20170127 tokyoserversideswiftmeetupÙYÁÏ
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This summary provides an overview of key concepts discussed in the document: - Swift uses kevent to monitor file descriptors for read/write events in an efficient O(1) manner similar to kqueue in C. - AsyncQueue in Swift uses a mutex to synchronize access to an internal array queue in order to prevent data races during concurrent put and get operations. - Swift arrays grow their capacity by 2x when the count exceeds the current capacity, allowing append and insert operations to remain amortized O(1) time due to infrequent capacity resizing.
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![Swift O(1)
data race)
mutex
class AsyncQueue<T> : AsyncQueueType<T> {
var queue = [T]()
override func put(obj: T) throws {
mutex.lock()
defer {
mutex.unlock()
}
queue += [obj]
}
override func get() throws -> T? {
mutex.lock()
defer {
mutex.unlock()
}
queue.remove(at: 0)
}](https://image.slidesharecdn.com/20170127tokyoserversideswiftmeetup-170127215227/85/20170127-tokyoserversideswiftmeetup-19-320.jpg)
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1. Ring has 46 keywords including class, def, func, if, else, for, and, or, not.
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The document describes various digital logic circuits including flip-flops, counters, and serial-parallel converters. It provides code examples in Verilog and VHDL for implementing D flip-flops, JK flip-flops, T flip-flops, SR flip-flops, up/down counters, and serial-in serial-out, serial-in parallel-out, parallel-in parallel-out converters. The examples show how to describe the logic functions and simulate the behavior of basic digital circuits using hardware description languages.C++11
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C++11 introduced several new features for functions and lambdas including:
1. Lambda expressions that allow the definition of anonymous inline functions.
2. The std::function wrapper that allows functions and lambdas to be used interchangeably.
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These features improved support for functional programming patterns in C++.Java Concurrency Idioms
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An overview of Java concurrency idioms for managing shared data, coordinating multiple threads, and managing work.Unit 3 - Styles of Modeling-1 for resource management techniques
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This document summarizes a user's journey developing a custom aggregation function for Apache Spark using a T-Digest sketch. The user initially implemented it as a User Defined Aggregate Function (UDAF) but ran into performance issues due to excessive serialization/deserialization. They then worked to resolve it by implementing the function as a custom Aggregator using Spark 3.0's new aggregation APIs, which avoided unnecessary serialization and provided a 70x performance improvement. The story highlights the importance of understanding how custom functions interact with Spark's execution model and optimization techniques like avoiding excessive serialization.User Defined Aggregation in Apache Spark: A Love Story
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Defining customized scalable aggregation logic is one of Apache Spark¡¯s most powerful features. User Defined Aggregate Functions (UDAF) are a flexible mechanism for extending both Spark data frames and Structured Streaming with new functionality ranging from specialized summary techniques to building blocks for exploratory data analysis. Egor Bogatov - .NET Core intrinsics and other micro-optimizations
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The document discusses various micro-optimizations and techniques for optimizing .NET code including using Span over Substring, avoiding allocations by using stackalloc or ArrayPool, eliminating bounds checks, using intrinsics and SIMD, and examples of optimizing specific algorithms like reversing an array. It covers potential pitfalls like type casts not being free and differences between runtimes. Intrinsics and SIMD can provide significant performance improvements over naive implementations by leveraging CPU instructions.PVS-Studio team experience: checking various open source projects, or mistake...
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This document discusses binary vulnerabilities and methods for finding and exploiting them. It covers different types of vulnerabilities like stack overflows, heap overflows, and integer overflows. It also discusses vulnerability metrics, identifiers, and tools that can be used to analyze binaries both statically and dynamically like fuzzers, symbolic execution, and taint analysis. Exploitation techniques like bypassing protections and ROP are also mentioned.3
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This document discusses pointcuts and static analysis in aspect-oriented programming. It provides an example of using aspects to ensure thread safety in Swing by wrapping method calls in invokeLater. It proposes representing pointcuts as relational queries over a program representation, and rewriting pointcuts as Datalog queries for static analysis. Representing programs and pointcuts relationally in this way enables precise static analysis of crosscutting concerns.VHDL summary.pdf
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The document provides examples of various VHDL constructs including entity declarations, architecture bodies, components, signals, constants, data types and objects. It shows how to describe synchronous and asynchronous logic using processes, and how to translate a state flow diagram to a two-process finite state machine description. Key constructs demonstrated include entity and architecture declarations, port maps, processes, if/else statements, case statements and sequential signal assignments.Infinum iOS Talks #1 - Swift under the hood: Method Dispatching by Vlaho Poluta
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In this presentation, I will discuss how technology has changed consumer behaviour and its impact on consumers and businesses. I will focus on internet access, digital devices, how customers search for information and what they buy online, video consumption, and lastly consumer trends.
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Just like life, our code must evolve to meet the demands of an ever-changing world. Adaptability is key in developing for the web, tablets, APIs, or serverless applications. Multi-runtime development is the future, and that future is dynamic. Enter BoxLang: Dynamic. Modular. Productive. (www.boxlang.io)
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Key tasks managed under FDM include:
Configuration
Monitoring
Diagnostics
Maintenance
Performance optimization
FDM solutions offer a comprehensive platform for real-time data collection, analysis, and decision-making, enabling:
Proactive maintenance
Predictive analytics
Remote monitoring
By streamlining operations and ensuring compliance, FDM enhances operational efficiency, reduces downtime, and improves asset reliability, ultimately leading to greater performance in industrial processes. FDM¡¯s emphasis on predictive maintenance is particularly important in ensuring the long-term sustainability and success of industrial operations.
For more information, explore the full report: https://shorturl.at/EJnzR
Major companies operating in Global?Field Device Management Market are:
General Electric Co
Siemens AG
ABB Ltd
Emerson Electric Co
Aveva Group Ltd
Schneider Electric SE
STMicroelectronics Inc
Techno Systems Inc
Semiconductor Components Industries LLC
International Business Machines Corporation (IBM)
#FieldDeviceManagement #IndustrialAutomation #PredictiveMaintenance #TechInnovation #IndustrialEfficiency #RemoteMonitoring #TechAdvancements #MarketGrowth #OperationalExcellence #SensorsAndActuatorsEarly Adopter's Guide to AI Moderation (Preview)
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Wondershare Filmora Crack 14.3.2.11147 Latestudkg888
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Brave Browser Crack 1.45.133 Activated 2025kherorpacca00126
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https://ncracked.com/7961-2/
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Brave is a free Chromium browser developed for Win Downloads, macOS and Linux systems that allows users to browse the internet in a safer, faster and more secure way than its competition. Designed with security in mind, Brave automatically blocks ads and trackers which also makes it faster,
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DevOps i? t?hl¨¹k?sizliyi sizi maraqland?r?r? ?st?r developer, ist?r t?hl¨¹k?sizlik m¨¹h?ndisi, ist?rs? d? DevOps h?v?skar? olun, bu t?dbir ??b?k?l??m?k, bilikl?rinizi b?l¨¹?m?k v? DevSecOps sah?sind? ?n son t?cr¨¹b?l?ri ?yr?nm?k ¨¹?¨¹n m¨¹k?mm?l f¨¹rs?tdir!
Bu workshopda DevOps infrastrukturlar?n?n t?hl¨¹k?sizliyini nec? art?rmaq bar?d? dan??acay?q. DevOps sisteml?ri qurulark?n avtomatla?d?r?lm??, y¨¹ks?k ?l?atan v? etibarl? olmas? il? yana??, h?m d? t?hl¨¹k?sizlik m?s?l?l?ri n?z?r? al?nmal?d?r. Bu s?b?bd?n, DevOps komandolar?n?n t?hl¨¹k?sizliy? y?n?lmi? praktikalara riay?t etm?si vacibdir.
Endpoint Backup: 3 Reasons MSPs Ignore It
Endpoint Backup: 3 Reasons MSPs Ignore ItMSP360
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Many MSPs overlook endpoint backup, missing out on additional profit and leaving a gap that puts client data at risk.
Join our webinar as we break down the top challenges of endpoint backup¡ªand how to overcome them.
Transform Your Future with Front-End Development Training
Transform Your Future with Front-End Development TrainingVtechlabs
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Kickstart your career in web development with our front-end web development course in Vadodara. Learn HTML, CSS, JavaScript, React, and more through hands-on projects and expert mentorship. Our front-end development course with placement includes real-world training, mock interviews, and job assistance to help you secure top roles like Front-End Developer, UI/UX Developer, and Web Designer.
Join VtechLabs today and build a successful career in the booming IT industry!UiPath Automation Developer Associate Training Series 2025 - Session 2
UiPath Automation Developer Associate Training Series 2025 - Session 2DianaGray10
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In session 2, we will introduce you to Data manipulation in UiPath Studio.
Topics covered:
Data Manipulation
What is Data Manipulation
Strings
Lists
Dictionaries
RegEx Builder
Date and Time
Required Self-Paced Learning for this session:
Data Manipulation with Strings in UiPath Studio (v2022.10) 2 modules - 1h 30m - https://academy.uipath.com/courses/data-manipulation-with-strings-in-studio
Data Manipulation with Lists and Dictionaries in UiPath Studio (v2022.10) 2 modules - 1h - https:/academy.uipath.com/courses/data-manipulation-with-lists-and-dictionaries-in-studio
Data Manipulation with Data Tables in UiPath Studio (v2022.10) 2 modules - 1h 30m - https:/academy.uipath.com/courses/data-manipulation-with-data-tables-in-studio
?? For any questions you may have, please use the dedicated Forum thread. You can tag the hosts and mentors directly and they will reply as soon as possible. The Future of Repair: Transparent and Incremental by Botond De?nes
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Regularly run repairs are essential to keep clusters healthy, yet having a good repair schedule is more challenging than it should be. Repairs often take a long time, preventing running them often. This has an impact on data consistency and also limits the usefulness of the new repair based tombstone garbage collection. We want to address these challenges by making repairs incremental and allowing for automatic repair scheduling, without relying on external tools.20170127 tokyoserversideswiftmeetupÙYÁÏ
- 17. Swift C var kev = kevent(); kev.ident = UInt(socket); kev.filter = Int16(EVFILT_READ); kev.flags = UInt16(flags); kev.data = 0; ret = kevent(kq, &kev, 1, nil, 0, nil) C
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- 19. Swift O(1) data race) mutex class AsyncQueue<T> : AsyncQueueType<T> { var queue = [T]() override func put(obj: T) throws { mutex.lock() defer { mutex.unlock() } queue += [obj] } override func get() throws -> T? { mutex.lock() defer { mutex.unlock() } queue.remove(at: 0) }
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- 26. protocol GenericQueue{ associatedtype A } class QueueA<B> : GenericQueue{ typealias A = B } class QueueB<B> : GenericQueue{ // Queue typealias A = B } typealias ClosureType = () -> Void class QueueUser { func useQueue(queue: GenericQueue<ClosureType>) { // ? } }
- 31. Swift Array O(1) Swift Array RangeReplaceableCollection extension public struct Array<Element> : RandomAccessCollection, MutableCollection { extension Array : RangeReplaceableCollection { Swift Array github: swift/stdlib/public/core/Arrays.swift.gyb gyb = Generate Your Boilerplate Python Python
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