Prefer Domain- Specific Types to Primitive Types

本文探讨了使用特定领域的类型而非原始类型在编程中带来的价值,通过具体案例分析了这种做法如何避免软件错误,如火星气候轨道器任务失败的原因,并介绍了Ada语言等支持强类型系统的优点。

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Prefer Domain- Specific Types to Primitive Types

Einar Landre

ON SEPTEMBER 23, 1999, the $327.6 million Mars Climate Orbiter was lost while entering orbit around Mars due to a software error back on Earth. The error was later called the metric mix-up. The ground-station software was working in pounds, while the spacecraft expected newtons, leading the ground station to underestimate the power of the spacecraft’s thrusters by a factor of 4.45.
This is one of many examples of software failures that could have been pre- vented if stronger and more domain-specific typing had been applied. It is also an example of the rationale behind many features in the Ada language, one of whose primary design goals was to implement embedded safety-critical software. Ada has strong typing with static checking for both primitive types and user-defined types:
type Velocity_In_Knots is new Float range 0.0 .. 500.00;
type Distance_In_Nautical_Miles is new Float range 0.0 .. 3000.00;
Velocity: Velocity_In_Knots;
Distance: Distance_In_Nautical_Miles;
Some_Number: Float;
Some_Number:= Distance + Velocity; – Will be caught by the compiler as a type error.
130 97 Things Every Programmer Should Know

Developers in less demanding domains might also benefit from applying more domain-specific typing, where they might otherwise continue to use the primitive data types offered by the language and its libraries, such as strings and floats. In Java, C++, Python, and other modern languages, the abstract data type is known as class. Using classes such as Velocity_In_Knots and Distance_In_Nautical_Miles adds a lot of value with respect to code quality:
• The code becomes more readable, as it expresses concepts of a domain, not just Float or String.
• The code becomes more testable, as the code encapsulates behavior that is easily testable.
• The code facilitates reuse across applications and systems.
The approach is equally valid for users of both statically and dynamically typed languages. The only difference is that developers using statically typed languages get some help from the compiler, while those embracing dynami- cally typed languages are more likely to rely on their unit tests. The style of checking may be different, but the motivation and style of expression is not.
The moral is to start exploring domain-specific types for the purpose of developing quality software.

内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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