assistant. XDA points out a post by the developer on Reddit where he points out the currently available triggers, which you can use to run your favorite automations by name. Tasker is an incredibly powerful utility, but it can be a bit complex and intimidating, and voice control could be the difference in making it usable on a regular basis around your home. If you have the Google Play Pass subscription then access is free, and there's also a seven day trial available, otherwise it costs $3.49 in the Play Store.
Resembling a sawed-off section of stovepipe in black or PVC in white, the original Amazon Echo was the anti-smartphone. It operated tethered to an outlet, it was communal. And it was pre-pandemically touch-free if you didn't care about muting its microphone. Unbound by a display, it inspired voice-driven variants that ranged in size from tiny rings to giant rigs. We've hand-picked 11 smart displays that will satisfy a range of wants and needs.
The UK's data regulator is writing to WhatsApp to demand that the chat app does not hand user data to Facebook, as millions worldwide continue to sign up for alternatives such as Signal and Telegram to avoid forthcoming changes to its terms of service. Elizabeth Denham, the information commissioner, told a parliamentary committee that in 2017, WhatsApp had committed not to hand any user information over to Facebook until it could prove that doing so respected GDPR. But, she said, that agreement was enforced by the Irish data protection authority until the Brexit transition period ended on 1 January. Now that Britain is fully outside the EU, ensuring that those promises are being kept falls to the Information Commissioner's Office. "The change in the terms of service, and the requirement of users to share information with Facebook, does not apply to UK users or to users in the EU," Denham told the digital, culture, media and sport sub-committee on online harms and disinformation, "and that's because in 2017 my office negotiated with WhatsApp so that they agreed not to share user information and contact information until they could show that they complied with the GDPR."
Communication is more important than ever, with everything from college to CrossFit going virtual during the COVID-19 pandemic. Nobody understands this better than 2020 Marconi Prize recipient Andrea Goldsmith, who has spent her career making the wireless connections on which we rely more capable and stable. A pioneer of both theoretical and practical advances in adaptive wireless communications, Goldsmith spoke about her work on multiple-input and multiple-output (MIMO) channel performance limits, her new role as the incoming dean at Princeton University's School of Engineering and Applied Science, and what's next for networking. As an undergrad, you studied engineering at the University of California, Berkeley. What drew you to wireless communications?
Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas, such as network traffic engineering, medical image reconstruction, acoustics, astronomy, and many more. Unfortunately, most of the common approaches for solving SRP do not scale to large problem sizes. We propose a novel and scalable algorithm for solving this critical problem. Specifically, we make four major contributions. First, we propose a dual formulation of the problem and develop the DIRECT algorithm that is significantly more efficient than the state of the art. Second, we show how adapting database techniques developed for scalable similarity joins provides a substantial speedup over DIRECT. Third, we describe several practical techniques that allow our algorithm to scale--on a single machine--to settings that are orders of magnitude larger than previously studied. Finally, we use the database techniques of materialization and reuse to extend our result to dynamic settings where the input to the SRP changes. Extensive experiments on real-world and synthetic data confirm the efficiency, effectiveness, and scalability of our proposal. The database community has been at the forefront of grappling with challenges of big data and has developed numerous techniques for the scalable processing and analysis of massive datasets. These techniques often originate from solving core data management challenges but then find their way into effectively addressing the needs of big data analytics. We study how database techniques can benefit large-scale signal reconstruction,13 which is of interest to research communities as diverse as computer networks,15 medical imaging,7 etc. We demonstrate that the scalability of existing solutions can be significantly improved using ideas originally developed for similarity joins5 and selectivity estimation for set similarity queries.3 Signal reconstruction problem (SRP): The essence of SRP is to solve a linear system of the form AX b, where X is a high-dimensional unknown signal (represented by an m-d vector in Rm), b is a low-dimensional projection of X that can be observed in practice (represented by an n-d vector in Rn with n m), and A is an n m matrix that captures the linear relationship between X and b.
Anna Maria Feit (email@example.com) is a professor at Saarland University, Germany. This work was done while a researcher at Aalto University and ETH Zurich, Switzerland. Mathieu Nancel is a research scientist in the Loki research group at Inria Lille–Nord Europe; Lille, France. Maximilian John is a researcher at Max Planck Institute for Informatics, Saarbrücken, Germany. Andreas Karrenbauer is a senior researcher at Max Planck Institute for Informatics.
When problems are scaled to "big data," researchers must often come up with new solutions, leveraging ideas from multiple research areas--as we frequently witness in today's big data techniques and tools for machine learning, bioinformatics, and data visualization. Beyond these heavily studied topics, there exist other classes of general problems that must be rethought at scale. One such problem is that of large-scale signal reconstruction:4 taking a set of observations of relatively low dimensionality, and using them to reconstruct a high-dimensional, unknown signal. This class of problems arises when we can only observe a subset of a complex environment that we are seeking to model--for instance, placing a few sensors and using their readings to reconstruct an environment's temperature, or monitoring multiple points in a network and using the readings to estimate end-to-end network traffic, or using 2D slices to reconstruct a 3D image. The following paper is notable because it scalably addresses an underserved problem with practical impact, and does so in a clean, insightful, and systematic way. This signal reconstruction problem (SRP) is typically approached as an optimization task, in which we search for the high-dimensional signal that minimizes a loss function comparing it to the known properties of the signal.