iOS 8 提供的应用间和应用跟系统的功能交互特性。
• Today (iOS and OS X): widgets for the Today view of Notification Center
• Share (iOS and OS X): post content to web services or share content with others
• Actions (iOS and OS X): app extensions to view or manipulate inside another app
• Photo Editing (iOS): edit a photo or video in Apple's Photos app with extensions from a third-party apps
• Finder Sync (OS X): remote file storage in the Finder with support for Finder content annotation
• Storage Provider (iOS): an interface between files inside an app and other apps on a user's device
• Custom Keyboard (iOS): system-wide alternative keyboards

Source: iOS 8 Extensions: Apple’s Plan for a Powerful App Ecosystem

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“机器会思考吗”和“机器能做人类做的事情吗”是推动人工智能发展的任务。尽管最近的人工智能在许多数据密集型应用中取得了成功，但它仍然缺乏从有限的数据示例学习和对新任务的快速泛化的能力。为了解决这个问题，我们必须求助于机器学习，它支持人工智能的科学研究。特别地，在这种情况下，有一个机器学习问题称为小样本学习(Few-Shot Learning，FSL)。该方法利用先验知识，可以快速地推广到有限监督经验的新任务中，通过推广和类比，模拟人类从少数例子中获取知识的能力。它被视为真正人工智能，是一种减少繁重的数据收集和计算成本高昂的培训的方法，也是罕见案例学习有效方式。随着FSL研究的广泛开展，我们对其进行了全面的综述。我们首先给出了FSL的正式定义。然后指出了FSL的核心问题，将问题从“如何解决FSL”转变为“如何处理核心问题”。因此，从FSL诞生到最近发表的作品都被归为一个统一的类别，并对不同类别的优缺点进行了深入的讨论。最后，我们从问题设置、技术、应用和理论等方面展望了FSL未来可能的发展方向，希望为初学者和有经验的研究者提供一些见解。

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The quest of can machines think' and can machines do what human do' are quests that drive the development of artificial intelligence. Although recent artificial intelligence succeeds in many data intensive applications, it still lacks the ability of learning from limited exemplars and fast generalizing to new tasks. To tackle this problem, one has to turn to machine learning, which supports the scientific study of artificial intelligence. Particularly, a machine learning problem called Few-Shot Learning (FSL) targets at this case. It can rapidly generalize to new tasks of limited supervised experience by turning to prior knowledge, which mimics human's ability to acquire knowledge from few examples through generalization and analogy. It has been seen as a test-bed for real artificial intelligence, a way to reduce laborious data gathering and computationally costly training, and antidote for rare cases learning. With extensive works on FSL emerging, we give a comprehensive survey for it. We first give the formal definition for FSL. Then we point out the core issues of FSL, which turns the problem from "how to solve FSL" to "how to deal with the core issues". Accordingly, existing works from the birth of FSL to the most recent published ones are categorized in a unified taxonomy, with thorough discussion of the pros and cons for different categories. Finally, we envision possible future directions for FSL in terms of problem setup, techniques, applications and theory, hoping to provide insights to both beginners and experienced researchers.

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Quantitative separation logic (QSL) is an extension of separation logic (SL) for the verification of probabilistic pointer programs. In QSL, formulae evaluate to real numbers instead of truth values, e.g., the probability of memory-safe termination in a given symbolic heap. As with \SL, one of the key problems when reasoning with QSL is \emph{entailment}: does a formula f entail another formula g? We give a generic reduction from entailment checking in QSL to entailment checking in SL. This allows to leverage the large body of SL research for the automated verification of probabilistic pointer programs. We analyze the complexity of our approach and demonstrate its applicability. In particular, we obtain the first decidability results for the verification of such programs by applying our reduction to a quantitative extension of the well-known symbolic-heap fragment of separation logic.

### 最新论文

Quantitative separation logic (QSL) is an extension of separation logic (SL) for the verification of probabilistic pointer programs. In QSL, formulae evaluate to real numbers instead of truth values, e.g., the probability of memory-safe termination in a given symbolic heap. As with \SL, one of the key problems when reasoning with QSL is \emph{entailment}: does a formula f entail another formula g? We give a generic reduction from entailment checking in QSL to entailment checking in SL. This allows to leverage the large body of SL research for the automated verification of probabilistic pointer programs. We analyze the complexity of our approach and demonstrate its applicability. In particular, we obtain the first decidability results for the verification of such programs by applying our reduction to a quantitative extension of the well-known symbolic-heap fragment of separation logic.

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