Ultra Model Sets 11 - 14 [HOT]

Ultra Model Sets 11 - 14 [HOT]

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Before you take a photo, the camera automatically sets the focus and exposure, and face detection balances the exposure across many faces. You can use Exposure Compensation Control to precisely set and lock the exposure for upcoming shots.

Details: The front and back are flat and made of glass, and there's a stainless steel band around the edges. The volume up and down buttons are marked with a "+" and "-" sign. There's a SIM tray on the right side that holds a "third form factor" (3FF) micro-SIM card. The CDMA model has no SIM tray.

Available in the popular 6.1-inch size and a stunning new 6.7-inch size,2 iPhone 14 and iPhone 14 Plus feature a durable and sleek aerospace-grade aluminum design in five beautiful finishes. The larger display of iPhone 14 Plus is great for streaming movies and playing games, and iPhone 14 Plus boasts the best battery life ever in an iPhone.3 Both models have an updated internal design for better thermal performance, gorgeous Super Retina XDR displays with OLED technology that supports 1200 nits of peak HDR brightness, a 2,000,000:1 contrast ratio, and Dolby Vision.

iPhone offers users super-fast downloads and uploads, better streaming, and real-time connectivity with 5G to help them stay in touch, share, and enjoy content.8 Support for 5G on iPhone now extends to over 250 carrier partners in over 70 markets around the world, with expanded support for standalone networks. eSIM allows users to easily connect or quickly transfer their existing plans digitally, is a more secure alternative to a physical SIM card, and allows for multiple cellular plans on a single device. iPhone 14 and iPhone 14 Plus remove the SIM tray for US models, enabling users to more quickly and easily set up their devices.

iPhone 14 and iPhone 14 Plus feature iOS 16, offering a reimagined Lock Screen along with new communication, sharing, and intelligence features that together change the way users experience iPhone. The Lock Screen is more personal, beautiful, and helpful than ever with a multilayered effect that artfully sets subjects of photos in front of the time, and newly designed widgets that offer information at a glance. For Lock Screen inspiration, the wallpaper gallery offers a range of options, including Apple collections, a Weather wallpaper to see live weather conditions as they change throughout the day, an Astronomy wallpaper for views of the Earth, moon, and solar system, and many more. With Messages, users can now edit or recall recently sent messages, and mark conversations as unread to revisit them later.9 iCloud Shared Photo Library makes it even easier to share a collection of photos with family.10 Live Text gets more powerful with the ability to recognize text in video and quickly convert currency, translate text, and more, and Visual Look Up adds a new feature that allows users to tap and hold on the subject of an image to lift it from the background and place it in apps like Messages.11

iPhone 14 and iPhone 14 Plus are designed to minimize their impact on the environment, including antenna lines that use upcycled plastic water bottles that have been chemically transformed into a stronger, higher-performance material. iPhone 14 models also use 100 percent recycled rare earth elements in all magnets, including those used in MagSafe, and 100 percent recycled tungsten in the Taptic Engine. Both models also include 100 percent recycled tin in the solder of multiple printed circuit boards, and 100 percent recycled gold in the plating of multiple printed circuit boards and in the wire of all cameras. Fiber-based packaging does not use outer plastic wrap, bringing Apple closer to its goal of completely removing plastic from all packaging by 2025.

Starting today, customers can pre-order all models of the advanced iPhone 14 lineup on apple.com and on the Apple Store app. iPhone 14, iPhone 14 Pro, and iPhone 14 Pro Max will be available in stores and for delivery starting Friday, September 16. iPhone 14 Plus will be available in stores and for delivery beginning Friday, October 7.

It's September, which means it's time for Apple to update the iPhone. This year brings the iPhone 14 and 14 Pro, handsets that look quite a bit like last year's 13 series on the outside, though there are some big changes inside, especially with the 14 Pro and 14 Pro Max. If you're interested in learning about the phones in general, we've got you covered; here we'll take a close look at the cameras.

Like the iPhone 13, the "regular" iPhone 14 models sport dual cameras, one with an ultra-wide view and another with the classic wide angle (26mm) that's become the de facto standard view for smartphones. The front-facing selfie camera has an f/2.2 lens for FaceTime chats and selfies.

For video, the basic iPhone 14 models support 4K60 recording in SDR or Dolby Vision HDR, the same as last time. Cinematic mode gets an upgrade, though. The feature made its debut last year and brings the bokeh effect to video, but was limited to 1080p24 with the 13 series. With the iPhone 14 it gets an upgrade to 4K, and you can pick between the classic 24fps look or more video-like 30fps, both with Dolby Vision color.

This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question.

Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then.

Protein contact prediction and contact-assisted folding has made good progress due to direct evolutionary coupling analysis (DCA). However, DCA is effective on only some proteins with a very large number of sequence homologs. To further improve contact prediction, we borrow ideas from deep learning, which has recently revolutionized object recognition, speech recognition and the GO game. Our deep learning method can model complex sequence-structure relationship and high-order correlation (i.e., contact occurrence patterns) and thus, improve contact prediction accuracy greatly. Our test results show that our method greatly outperforms the state-of-the-art methods regardless how many sequence homologs are available for a protein in question. Ab initio folding guided by our predicted contacts may fold many more test proteins than the other contact predictors. Our contact-assisted 3D models also have much better quality than homology models built from the training proteins, especially for membrane proteins. One interesting finding is that even trained mostly with soluble proteins, our method performs very well on membrane proteins. Recent blind CAMEO test confirms that our method can fold large proteins with a new fold and only a small number of sequence homologs.

De novo protein structure prediction from sequence alone is one of most challenging problems in computational biology. Recent progress has indicated that some correctly-predicted long-range contacts may allow accurate topology-level structure modeling [1] and that direct evolutionary coupling analysis (DCA) of multiple sequence alignment (MSA) may reveal some long-range native contacts for proteins and protein-protein interactions with a large number of sequence homologs [2, 3]. Therefore, contact prediction and contact-assisted protein folding has recently gained much attention in the community. However, for many proteins especially those without many sequence homologs, the predicted contacts by the state-of-the-art predictors such as CCMpred [4], PSICOV [5], Evfold [6], plmDCA[7], Gremlin[8], MetaPSICOV [9] and CoinDCA [10] are still of low quality and insufficient for accurate contact-assisted protein folding [11, 12]. This motivates us to develop a better contact prediction method, especially for proteins without a large number of sequence homologs. In this paper we define that two residues form a contact if they are spatially proximal in the native structure, i.e., the Euclidean distance of their Cβ atoms less than 8Å [13]. 2b1af7f3a8