Leaderboard

No More spam Messages About acces Admin ! we dont give Free Admin Direct ... who want acces Admin must Make Request ( have 60 hours + daily actif ). no need Make Fake Account Old admin ! so create New Name And play hours necessary For Admin Good luck

One day those who never believed in you will talk about when they met you.

Voleyball game in 1H20Mins, preparing for rekt people.! Good luck guys see u later, dont think i leave i still here.!

Do as you would be done عامل الناس كما تحب أن يعاملوك

Playing vs @Equin0x with a massive lag be like

In Romania it takes 5 minutes. In Lebanon it takes more then 3 days.

My Friend @aNaKoNDa I Have Gift For U ! I Hope u Like That Hahahaha !

30 Friends, 29 Judas. Nice play CSBD.

It is very hard for abstinence. I was trying to live as a vicious once I had a bad dream :((

Free VIPs for all - connect streetzp

One more draw Mother be like

hardest name in mama africa xD

congra bro nice to see you in staff again

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.

I rrly don't understand some people i made this nickname "baby" 3 or maybe 4 years ago cuz i love that name so i start playing with that name in streetzm in shadowszm ... now i entered differents servers they told me i'm the fake baby , listen carefull all THERE IS ONLY ONE BABY IN THIS WHOLE CSBD AND HE IS FROM BELGIUM ! i rrly got sick of people who steal my name

CRISTIANO RONALDO is set to star in a TV show alongside Angelina Jolie, according to reports in Turkey. The Real Madrid superstar will appear in a series about a Syrian family fleeing the country’s civil war. Ronaldo, 32, often visits the country while Hollywood actress Jolie is well known for her charity work. Eyup Dirlik – the director Hayat Koprusu – was quoted in Turkish Football confirming the star duo’s involvement. He said: “We will begin filming in the first week of April, the series is about the plight of a refugee family and what they go through. “There will be appearances from actors and actresses from all over the world including Cristiano Ronaldo, Angelina Jolie and Nancy Ajram.” Ronaldo helped Turkish Football Federation president Yildirim Demiroren open his shopping mall and has other business interests in the country. The four-time Ballon d’Or winner is set to make a staggering £800million with Nike just for posting through his social media. He generated over £422million for the sports company in 2016 just by posting to his Twitter, Facebook and Instagram accounts. Ronaldo posted 1,703 times on social media during the 2016 calendar year, which according to digital media tool Hookit, generated over 2.25billion interactions.

Who Can Help Me When I Start Counter-Strike With OpenGL or D3D Graphics I Hawe Wery Lag In These Graphics Who Can Help Me, Thanks

congratulations dude welcome to staff csblackdevil community

welcome back dude to staff

Respect the model first! Send here the graphic card details

Success is not the end, failure is not fatal; Is the courage to continue what counts.-Winston Churchill.

Dean Hall, who created the po[CENSORED]r zombie game DayZ, is gearing up to announce a new game. Eurogamer reports that Hall will bring this unannounced game to the London-based show EGX Rezzed, where attendees can go hands on with it. Additionally, Hall will host a panel during the show where he will discuss the project and his New Zealand-based studio RocketWerkz. RocketWerkz is not an average studio when it comes to structure and benefits. Hall recently told RadioNZ that he gives his 40 employees unlimited leave and that his salary is only 10 percent more than the highest-paid staffer. Additionally, RocketWerkz has a profit-sharing scheme and reportedly no middle management. Hall will probably discuss this business philosophy during his EGZ Rezzed talk. EGX Rezzed runs March 30 through Sunday, April 1. BioShock designer Ken Levine will provide the opening keynote speech to kick off the show. According to RadioNZ, RocketWerkz has five games in development, including a VR title called Out of Ammo for HTC Vive. Another is the mysterious-looking Ion, which was revealed at E3 2015. Last year, Hall teased some kind of intriguing-sounding multiplayer game, which could be the project that will be shown at EGX Rezzed. As for DayZ, the game remains an Early Access title on Steam, where it's sold over 3 million copies. A PlayStation 4 edition is on the way, but don't expect it anytime soon.

Accepted !

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.

Now you see me.. now you don't

It's not from ur PC the problem, the nightvision is causing lag to everyone, this is from Counter-Strike, I don't know why but maybe needs some special settings to keep your FPS same, I'll search for it.

welcome to csbd enjoy your stay