Visual C＋＋6.0是由 Microsoft 公司推出的一款面向对象的计算机程序开发工具，是编程入门的良好编译工具，在 Windows 环境下很常用，是使用最广的开发工具。AOS 是高级在轨系统（Advanced Orbiting Systems）的缩略词，主要用来达成...Visual C＋＋6.0是由 Microsoft 公司推出的一款面向对象的计算机程序开发工具，是编程入门的良好编译工具，在 Windows 环境下很常用，是使用最广的开发工具。AOS 是高级在轨系统（Advanced Orbiting Systems）的缩略词，主要用来达成航天器与地面站之间的双向传送。本文中应用 Visual C＋＋6.0程序设计软件，根据 AOS 空间包提取的方法，给出实验仿真结果。展开更多
介绍在Visual C＋＋6．0环境下的两种Oracle开发接口：ADO（ActiveX Data Objects，AetiveX数据对象）与Pro＊C／C＋＋，其中ADO是一组由微软提供的COM组件，它通过使用OLEDB这一新技术实现了以相同方式可以快捷、便利地访问多种类型数...介绍在Visual C＋＋6．0环境下的两种Oracle开发接口：ADO（ActiveX Data Objects，AetiveX数据对象）与Pro＊C／C＋＋，其中ADO是一组由微软提供的COM组件，它通过使用OLEDB这一新技术实现了以相同方式可以快捷、便利地访问多种类型数据库数据，扩大了应用程序中可使用的数据源范围，Pro＊C／C＋＋是利用在C／C＋＋内嵌入的SQL语句来访问数据库数据，使用它可以开发出满足各种复杂程度的应用程序，并可有效提高应用程序的执行速度。通过设计实验，分别采用这两种方式完成数据库初始化、数据录入等工作，通过对比两种方式的实现分析两种方式的特点，并最终总结出两种方式各自的优缺点与适用范围。展开更多
The year 2024 marks the 60^(th)anniversary of Title IX and 25 years since the New York Times revealed bias against female faculty members at the Massachusetts Institute of Technology.We take an opportunity here to exa...The year 2024 marks the 60^(th)anniversary of Title IX and 25 years since the New York Times revealed bias against female faculty members at the Massachusetts Institute of Technology.We take an opportunity here to examine the state of gender bias in a relatively new yet already prominent field,neural regeneration in the visual system,for which there is a well-defined context useful for this purpose.The National Eye Institute(NEI)provided the first round of research funding for its Audacious Goals Initiative(AGI)on visual neural regeneration in 2013 and the last round in 2021.Therefore,we focus on this timespan.Data sources included PubMed,the National Science Foundation(NSF),the NEI,the Blue Ridge Institute for Medical Research and data from the major professional organization for eye and vision research,the Association for Research in Vision and Ophthalmology(ARVO).展开更多
Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception...Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.展开更多
文摘Visual C＋＋6.0是由 Microsoft 公司推出的一款面向对象的计算机程序开发工具，是编程入门的良好编译工具，在 Windows 环境下很常用，是使用最广的开发工具。AOS 是高级在轨系统（Advanced Orbiting Systems）的缩略词，主要用来达成航天器与地面站之间的双向传送。本文中应用 Visual C＋＋6.0程序设计软件，根据 AOS 空间包提取的方法，给出实验仿真结果。
文摘介绍在Visual C＋＋6．0环境下的两种Oracle开发接口：ADO（ActiveX Data Objects，AetiveX数据对象）与Pro＊C／C＋＋，其中ADO是一组由微软提供的COM组件，它通过使用OLEDB这一新技术实现了以相同方式可以快捷、便利地访问多种类型数据库数据，扩大了应用程序中可使用的数据源范围，Pro＊C／C＋＋是利用在C／C＋＋内嵌入的SQL语句来访问数据库数据，使用它可以开发出满足各种复杂程度的应用程序，并可有效提高应用程序的执行速度。通过设计实验，分别采用这两种方式完成数据库初始化、数据录入等工作，通过对比两种方式的实现分析两种方式的特点，并最终总结出两种方式各自的优缺点与适用范围。
文摘The year 2024 marks the 60^(th)anniversary of Title IX and 25 years since the New York Times revealed bias against female faculty members at the Massachusetts Institute of Technology.We take an opportunity here to examine the state of gender bias in a relatively new yet already prominent field,neural regeneration in the visual system,for which there is a well-defined context useful for this purpose.The National Eye Institute(NEI)provided the first round of research funding for its Audacious Goals Initiative(AGI)on visual neural regeneration in 2013 and the last round in 2021.Therefore,we focus on this timespan.Data sources included PubMed,the National Science Foundation(NSF),the NEI,the Blue Ridge Institute for Medical Research and data from the major professional organization for eye and vision research,the Association for Research in Vision and Ophthalmology(ARVO).
基金supported by National Key Research and Development Program of China(2021YFB1714300)the National Natural Science Foundation of China(62233005)+2 种基金in part by the CNPC Innovation Fund(2021D002-0902)Fundamental Research Funds for the Central Universities and Shanghai AI Labsponsored by Shanghai Gaofeng and Gaoyuan Project for University Academic Program Development。
文摘Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.
