Keepman.

Happy birthday bro wish you all the best!

Artist: PNL = Peac N' Love Real Name: Ademo and N.O.S (2 brothers) Birth Date /Place: Ademo Born in Paris December 26, 1986 , N.O.S born in Paris April 25 1989 Age: Ademo: 35 years N.O.S : 32 years Social status (Single / Married): Single Artist Picture: https://imgur.com/a/ND3tGDC Musical Genres: RAP Awards: Disque D'Or , Disque De Platine. Top 3 Songs (Names): PNL - Au DD , PNL - Le monde ou rien , PNL - Blanka Other Information: On April 5, 2019, PNL released their third album, Deux frères, which sold more than 100,000 copies in its first week of release, a record for French rap. The album's third single, "Au DD", has topped the French singles chart. They shot the music video for "Au DD" on the Eiffel Tower, becoming the first duo or band to do so.The album was nominated for IMPALA's European Independent Album of the Year Award. In June 2019, the duo presented their QLF collection of sneakers, tracksuits, jeans and T-shirts, followed by their appearance in the Off-White fashion show in Paris. In July, it was announced that Apple Music has signed PNL and will co-brand their music videos and promote special joint events. In the same month, PNL released the song "Tahia" in honor of Algeria after the country's victory in the 2019 Africa Cup of Nations. In July 2020, PNL released an hour-long tour documentary on Netflix France from a 2017 concert. In September 2020, Ademo was arrested in Paris on drug use and public disorder charges. The arrest was filmed by several fans and appeared to show him being held on the ground by police officers, and then handcuffed.

Until now, genomics research groups working with sensitive medical data were largely limited to using local Genome Browser installations to maintain confidentiality, complicating data-sharing among collaborators. Today, the Genome Browser group of the UC Santa Cruz Genomics Institute announced they have changed that by launching a new product, Genome Browser in the Cloud (GBiC). GBiC introduces new freedom to collaborate by allowing rapid Browser installation, in any UNIX-based cloud. Users provide the cloud instance, then install the Genome Browser image and grant access to whomever needs it. GBiC functions the same and is as secure as the public version of the Genome Browser, Genome Browser in a Box (GBiB), or a Genome Browser mirror site. Another GBiC innovation is significantly reduced installation time as compared to earlier Genome Browser versions. "We are very pleased with how this product facilitates remote collaboration—for example, between a hospital physician, an off-site lab technician and a third-party genomic researcher," said Genome Browser author and Principal Investigator Jim Kent. "Thanks to the efforts of GBiC Engineer Max Haeussler, users also benefit from significantly faster installation time," Kent continued. "What historically took at least a week, now typically is less than an hour," he said. While the GBiC is intended specifically for cloud-based installations, its functionality is versatile. For most purposes, the GBiC essentially replaces the manual installation process for mirroring the UCSC Genome Browser in multiple environments (cloud servers, dedicated servers, or even a laptop).

Samsung’s upcoming Galaxy S8 smartphone is expected to be the first to hit the market running Qualcomm’s much-improved Snapdragon 835 SoC. That should give the phone a leg-up on the competition out of the gate although as is often the case with Samsung flagships, some regional models will instead be powered by Samsung’s own Exynos-branded chip. The official Twitter account for Samsung’s Exynos processor division on Friday tweeted a teaser image of a chip accompanied by the tag line, “Discover cloud 9 with Exynos.” The tweet further told fans to be ready for #TheNextExynos which is “coming soon.” The type of processor you get in a Samsung flagship has traditionally been determined based on where you live. As CNET correctly highlights, the Exynos chips often come in phones sold in Asia while those in the US usually get Qualcomm’s chipset. The rare exception to this rule, however, was the Galaxy S6 which shipped with Samsung’s Exynos chips in all markets. It is believed that Samsung made this decision in order to avoid using Qualcomm’s toasty Snapdragon 810 processor. The Snapdragon 835 is expected to be 30 percent smaller than the Snapdragon 820. It’ll also consume 40 percent less power despite being 27 percent more powerful and with Quick Charge 4.0 technology, users should be able to gain five hours of battery life from just five minutes on a charger. Samsung’s Galaxy S8 is rumored to make its debut on March 29.

Altering a person's voice so that it sounds like another person is a useful technique for use in security and privacy, for example. This computational technique, known as voice conversion (VC), usually requires parallel data from two speakers to achieve a natural-sounding conversion. Parallel data requires recordings of two people saying the same sentences, with the necessary vocabulary, which are then time-matched and used to create a new target voice for the original speaker. However, there are issues surrounding parallel data in speech processing, not least a need for exact matching vocabulary between two speakers, which leads to a lack of corpus for other vocabulary not included in the pre-defined model training. Now, Toru Nakashika at the University of Electro-Communications in Tokyo and co-workers have successfully created a model capable of using non-parallel data to create a target voice - in other words, the target voice can say sentences and vocabulary not used in model training. Their new VC method is based on the simple premise that the acoustic features of speech are made up of two layers - neutral phonological information belonging to no specific person, and 'speaker identity' features that make words sound like they are coming from a particular speaker. Nakashika's model, called an adaptive restricted Boltzmann machine, helps deconstruct speech, retaining the neutral phonological information but replacing speaker specific information with that of the target speaker. After training, the model was comparable with existing parallel-trained models with the added advantage that new phonemic sounds can be generated for the target speaker, which enables speech generation of the target speaker with a different language.

The National Center for Atmospheric Research (NCAR) is launching operations this month of one of the world's most powerful and energy-efficient supercomputers, providing the nation with a major new tool to advance understanding of the atmospheric and related Earth system sciences. Named Cheyenne, the 5.34-petaflop system is capable of more than triple the amount of scientific computing performed by the previous NCAR supercomputer, Yellowstone. It also is three times more energy efficient. Scientists across the country will use Cheyenne to study phenomena ranging from wildfires and seismic activity to gusts that generate power at wind farms. Their findings will lay the groundwork for better protecting society from natural disasters, lead to more detailed projections of seasonal and longer-term weather and climate variability and change, and improve weather and water forecasts that are needed by economic sectors from agriculture and energy to transportation and tourism. "Cheyenne will help us advance the knowledge needed for saving lives, protecting property, and enabling U.S. businesses to better compete in the global marketplace," said Antonio J. Busalacchi, president of the University Corporation for Atmospheric Research. "This system is turbocharging our science." Cheyenne currently ranks as the 20th fastest supercomputer in the world and the fastest in the Mountain West, although such rankings change as new and more powerful machines begin operations. It is funded by NSF as well as by the state of Wyoming through an appropriation to the University of Wyoming. Cheyenne is housed in the NCAR-Wyoming Supercomputing Center (NWSC), one of the nation's premier supercomputing facilities for research. Since the NWSC opened in 2012, more than 2,200 scientists from more than 300 universities and federal labs have used its resources. "Through our work at the NWSC, we have a better understanding of such important processes as surface and subsurface hydrology, physics of flow in reservoir rock, and weather modification and precipitation stimulation," said William Gern, vice president of research and economic development at the University of Wyoming. "Importantly, we are also introducing Wyoming's school-age students to the significance and power of computing." The NWSC is located in Cheyenne, and the name of the new system was chosen to honor the support the center has received from the people of that city. The name also commemorates the upcoming 150th anniversary of the city, which was founded in 1867 and named for the American Indian Cheyenne Nation. Increased power, greater efficiency Cheyenne was built by Silicon Graphics International, or SGI (now part of Hewlett Packard Enterprise Co.), with DataDirect Networks (DDN) providing centralized file system and data storage components. Cheyenne is capable of 5.34 quadrillion calculations per second (5.34 petaflops, or floating point operations per second). The new system has a peak computation rate of more than 3 billion calculations per second for every watt of energy consumed. That is three times more energy efficient than the Yellowstone supercomputer, which is also highly efficient. The data storage system for Cheyenne provides an initial capacity of 20 petabytes, expandable to 40 petabytes with the addition of extra drives. The new DDN system also transfers data at the rate of 220 gigabytes per second, which is more than twice as fast as the previous file system's rate of 90 gigabytes per second Cheyenne is the latest in a long and successful history of supercomputers supported by the NSF and NCAR to advance the atmospheric and related sciences. "We're excited to provide the research community with more supercomputing power," said Anke Kamrath, interim director of NCAR's Computational and Information Systems Laboratory (CISL), which oversees operations at the NWSC. "Scientists have access to increasingly large amounts of data about our planet. The enhanced capabilities of the NWSC will enable them to tackle problems that used to be out of reach and obtain results at far greater speeds than ever." More detailed predictions High-performance computers such as Cheyenne allow researchers to run increasingly detailed models that simulate complex events and predict how they might unfold in the future. With more supercomputing power, scientists can capture additional processes, run their models at a higher resolution, and conduct an ensemble of modeling runs that provide a fuller picture of the same time period. "Providing next-generation supercomputing is vital to better understanding the Earth system that affects us all, " said NCAR Director James W. Hurrell. "We're delighted that this powerful resource is now available to the nation's scientists, and we're looking forward to new discoveries in climate, weather, space weather, renewable energy, and other critical areas of research." Some of the initial projects on Cheyenne include: *Long-range, seasonal to decadal forecasting: Several studies led by George Mason University, the University of Miami, and NCAR aim to improve prediction of weather patterns months to years in advance. Researchers will use Cheyenne's capabilities to generate more comprehensive simulations of finer-scale processes in the ocean, atmosphere, and sea ice. This research will help scientists refine computer models for improved long-term predictions, including how year-to-year changes in Arctic sea ice extent may affect the likelihood of extreme weather events thousands of miles away. *Wind energy: Projecting electricity output at a wind farm is extraordinarily challenging as it involves predicting variable gusts and complex wind eddies at the height of turbines, which are hundreds of feet above the sensors used for weather forecasting. University of Wyoming researchers will use Cheyenne to simulate wind conditions on different scales, from across the continent down to the tiny space near a wind turbine blade, as well as the vibrations within an individual turbine itself. In addition, an NCAR-led project will create high-resolution, 3-D simulations of vertical and horizontal drafts to provide more information about winds over complex terrain. This type of research is critical as utilities seek to make wind farms as efficient as possible. *Space weather: Scientists are working to better understand solar disturbances that buffet Earth's atmosphere and threaten the operation of satellites, communications, and power grids. New projects led by the University of Delaware and NCAR are using Cheyenne to gain more insight into how solar activity leads to damaging geomagnetic storms. The scientists plan to develop detailed simulations of the emergence of the magnetic field from the subsurface of the Sun into its atmosphere, as well as gain a three-dimensional view of plasma turbulence and magnetic reconnection in space that lead to plasma heating. *Extreme weather: One of the leading questions about climate change is how it could affect the frequency and severity of major storms and other types of severe weather. An NCAR-led project will explore how climate interacts with the land surface and hydrology over the United States, and how extreme weather events can be expected to change in the future. It will use advanced modeling approaches at high resolution (down to just a few miles) in ways that can help scientists configure future climate models to better simulate extreme events. *Climate engineering: To counter the effects of heat-trapping greenhouse gases, some experts have proposed artificially cooling the planet by injecting sulfates into the stratosphere, which would mimic the effects of a major volcanic eruption. But if society ever tried to engage in such climate engineering, or geoengineering, the results could alter the world's climate in unintended ways. An NCAR-led project is using Cheyenne's computing power to run an ensemble of climate engineering simulations to show how hypothetical sulfate injections could affect regional temperatures and precipitation. *Smoke and global climate: A study led by the University of Wyoming will look into emissions from wildfires and how they affect stratocumulus clouds over the southeastern Atlantic Ocean. This research is needed for a better understanding of the global climate system, as stratocumulus clouds, which cover 23 percent of Earth's surface, play a key role in reflecting sunlight back into space. The work will help reveal the extent to which particles emitted during biomass burning influence cloud processes in ways that affect global temperatures.

Google Translate has become a quick-and-dirty translation solution for millions of people worldwide since it debuted a decade ago. But Google’s engineers have been quietly tweaking their machine translation service’s algorithms behind the scenes. They recently delivered a huge Google Translate upgrade that harnesses the po[CENSORED]r artificial intelligence technique known as deep learning. Machine translation services such as Google Translate have mostly used a “phrase-based” approach of breaking down sentences into words and phrases to be independently translated. But several years ago, Google began experimenting with a deep-learning technique, called neural machine translation, that can translate entire sentences without breaking them down into smaller components. That approach eventually reduced the number of Google Translate errors by at least 60 percent on many language pairs in comparison with the older, phrase-based approach. “We believe we are the first using [neural machine translation] in a large-scale production environment,” says Mike Schuster, research scientist at Google. Many major tech companies have heavily invested in neural machine translation from a research standpoint, says Kyunghyun Cho, a deep-learning researcher at New York University with a focus on natural language processing. But he confirmed that Google seems to be the first to publicly announce its use of neural machine translation in a translation product. Google Translate has already begun using neural machine translation for its 18 million daily translations between English and Chinese. In a blog post, Google researchers also promised to roll out the improved translations to many more language pairs in the coming months. The deep-learning approach of Google’s neural machine translation relies on a type of software algorithm known as a recurrent neural network. The neural network consists of nodes, also called artificial neurons, arranged in a stack of layers consisting of 1,024 nodes per layer. A network of eight layers acts as the “encoder,” which takes the sentence targeted for translation—let’s say from Chinese to English—and transforms it into a list of “vectors.” Each vector in the list represents the meanings of all the words read so far in the sentence, so that a vector farther along the list will include more word meanings. Once the Chinese sentence has been read by the encoder, a network of eight layers acting as the “decoder” generates the English translation one word at a time in a series of steps. A separate “attention network” connects the encoder and decoder by directing the decoder to pay special attention to certain vectors (encoded words) when coming up with the translation. It’s not unlike a human translator constantly referring back to the original sentence during a translation. This represents an improved version of the original encoder-decoder method that would compress the starting sentence into a fixed-size vector, regardless of the original sentence’s length. The improved version was presented in a paper that includes Cho as coauthor. Cho, who is not affiliated with Google, explains the less accurate original encoder-decoder method as follows: If I made an analogy to a human translator, what this means is that the human translator is going to look at a source sentence once, memorize the whole thing and start writing down its translation without ever looking back at the source sentence. This is both unrealistic and extremely inefficient. Why wouldn't a translator look back at the source sentence over and over? Google started working on neural machine translation several years ago, but the method still generally proved less accurate and required more computational resources than the old approach of phrase-based machine translation. Better accuracy often came at the expense of speed, which is problematic for Google Translate users, who expect almost instantaneous translations. Google researchers had to harness several clever work-around solutions for their deep-learning algorithms to get beyond the existing limitations of neural machine translation. For example, the team connected the attention network to the encoder and decoder networks in a way that sacrificed some accuracy but allowed for faster speed through parallelism—the method of using several processors to run certain parts of the deep-learning algorithm simultaneously. “We believe some of our architectural choices are quite unique, mostly to allow maximum parallelism during computation while achieving good accuracy,” Schuster explains. Another innovation helped neural machine translation handle certain rare words. Part of Google’s solution to this came from the previous work of Schuster and his colleagues on improving the Google Japanese and Korean speech recognition systems. They figured out how to break down rare words into a limited set of smaller, common subunits called “wordpieces,” which the neural machine translation could handle more easily. A third innovation came from using “quantized computation” to reduce the precision of the system’s calculations and therefore speed up the translation process. Google’s team trained their system to tolerate the resulting “quantization errors” that could arise as a result. “Quantized computation is generally faster than nonquantized computation because all normally 32-bit or 64-bit data can be compressed into 8 or 16 bits, which reduces the time accessing that data and generally makes it faster to do any computations on it,” Schuster says. Google’s neural machine translation also benefits from running on better hardware than traditional CPUs. The tech giant is using a specialized chip designed for deep learning called the Tensor Processing Unit (TPU). The TPUs alone helped speed up translation by 3.5 times over ordinary chips. When combined with the new algorithm solutions, Google made its neural machine translation more than 30 times faster with almost no loss of translation accuracy. That huge speed boost made the difference in Google’s decision to finally begin using the deep-learning algorithms for Google Translate in Chinese-to-English translations. The results seem impressive enough to outside experts such as Cho. “I am extremely impressed by their effort and success in making the inference of neural machine translation fast enough for their production system by quantized inference and their TPU,” Cho says. Google Translate and other machine translation services still have room for improvement. For example, even the upgraded Google Translate still messes up rare words or simply leaves out certain parts of sentences without translating them. It also still has problems using context to improve its translations. But Schuster seems optimistic that machine translation services will continue to make future progress and creep ever closer to human capabilities. “If you look at the history of machine translation, you see a constant uptick of translation quality and speed, and we only see this [continuing] until the system is as good as a human in communicating information from one language to another,” Schuster says.

Back in the first half of 2015 Apple released the first version of the Apple Watch. The Apple Watch was a long-rumored product, often referred to as the iWatch before its release. At the time, it represented the best attempt that I had seen to provide a compelling smartwatch experience, but it was clearly a first generation product with flaws and shortcomings. It was not unlike the iPhone 2G or the iPad 1 in that regard, and for all the things it did well, there were other parts of the experience that really didn't deliver. While this shouldn't have been unexpected given the nature of first generation products, when a device is surrounded by so much hype for so many years, expectations can begin to run wild. On top of that, certain aspects like application performance were not up to the standards that are expected of a shipping product. In our review of the original Apple Watch we concluded that it was a good first attempt, but obviously flawed, and that ordinary consumers should wait for future iterations. Jumping to the present, Apple is back with the second generation of the Apple Watch, the aptly named Apple Watch Series 2. The launch of Apple Watch Series 2 comes two years after the original announcement of the Apple Watch. Even when you consider the six month gap between the first Apple Watch's announcement and launch, this still represents a longer time between versions than the yearly cadence that we've come to expect for many other products. Having a product in the market for one and a half years is a good span of time to observe how users are making use of it, what features they are and aren't using, and what parts of the experience create friction. For a first generation product this kind of information is essential to make the necessary improvements in future iterations, as taking the product in the wrong direction could doom its future prospects entirely. In addition to the improvements made in watchOS 3, Apple Watch Series 2 includes a number of hardware improvements. While one might think that specs are entirely irrelevant in a smartwatch, that actually couldn't be farther from the truth. Many of the issues with the original Apple Watch stem from various limitations in the hardware, particularly the slowness of the CPU and GPU. With Series 2 Apple has a chance to address many of these problems. I've compiled a table below with the specifications of both sizes of the original Apple Watch compared to their successors in Series 2. Internally, Apple has made some key changes that have a profound impact on the user experience. The most obvious is the new chip powering the watch. Apple's S2 SiP now has a dual core processor and an improved GPU. Apple rates it as 50% faster for CPU-bound workloads, and twice as fast for GPU-bound workloads. Apple has been known to state smaller gains than the theoretical doubling of performance when moving from a single core to a dual core CPU, and based on some investigation it appears that Apple has simply doubled up on CPU cores, adding another 520MHz ARM Cortex-A7 core to complement the first. Single-core/single-threaded performance appears unchanged, so getting better performance out of the S2 means putting that second core to work. As for the GPU, this is much harder to pin down. It's most likely the case that the Apple S1 SiP used the PowerVR GX5300 GPU, and I suspect that Apple is using Imagination Technologies' newer PowerVR "Rogue" architecture - likely some variant of the G6020 GPU - in the Apple S2. I say variant, as Apple's recent work with GPUs in their SoCs could be indicative that Apple does not need to use Imagination's reference design. Like the S1, the S2 is paired with 512MB of RAM. It's again hard to verify that this is LPDDR3 memory so I've marked that as speculative in the chart. I did want to note that other sources have reported 1GB of RAM for the S2, but I am fairly sure that this is not the case. iOS, and subsequently watchOS, provides an API for developers to query the number of CPU cores and amount of RAM available in the device, and it confirms that Apple has not increased the amount of RAM available in Apple Watch Series 2. Another major internal change is the battery. Apple has increased the battery capacity on the 38mm model by 32%, and the 42mm model by 36%. This will do well to offset the increased power requirements with the introduction of GPS in the Apple S2 SiP. Apple still rates the battery life for Series 2 at eighteen hours, and in my experience you could wear the watch for two days before having to recharge as long as you don't do too many workouts. However, I still charge it each night, and we're still not close to the point where you can wear a smartwatch for a week with both daytime tracking and sleep tracking. The last major hardware change in Series 2 is the display. Apple still uses a 326ppi OLED panel on both models, with the 38mm casing having a 1.32" display and the 42mm casing being a larger 1.5" display. What has changed is the peak brightness. One of the issues I encountered with the original Apple Watch was an inability to see what was on the screen when there was heavy glare. This was even more pronounced on the steel versions that use sapphire glass, which is more reflective than the Ion-X glass on the aluminum models. Apple rated the original Apple Watch displays at 450 nits of brightness, and with Series 2 they claim to have increased this to 1000 nits, which is an enormous improvement. Given that the Apple Watch is still a relatively new product, it's likely that many people have still not interacted with one before. Because of that, and the very personal nature of watches, it's worth covering the design in more detail, and so I'll talk about that next.