July 10th Friday (七月 十日 金曜日)

本文探讨了程序续延的概念,包括提示续延、限定续延及续延屏障的作用。解释了如何通过特定操作捕获和扩展当前续延,并介绍了续延屏障如何限制续延替换。还讨论了逃逸续延的特性及其使用场景。

Prompts, Delimited Continuations, and Barriers

  A prompt is a special kind of continuation frame that is annotated with a specific prompt tag (essentially a continuation mark). Various operations
allow the capture of frames in the continuation from the redex position out to the nearest enclosing prompt with a particular prompt tag; such a continuation
is sometimes called a delimited continuation. Other operations allow the current continuation to be extended with a captured continuation (specifically,
a composable continuation). Yet other operations abort the computation to the nearest enclosing prompt with a particular tag, or replace the continuation
to the nearest enclosing prompt with another one. When a delimited continuation is captured, the marks associated with the relevant frames are also captured.

  A continuation barrier is another kind of continuation frame that prohibits certain replacements of the current continuation with another. Specifically,
while an abort is allowed to remove a portion of the continuation containing a prompt, the continuation can be replaced by another only when the replacement
also includes the continuation barrier. Certain operations install barriers automatically; in particular, when an exception handler is called, a continuation
barrier prohibits the continuation of the handler from capturing the continuation past the exception point.

  A escape continuation is essentially a derived concept. It combines a prompt for escape purposes with a continuation for mark-gathering purposes. as the name
implies, escape continuations are used only to abort to the point of capture, which means that escape-continuation aborts can cross continuation barriers.

数据集介绍:垃圾分类检测数据集 一、基础信息 数据集名称:垃圾分类检测数据集 图片数量: 训练集:2,817张图片 验证集:621张图片 测试集:317张图片 总计:3,755张图片 分类类别: - 属:常见的属垃圾材料。 - 纸板:纸板类垃圾,如包装盒等。 - 塑料:塑料类垃圾,如瓶子、容器等。 标注格式: YOLO格式,包含边界框和类别标签,适用于目标检测任务。 数据格式:图片来源于实际场景,格式为常见图像格式(如JPEG/PNG)。 二、适用场景 智能垃圾回收系统开发: 数据集支持目标检测任务,帮助构建能够自动识别和分类垃圾材料的AI模型,用于自动化废物分类和回收系统。 环境监测与废物管理: 集成至监控系统或机器人中,实时检测垃圾并分类,提升废物处理效率和环保水平。 学术研究与教育: 支持计算机视觉与环保领域的交叉研究,用于教学、实验和论文发表。 三、数据集优势 类别覆盖全面: 包含三种常见垃圾材料类别,覆盖日常生活中主要的可回收物类型,具有实际应用价值。 标注精准可靠: 采用YOLO标注格式,边界框定位精确,类别标签准确,便于模型直接训练和使用。 数据量适中合理: 训练集、验证集和测试集分布均衡,提供足够样本用于模型学习和评估。 任务适配性强: 标注兼容主流深度学习框架(如YOLO等),可直接用于目标检测任务,支持垃圾检测相关应用。
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