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 (firstname.lastname@example.org) 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.
Last October, Endel announced that it had partnered with Grimes for a special'soundscape' inside its music app, which is meant to help you focus, relax or fall asleep. The collaboration was called AI Lullaby and, as you might have guessed from the name, combined artificial intelligence with Grimes' original music and vocals. There was just one problem: it was only available on iOS. The slumber sounds have now disappeared for iPhone users and, as promised, hopped over to Endel's Android application. If you own a phone or tablet powered by Google's operating system, it should be accessible the next time you open the app.
Battery-powered security cameras are a great option for outdoor use, because they remove the logistical hassle of finding a convenient electrical outlet to power them. But their easier installation comes with a cost, as they tend to be priced higher than their AC-powered counterparts. The $139 Vacos Cam would seem to be the best of both worlds, then--supremely flexible, modestly priced. Unfortunately, testing revealed this camera to be far from a polished product. While its video quality and smart motion detection are solid, its barely baked app makes the camera virtually unusable. The camera is the latest to crib its look from the Arlo line of home security cameras, in this case the Arlo Go (except that camera connects to the internet via an onboard LTE radio).
The more time you spend at home, the more you start to notice things that need upgrading. Want to invest in a smart doorbell? You've probably already heard of the Ring Video Doorbell, which features an advanced 1080P HD camera, two-way audio, and customizable motion sensors. So, next time you get a contactless delivery, you can still communicate with (and thank) the delivery person. The Ring Doorbell also includes infrared night vision and motion-activated alerts that can be sent straight to your smartphone.
The U.S. government is tasked with protecting classified data and combating potential threats, an area of growing concern with the increasing use of web-based applications required for remote working. Due to high demands, the teams tasked with safeguarding data need a new way--or new capabilities--to scale cybersecurity efforts, especially as many government agencies also face the challenge of limited resources and massively growing data sets and feeds. Pushed by the pandemic, governments are accelerating digital transformation efforts to implement artificial intelligence for cybersecurity needs, as it brings capabilities beyond what manual human surveillance can provide. In fact, the Defense Department's investment in AI has increased from $600 million in fiscal 2016 to $2.5 billion in fiscal 2021. The security operations center is the "mothership" of security within government agencies.