题型reading toefl 183
Proj CDeepFuzz Paper Reading: NeuRI: Diversifying DNN Generation via Inductive Rule Inference
## Abstract 背景：The correctness of DL systems is crucial for trust in DL applications 本文: NeuRI BaseTool: FreeFuzz Github: https://github.com/ise-uiuc/ ......
Proj CDeepFuzz Paper Reading: Natural attack for pre-trained models of code
## Abstract 背景：目前大多数的adversarial attack method on pre-trained models of code忽略了perturbations should be natural to human judges(naturalness requirement ......
Proj CDeepFuzz Paper Reading: COMET: Coverage-guided Model Generation For Deep Learning Library Testing
## Abstract 背景：已有的方法(Muffin, Lemon, Cradle) can cover at most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer sequences. 本文：COMET Gi ......
Proj CDeepFuzz Paper Reading: IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks
## Abstract 本文：IvySyn Task: discover memory error vulnerabilities in DL frameworks BugType: memory safety errors, fatal runtime errors Method: 1. 利用na ......
MAPF Paper Reading Note
随便写写记录一下 ## 1. 2005-Cooperative Pathfinding ### 1.1. LRA* local repair A* - 依次做A* - 即将开始碰撞时，replan - a general replan solution: 每次重规划时，新增noise，按照比例加入$ ......
Proj CDeepFuzz Paper Reading: Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
## Abstract 本文： Task: 1. prove invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations 2. prove that ......
Proj CDeepFuzz Paper Reading: Framework for Evaluating Faithfulness of Local Explanations
## Abstract 本文: Task: 1. study the faithfulness of an explanation system to the underlying prediction model on consistency and sufficiency 2. introduc ......
【Python 自动化】自媒体剪辑第一版·思路简述与技术方案
大家都知道我主业是个运维开发（或者算法工程师），每天时间不多，但我又想做自媒体。然后呢，我就想了个方案，每天起来之后写个短视频的脚本，包含一系列图片和文字，然后上班的时候给它提交到流水线上跑，下班之前就能拿到视频，然后往各大平台上一传，是不是挺美滋滋的。 我和我之前的合伙人一说，他就觉得做短视频没啥 ......
Proj CDeepFuzz Paper Reading: Differential Testing of Cross Deep Learning Framework APIs: Revealing Inconsistencies and Vulnerabilities
## Abstract 背景：目前对cross-framework conversion中的inconsistencies和security bugs的研究少有 本文：TensorScope Task: test cross-frame APIs in Machine Learning Librar ......
P4345 [SHOI2015] 超能粒子炮·改 Lucas定理
求解$\sum_{i=0}^kC(n,i)\mod 2333$ 值得一提的是$2,23,233,2333$均为质数。 这次是对行求和。并没有很难好的公式。 但是由于模数非常特殊可以使用卢卡斯定理。 $C(n,i)\%\ p=C(n\%p,i\%p)\cdot C(n/p,i/p)$ 不妨设$f(n, ......
Proj CDeepFuzz Paper Reading: DeepTest: automated testing of deep-neural-network-driven autonomous cars
## Abstract 本文: DeepTest Task: a systematic testing tool for DNN-driven vehicles Method: 1. generated test cases with real-world changes like rain, fo ......
Proj CDeepFuzz Paper Reading: DeepGauge: multi-granularity testing criteria for deep learning systems
## Abstract 本文: DeepGauge Task: provide multi-granularity testing criteria for DL systems Method: multi-granularity testing criteria for DL systems: 1 ......
Proj CDeepFuzz Paper Reading: Combinatorial Testing for Deep Learning Systems
## Abstract 本文：DeepCT Task: Testing DL Models with Combinatorial Testing Method: 1. 将输出值的空间离散化为区间，以便覆盖每个区间,对不同层内的神经元交互进⾏采样，并减少必须执⾏的测试输⼊的数量。 2. a set o ......
我的 Kafka 旅程 - 基于账号密码的 SASL+PLAIN 认证授权 · 配置 · 创建账号 · 用户授权 · .NET接入
Kafka 3.x，Kraft模式，账号密码方式，SASL+PLAINTEXT+SCRAM 认证授权，用户的分类，命令行创建用户，查看账号信息，认证授权的配置，将账号授权到Topic，写入与读取的权限，创建用户凭证，以用户凭证连接到Kafka，.NET用户凭证接入。 ......
Proj CDeepFuzz Paper Reading: Automatic differentiation in PyTorch
## Abstract 本文：描述automatic differentiation module of PyTorch 包括：Lua Torch, Chainer, HIPS Autograd Task: Provides a high-performance environment on dif ......
Further reading: Theory of computation
找了些：https://en.wikipedia.org/wiki/Theory_of_computation 提到的书籍： Textbooks aimed at computer scientists (There are many textbooks in this area; this lis ......
Got fatal error 1236 from master when reading data from binary log: 'Client requested master to start replication from position > file size', Error_code: 1236
MySQL主从复制报错，MySQL主主复制结构，MySQL版本5.7.32 2023-08-31T09:08:29.316553+08:00 1 [ERROR] Error reading packet from server for channel '': Client requested mas ......
【专题】2022年·母婴行业用户洞察报告PDF合集分享（附原数据表）
原文链接：https://tecdat.cn/?p=33528 报告合集显示，由于新生儿出生率下降，母婴行业进入了存量时代。在这一背景下，抖音电商成为越来越多消费者的选择，尤其是24-40岁的三四线城市女性。这一消费群体更倾向于在线上购买，给母婴行业的线上销售带来了巨大的机遇。阅读原文，获取专题报告 ......
Proj CDeepFuzz Paper Reading: ACETest: Automated Constraint Extraction for Testing Deep Learning Operators
## Abstract Github: https://github.com/shijy16/ACETest 背景： 1. DL operators 用来计算多维tensors，很重要 本文：ACETest Task: automatically extract input validation c ......
Proj CDeepFuzz Paper Reading: AutoML: A survey of the state-of-the-art
## Abstract Github: https://github.com/marsggbo/automl_a_survey_of_state_of_the_art 本文： 1. intro AutoML methods: data preparation, feature engineering ......
【CF1374E1】Reading Books (easy version)（贪心）
**题目大意：** 给出$n(1\le n\le2\times 10^{5})$个三元组$(t,a,b)(0\le a,b\le 1)$，选出其中任意个，使得被选中的元素$a$、$b$的总和均为$k$，求$t$总和的最小值 *** 因为被选中的元素$a$、$b$的总和均为$k$，所以被选中的三元组中 ......
【校招VIP】前端算法考点之快慢指针题型
考点介绍： 链表是校招面试里手撕代码出现频度比较高的题型，三线和中小厂会考察简单的链表反转，大厂会进一步考察复杂度和双指针问题，比如中间元素、是否存在环等。 一、考点题目 1.一个长度为n的单向链表，用O(1) 空间复杂度来实现倒转输出，使用最低时间复杂度 解答：单向链表，直接设结点 Node he ......
Proj CDeepFuzz Paper Reading: Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
## Abstract 背景： 1. the de facto standard to assess the quality of DNNs in the industry is to check their performance (accuracy) on a collected set of ......
Proj CDeepFuzz Paper Reading: An Extensive Study on Pre-trained Models for Program Understanding and Generation
## Abstract ## 1. Intro ## 2. Background ### 2.1 Program Understanding and Generation Tasks ### 2.2 NL-PL Pre-Trained Models ; ``` 函数原型为：ssize_t read(int fd, void *buf, size_t count); ......
车灯LED电源控制降压恒流芯片AP2403半亮·全亮·爆闪切换
产品描述AP2403 是一款深圳市世微半导体有限公司生产并研发的 PWM 工作模式,高效率、外围简单、内置功率管，适用于 5-100V 输入的高精度降压 LED 恒流驱动芯片。输出最大功率可达 22W，最大电流 2.2A。AP2403 可实现三段功能切换，通过 MODE1/2/3 切换三种功能模式： ......
Cannot read property 'clearValidate' of undefined
Cannot read property 'clearValidate' of undefined  这个错误是因 ......
PacBio long read error correction
PacBio长读错误校正算法有多种，每种算法都具有各自的优点和缺点。以下是常用的几种算法及其特点： Pacific Biosciences (PacBio) SMRT Analysis软件套件： - 优点：PacBio提供了一套完整的错误校正工具，包括PacBioToCA、Quiver、Arrow等 ......