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ChatGPT高端用法简介ChatGPT是OpenAI推出的一种基于GPT（生成式预训练模型）的聊天机器人。
它通过大量的文本数据进行训练，可以生成与用户进行自然语言对话的回复。
ChatGPT可以应用于各种场景，包括客服、教育、娱乐等。
本文将介绍ChatGPT的高端用法，包括如何提升回复质量、控制回复风格、添加特定功能等。
提升回复质量1. 数据清洗在使用ChatGPT之前，我们可以对训练数据进行清洗，去除噪声和不必要的信息。
这可以提高ChatGPT的回复质量。
可以使用一些文本处理工具来清洗数据，例如去除HTML标签、特殊字符等。
2. Fine-tuningFine-tuning是一种在预训练模型上进行微调的方法，可以使ChatGPT更适应特定的任务或领域。
我们可以使用自己的数据集进行Fine-tuning，或者使用OpenAI提供的Fine-tuning API。
通过Fine-tuning，ChatGPT可以更好地理解特定领域的问题，并给出更准确的回复。
3. 上下文重要性ChatGPT是基于上下文进行回复的，因此上下文的重要性对回复质量至关重要。
我们可以通过调整模型的temperature参数来控制生成回复的随机性。
较高的temperature值会导致更随机的回复，而较低的值会使回复更加确定和一致。
控制回复风格1. 设置回复长度为了控制回复的长度，我们可以通过设置模型的max_tokens参数来限制生成回复的最大长度。
这样可以避免生成过长的回复，保持回复的简洁性。
2. 添加回复模板为了确保回复的一致性和准确性，我们可以为ChatGPT提供一些回复模板。
这些模板可以是预定义的，也可以根据具体情况动态生成。
通过使用回复模板，我们可以更好地控制回复的风格和内容。
3. 过滤敏感主题为了避免生成敏感内容，我们可以对输入进行过滤，将与敏感主题相关的问题或关键词进行屏蔽或替换。
这有助于确保ChatGPT生成的回复符合规范和道德要求。
人工操控相关名词术语A算法（Algorithms）：一组用于人工智能、神经网络或其他机器的规则或指令，以帮助它自己学习；分类、聚类、推荐和回归是四种最常见的类型。
人工智能（Artificial intelligence）：机器模拟人类智力和行为做出决策、执行任务的能力。
人工神经网络（ANN）：这种学习模型，模拟人脑运作，从而解决传统计算机系统难以解决的任务。
自主计算（Autonomic computing）：系统自适应自我管理自身资源用于高级计算功能的能力，而无需用户输入。
C聊天机器人（Chatbots）：聊天机器人（简称chatbot）通过文本对话、语音命令来模拟与人类用户进行对话。
它们是有AI功能的计算机程序的常用界面。
分类（Classification）：分类算法让机器根据训练数据给数据点进行分类。
聚类分析（Cluster analysis）：一种用于探索性数据分析的无监督学习，查找数据中的隐藏模式或分组；群集的建立是通过欧氏距离（Euclidean）或概率距离等定义的相似性度量。
聚类（Clustering）：聚类算法让机器将数据点或项目分成具有相似特征的组。
认知计算（Cognitive computing）：一种模仿人类大脑思维方式的计算模型。
通过使用数据挖掘、自然语言处理和模式识别来进行自学习（self-learning）。
卷积神经网络（CNN）：一种识别和处理图像的神经网络。
D数据挖掘（Data mining）：通过查看数据集以发现和挖掘其中模式，从而进一步使用数据。
数据科学（Data science）：结合统计、信息科学、计算机科学的科学方法、科学系统和科学过程的交叉学科，通过结构化或非结构化数据提供对现象的洞察。
决策树（Decision tree）：一个基于分支的树模型，绘制决策及其可能后果的模型图，与流程图类似。
深度学习（Deep learning）：机器通过由层叠信息层组成的人工神经网络自主模仿人类思维模式的能力。
AI in Cybersecurity TrainingAI (Artificial Intelligence) in cybersecurity training is revolutionizing the way organizations prepare their workforce to defend against cyber threats. In today's digitally-driven world, where cyber attacks are becoming more sophisticated and frequent, staying ahead of malicious actors is paramount. Traditional cybersecurity training methods may not be sufficient to keep up with the evolving threat landscape. This is where AI comes into play, offering advanced solutions to enhance the effectiveness of cybersecurity training.One of the key benefits of using AI in cybersecurity training is its ability to personalize learning experiences for individuals. By analyzing the strengths and weaknesses of each trainee, AI can tailor training programs to address specific skill gaps and knowledge deficiencies. This targeted approach not only improves the overall effectiveness of the training but also ensures that each individual receives the instruction they need to succeed in defending against cyber threats.Furthermore, AI-powered cybersecurity training platforms can simulate real-world cyber attack scenarios, providing trainees with hands-on experience in identifying and responding to security incidents. These simulations enable trainees to apply their knowledge in a practical setting, helping them develop the critical thinking and decision-making skills necessary to effectively mitigate cyber risks.In addition, AI can be used to automate the assessment of trainee performance, providing instant feedback on their progress and identifying areas for improvement. By leveraging AI algorithms to analyze training data, organizations can continuously refine and optimize their cybersecurity training programs to ensure maximum efficacy.Another advantage of incorporating AI into cybersecurity training is its ability to detect and mitigate cyber threats in real-time. AI algorithms can analyze vast amounts of data to identify anomalous behavior and potential security vulnerabilities, allowing organizations to proactively defend against cyber attacks. By integrating AI-poweredthreat detection and response capabilities into cybersecurity training, organizations can better prepare their workforce to anticipate and respond to emerging cybersecurity threats.Moreover, AI can assist in keeping cybersecurity training content up to date with the latest industry trends and technologies. With the rapid advancements in cyber threats and defense strategies, it is crucial for training programs to evolve to address new challenges. AI can help organizations stay current by continuously monitoring industry developments and updating training materials accordingly.Overall, the integration of AI in cybersecurity training offers numerous benefits, including personalized learning experiences, realistic simulations, automated performance assessment, real-time threat detection, and up-to-date content. By leveraging AI-powered solutions, organizations can effectively empower their workforce with the knowledge and skills needed to protect against cyber threats in today's complex digital landscape. As technology continues to advance, AI will undoubtedly play a crucial role in shaping the future of cybersecurity training, ensuring that organizations remain vigilant and resilient against evolving cyber threats.。
23年高考英语外刊阅读训练——阅读理解：人工智能如何转变教育——改编自How Artificial Intelligence Is Changing Education AI, or artificial intelligence, has bee increasingly prevalent in everyday life. Programmers code and "train〞puters to perform tasks that normally require human intelligence, such as visual recognition, artificial speech, and problem solving. AI algorithms work behind the scenes to customize ads and content online, and natural language processors interact and respond with users in a conversational manner. AI chatbots are now widely available and can be used for a variety of purposes such as customer service and generating sales leads.One example of an AI that may help protect individuals is “Take It Down,〞a bot developed by Meta to help teenagers remove unauthorized photos from the internet. While there are concerns about AI bots collecting data and invading privacy, AI automation can save time and improve efficiency in many areas, including this task that would otherwise take hours to perform manually.The current generation of AI chatbots still face limitations in fully simulating human emotions and attending to nuanced language cues in human conversations. This reflects the biases and stereotypes "learned" from their creators. AI developers must continue to work on improving AI processing and linguistics for a more humanized approach.AI may also change how we approach education and tutoring. AI tutoring programs and AIassisted mental health services could provide 24/7 accessibility for students and personalized assessments for academic, behavioral, and mental health issues. However, concerns remain over the potential misuse of AI technology for cheating and plagiarism in academic settings.Despite concerns about privacy and the scifi notion of AI robots taking over, AI will continue to open up new discoveries in all aspects of life. This technology carries both risks and benefits, but if used with care, it can bring positive developments while avoiding risks to livelihoods and privacy.【重点词汇】1. AI /ˌɑːtɪˌɪnˈtelɪdʒəns/ n. 人工智能，人工才智2. artificial /ˌɑːrtɪˈfɪʃl/ adj. 人造的，人工的3. intelligence /ɪnˈtelɪdʒəns/ n. 智能，才智4. programmer /ˈprəʊɡræmər/ n. 程序员，编程者5. code /kəʊd/ v. 编码，编程6. train /treɪn/ v. 训练，培育7. puter /kəmˈpjuːtə(r)/ n. 计算机，电脑8. visual /ˈvɪʒuəl/ adj. 视觉的，视力的9. recognition /rekəɡˈnɪʃn/ n. 认知，识别10. speech /spiːtʃ/ n. 语音，演讲11. problemsolving /ˈprɑːbləmˌsɑlvɪŋ/ n. 解决问题的力量12. algorithm /ˈælɡərɪðəm/ n. 算法，计算方法13. customize /ˈkʌstəmaɪz/ v. 定制，定做14. content /ˈkɒntent/ n. 内容，名目15. online /ˈɒnlaɪn/ adj. 在线的，联网的16. natural language /ˈnætʃrəl ˈleŋɡwɪdʒ/ 自然语言17. processor /ˈprəʊsesə(r)/ n. 处理器，处理机18. interact /ˌɪntərˈækt/ v. 相互作用，相互影响19. conversational /ˌkɑːnvəˈseɪʃənl/ adj. 对话的，会话的20. chatbot /ˈtʃætˌbɒt/ n. 谈天机器人21. available /əˈveɪləbl/ adj. 可获得的，可得到的22. customer service /ˈkʌstəməˈsɜːvɪs/ 客户效劳23. generate /ˈdʒenəreɪt/ v. 生成，产生24. sales lead /seɪlz liːd/ 销售线索25. protect /prəˈtekt/ v. 爱护，防护26. unauthorized /ˌʌnˈɔːθəraɪzd/ adj. 未经授权的，越权的27. image /ˈɪmɪdʒ/ n. 图像，形象28. privacy /ˈpraɪvəsi/ n. 隐私，保密29. automation /ˌɔːtəˈmeɪʃn/ n. 自动化，自动化掌握30. emotion /ɪˈməʊʃn/ n. 情感，心情31. nuanced /ˈnjuːɑːnst/ adj. 微小的，差异的32. cue /kjuː/ n. 示意，信号，提示33. bias /ˈbaɪəs/ n. 偏见，偏向34. stereotype /ˈsteriətaɪp/ n. 刻板印象，陈规旧习35. approach /əˈprəʊtʃ/ v. 接近，靠近36. education /ˌedʒuˈkeɪʃn/ n. 教育，教学37. tutor /ˈtjuːtə(r)/ n. 导师，辅导员，家庭老师38. mental health /ˌmentl ˈhelθ/ 心理健康39. accessible /əkˈsesəbl/ adj. 可得到的，可进入的40. selfpaced /self peɪst/ adj. 自我调整的，自学的【阅读理解练习题】1. What is the main purpose of "Take It Down"?A. To help teenagers remove unauthorized photos fromthe Internet.B. To collect data and invade privacy.C. To simulate human emotions in AI chatbots.D. To generate sales leads.答案：A中文解析：题目问的是“Take It Down〞的主要目的是什么，依据文章其次段第一句可知，“T ake It Down〞是Meta开发的一个机器人关心青少年从互联网上删除未经授权的照片。
多智能体强化学习的几种BestPractice（草稿阶段，完成度40%）多智能体强化学习的几种Best Practice - vonZooming的文章 - 知乎 https:///p/99120143这里分享一下A Survey and Critique of Multiagent Deep Reinforcement Learning这篇综述里面介绍的多智能体强化学习Best Practice。
这部分内容大部分来自第四章，但是我根据自己的理解加上了其他的内容。
1.改良Experience replay buffer1.1 传统的Single-agent场景之下的Replay bufferReplay Buffer[90, 89]自从被提出后就成了Single-Agent强化学习的常规操作，特别是DQN一炮走红之后[72] 。
不过，Replay Buffer有着很强的理论假设，用原作者的话说是——The environment should not changeover time because this makes pastexperiences irrelevantor even harmful.（环境不应随时间而改变，因为这会使过去的experience replay变得无关紧要甚至有害）Replay buffer假设环境是stationary的，如果当前的环境信息不同于过去的环境信息，那么就无法从过去环境的replay中学到有价值的经验。
（画外音：大人，时代变了……别刻舟求剑了）在multi-agent场景下，每个agent都可以把其他的agent当作环境的一部分。
因为其他的agent不断地学习进化，所以agent所处的环境也是在不断变换的，也就是所谓的non-stationary。
因为multi-agent场景不符合replay buffer的理论假设，所以有的人就直接放弃治疗了——例如2016年发表的大名鼎鼎的RIAL和DIAL中就没有使用replay buffer。
controlnet训练代码英文回答：ControlNet is a training code that is used in computer vision tasks, specifically for object detection and localization. It is designed to provide a reliable and efficient way to train deep neural networks for these tasks.One of the main advantages of ControlNet is its ability to handle large-scale datasets. It can efficiently process and train on millions of images, which is crucial for achieving state-of-the-art performance in object detection. This is especially important in applications such as autonomous driving, where the network needs to be trainedon a vast amount of diverse and representative data.ControlNet also incorporates various advancedtechniques to improve the training process. For example, it utilizes data augmentation techniques to artificially increase the size of the training dataset. This helps toprevent overfitting and improve the generalization abilityof the network. Data augmentation techniques can include random cropping, flipping, rotation, and color jittering.Additionally, ControlNet employs a multi-scale training strategy. This means that during training, the network is exposed to objects of different scales, from small to large. This helps the network learn to detect objects at various sizes and improves its robustness in real-world scenarios. For example, in the context of autonomous driving, objects can appear at different distances and scales, and the network needs to be able to accurately detect them regardless of their size.Furthermore, ControlNet utilizes a combination of different loss functions to optimize the network. One commonly used loss function is the localization loss, which penalizes the discrepancy between the predicted boundingbox and the ground truth bounding box. Another lossfunction is the classification loss, which penalizes the misclassification of objects. By combining these loss functions, ControlNet can effectively learn to localize andclassify objects simultaneously.ControlNet also incorporates transfer learning, which allows the network to leverage pre-trained models on large-scale datasets such as ImageNet. This helps to bootstrap the training process and enables the network to learn from the knowledge already acquired by the pre-trained model. Transfer learning can significantly speed up the training process and improve the final performance of the network.中文回答：ControlNet是一种用于计算机视觉任务的训练代码，特别用于目标检测和定位。
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SevenGo ™ portable pH-meter2. In-line turbidity, pH and conductivity sensors3. DL22 Food and beverage analyzer4. Halogen moisture analyzersA wide range of solutions to improve processes1. Statistical Quality Control/Statistical Process Control2. Process weighing3. Predictive maintenance4. Methods of moisture content determinationShare our knowledgeLearn from our specialists – our knowledge and experience are at your disposal in print or online.Learn more about all of our solutions for the dairy and cheese industry at our website. You can find information on a wide range of topics to improve your processes, including case studies,application stories, return-on invest-ment calculators, plus all the product information you need to make aninformed decision.1 2 341423。
ChatGPT专题|ChatGPT/InstructGPT详解前言GPT系列是OpenAI的一系列预训练文章，GPT的全称是Generative Pre-Trained Transformer，顾名思义，GPT的目的就是通过Transformer为基础模型，使用预训练技术得到通用的文本模型。
目前已经公布论文的有文本预训练GPT-1，GPT-2，GPT-3，以及图像预训练iGPT。
据传还未发布的GPT-4是一个多模态模型。
最近非常火的ChatGPT和今年年初公布的[1]是一对姐妹模型，是在GPT-4之前发布的预热模型，有时候也被叫做GPT3.5。
ChatGPT和InstructGPT在模型结构，训练方式上都完全一致，即都使用了指示学习（Instruction Learning）和人工反馈的强化学习（Reinforcement Learning from Human Feedback，RLHF）来指导模型的训练，它们不同的仅仅是采集数据的方式上有所差异。
所以要搞懂ChatGPT，我们必须要先读懂InstructGPT。
1.背景知识在介绍ChatGPT/InstructGPT之前，我们先介绍它们依赖的基础算法。
1.1 GPT系列基于文本预训练的GPT-1[2]，GPT-2[3]，GPT-3[4]三代模型都是采用的以Transformer为核心结构的模型（图1），不同的是模型的层数和词向量长度等超参，它们具体的内容如表1。
图1：GPT系列的模型结构（其中Trm是一个Transformer结构）表1：历代GPT的发布时间，参数量以及训练量模型发布时间层数头数词向量长度参数量预训练数据量GPT-12018 年 6 月1212768 1.17 亿约 5GBGPT-22019 年 2 月48-160015 亿40GBGPT-32020 年 5 月9696128881,750 亿45TBGPT-1比BERT诞生略早几个月。
To Transfer or Not To TransferMichael T.Rosenstein,Zvika Marx,Leslie Pack KaelblingComputer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridge,MA02139{mtr,zvim,lpk}@Thomas G.DietterichSchool of Electrical Engineering and Computer ScienceOregon State UniversityCorvallis,OR97331tgd@AbstractWith transfer learning,one set of tasks is used to bias learning and im-prove performance on another task.However,transfer learning may ac-tually hinder performance if the tasks are too dissimilar.As describedin this paper,one challenge for transfer learning research is to developapproaches that detect and avoid negative transfer using very little datafrom the target task.1IntroductionTransfer learning involves two interrelated learning problems with the goal of using knowl-edge about one set of tasks to improve performance on a related task.In particular,learning for some target task—the task on which performance is ultimately measured—is influenced by inductive bias learned from one or more auxiliary tasks,e.g.,[1,2,8,9].For example, athletes make use of transfer learning when they practice fundamental skills to improve training in a more competitive setting.Even for the restricted class of problems addressed by supervised learning,transfer can be realized in many different ways.For instance,Caruana[2]trained a neural network on several tasks simultaneously as a way to induce efficient internal representations for the target task.Wu and Dietterich[9]showed improved image classification by SVMs when trained on a large set of related images but relatively few target images.Sutton and McCallum[7]demonstrated effective transfer by“cascading”a class of graphical models, with the prediction from one classifier serving as a feature for the next one in the cascade. In this paper we focus on transfer using hierarchical Bayesian methods,and elsewhere we report on transfer using learned prior distributions over classifier parameters[5].In broad terms,the challenge for a transfer learning system is to learn what knowledge should be transferred and how.The emphasis of this paper is the more specific problem of deciding when transfer should be attempted for a particular class of learning algorithms. With no prior guarantee that the auxiliary and target tasks are sufficiently similar,an algo-rithm must use the available data to guide transfer learning.We are particularly interested in the situation where an algorithm must detect,perhaps implicitly,that the inductive bias learned from the auxiliary tasks will actually hurt performance on the target task.In the next section,we describe a“transfer-aware”version of the naive Bayes classification algorithm.We then illustrate that the benefits of transfer learning depend,not surprisingly, on the similarity of the auxiliary and target tasks.The key challenge is to identify harmful transfer with very few training examples from the target task.With larger amounts of “target”data,the need for auxiliary training becomes diminished and transfer learning becomes unnecessary.2Hierarchical Naive BayesThe standard naive Bayes algorithm—which we callflat naive Bayes in this paper—has proven to be effective for learning classifiers in non-transfer settings[3].Theflat naive Bayes algorithm constructs a separate probabilistic model for each output class,under the “naive”assumption that each feature has an independent impact on the probability of the class.We chose naive Bayes not only for its effectiveness but also for its relative sim-plicity,which facilitates analysis of our hierarchical version of the algorithm.Hierarchical Bayesian models,in turn,are well suited for transfer learning because they effectively combine data from multiple sources,e.g.,[4].To simplify our presentation we assume that just two tasks,A and B,provide sources of data,although the methods extend easily to multiple A data sources.Theflat version of naive Bayes merges all the data without distinction,whereas the hierarchical version con-structs two ordinary naive Bayes models that are coupled together.LetθA i andθB i denote the i-th parameter in the two models.Transfer is achieved by encouragingθA i andθB i to have similar values during learning.This is implemented by assuming thatθA i andθB i are both drawn from a common hyperprior distribution,P i,that is designed to have unknown mean but small variance.Consequently,at the start of learning,the values ofθA i andθB i are unknown,but they are constrained to be similar.As with any Bayesian learning method,learning consists of computing posterior distribu-tions for all of the parameters in the two models,including the hyperprior parameters.The overall model can“decide”that two parameters are very similar(by decreasing the variance of the hyperprior)or that two other parameters are very different(by increasing the vari-ance of the hyperprior).To compute the posterior distributions,we developed an extension of the“slice sampling”method introduced by Neal[6].3ExperimentsWe tested the hierarchical naive Bayes algorithm on data from a meeting acceptance task. For this task,the goal is to learn to predict whether a person will accept an invitation to a meeting given information about(a)the current state of the person’s calendar,(b)the person’s roles and relationships to other people and projects in his or her world,and(c)a description of the meeting request including time,place,topic,importance,and expected duration.Twenty-one individuals participated in the experiment:eight from a military exercise and 13from an academic setting.Each individual supplied between99and400labeled ex-amples(3966total examples).Each example was represented as a15-dimensional feature vector that captured relational information about the inviter,the proposed meeting,and any conflicting meetings.The features were designed with the meeting acceptance task in mind but were not tailored to the algorithms studied.For each experiment,a single person was08162432Amount of Task B Training (# instances)T a s k B P e r f o r m a n c e (% c o r r e c t )Figure 1:Effects of B training set size on performance of the hierarchical naive Bayes al-gorithm for three cases:no transfer (“B-only”)and transfer between similar and dissimilar individuals.In each case,the same person served as the B data source.Filled circles de-note statistically significant differences (p <0.05)between the corresponding transfer and B-only conditions.chosen as the target (B )data source;100of his or her examples were set aside as a holdout test set,and from the remaining examples either 2,4,8,16,or 32were used for training.These training and test sets were disjoint and stratified by class.All of the examples from one or more other individuals served as the auxiliary (A )data source.Figure 1illustrates the performance of the hierarchical naive Bayes algorithm for a single B data source and two representative A data sources.Also shown is the performance for the standard algorithm that ignores the auxiliary data (denoted “B-only”in the figure).Transfer learning has a clear advantage over the B-only approach when the A and B data sources are similar,but the effect is reversed when A and B are too dissimilar.Figure 2a demonstrates that the hierarchical naive Bayes algorithm almost always performs at least as well as flat naive Bayes,which simply merges all the available data.Figure 2b shows the more interesting comparison between the hierarchical and B-only algorithms.The hierarchical algorithm performs well,although the large gray regions depict the many pairs of dissimilar individuals that lead to negative transfer.This effect diminishes—along with the positive transfer effect—as the amount of B training data increases.We also ob-served qualitatively similar results using a transfer-aware version of the logistic regression classification algorithm [5].4ConclusionsOur experiments with the meeting acceptance task demonstrate that transfer learning often helps,but can also hurt performance if the sources of data are too dissimilar.The hierar-chical naive Bayes algorithm was designed to avoid negative transfer,and indeed it does so quite well compared to the flat pared to the standard B-only approach,however,there is still room for improvement.As part of ongoing work we are exploring the use of clustering techniques,e.g.,[8],to represent more explicitly that some sources of data may be better candidates for transfer than others.Amount of Task B Training (#instances)F r a c t i o n o f P e r s o n P a i r sAmount of Task B Training (#instances)F r a c t i o n o f P e r s o n P a i r s(a)(b)Figure 2:Effects of B training set size on performance of the hierarchical naive Bayes al-gorithm versus (a)flat naive Bayes and (b)training with no auxiliary data.Shown are the fraction of tested A-B pairs with a statistically significant transfer effect (p <0.05).Black and gray respectively denote positive and negative transfer,and white indicates no statis-tically significant difference.Performance scores were quantified using the log odds of making the correct prediction.AcknowledgmentsThis material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA),through the Department of the Interior,NBC,Acquisition Services Division,under Con-tract No.NBCHD030010.Any opinions,findings,and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.References[1]J.Baxter.A model of inductive bias learning.Journal of Artificial Intelligence Research ,12:149–198,2000.[2]R.Caruana.Multitask learning.Machine Learning ,28(1):41–70,1997.[3]P.Domingos and M.Pazzani.On the optimality of the simple bayesian classifier under zero-one loss.Machine Learning ,29(2–3):103–130,1997.[4] A.Gelman,J.B.Carlin,H.S.Stern,and D.B.Rubin.Bayesian Data Analysis,Second Edition .Chapman and Hall/CRC,Boca Raton,FL,2004.[5]Z.Marx,M.T.Rosenstein,L.P.Kaelbling,and T.G.Dietterich.Transfer learning with an ensemble of background tasks.Submitted to this workshop.[6]R.Neal.Slice sampling.Annals of Statistics ,31(3):705–767,2003.[7] C.Sutton and position of conditional random fields for transfer learning.In Proceedings of the Human Language Technologies /Emprical Methods in Natural Language Processing Conference (HLT/EMNLP),2005.[8]S.Thrun and J.O’Sullivan.Discovering structure in multiple learning tasks:the TC algorithm.In L.Saitta,editor,Proceedings of the Thirteenth International Conference on Machine Learning ,pages 489–497.Morgan Kaufmann,1996.[9]P.Wu and T.G.Dietterich.Improving SVM accuracy by training on auxiliary data sources.In Proceedings of the Twenty-First International Conference on Machine Learning ,pages 871–878.Morgan Kaufmann,2004.。
关注培训是提高认知功能，增强重点和集中度的一个关键方面。
在本篇文章中，我们将讨论可用于培养注意力的各种参数，以及关注培训在生活不同方面的重要性。
必须了解可以培训的不同关注部分。
这包括有选择地关注、持续关注、分散关注和交替关注。
选择性关注是指既关注特定刺激又忽略其他刺激的能力，持续关注是指长时间保持关注的能力，分化关注是指同时关注多个任务的能力，交替关注是指在不同任务或刺激之间转移关注的能力。
关注培训中可使用若干关键参数，包括任务难度、培训期限、培训频率和反馈。
任务困难很重要，因为它有助于挑战个人并提高他们的注意能力。
培训期限是指每期培训班的长度，较长的培训班往往能更有效地提高关注程度。
培训的频率是指个人参加关注培训的频率，定期和持续的培训对于长期改进至关重要。
反馈对于关注培训也至关重要，因为它有助于个人了解他们的长处和弱点，并相应调整他们的培训。
在体育领域可以看到关注培训重要性的一个例子。
运动员需要强大的关注能力才能表现得最好，无论是在漫长的比赛中保持关注，还是在快节奏的比赛中迅速转移对手之间的注意力。
通过将注意力训练纳入常规，运动员可以提高其集中度，反应时间，以及整体性能。
除体育外，关注培训在学术和专业环境中也很重要。
学生和专业人员都能从关注的改善中受益，因为这可以导致更好的学习、生产力和总体成功。
通过利用任务难度、持续时间、频率和反馈等关注性培训参数，个人可以提高注意力能力，更有效地实现目标。
注意力训练是认知功能的一个重要方面，可以通过任务难度，持续时间，频率和反馈等各种参数加以改进。
通过将注意力训练纳入日常活动，个人可以增强他们的注意力和集中，从而改善生活各个方面的表现。
无论是在体育、学术还是专业环境中，关注培训都可以对整体成功和福祉产生积极影响。
英语作文-人工智能助力金融风控，提高风险防控能力In the realm of finance, risk management is a pivotal aspect that determines the stability and success of financial institutions. The advent of artificial intelligence (AI) has revolutionized numerous industries, and finance is no exception. AI's contribution to financial risk control is multifaceted, enhancing the ability to predict, manage, and mitigate risks in an unprecedented manner.AI algorithms are adept at analyzing vast amounts of data, identifying patterns that are imperceptible to the human eye. This capability is particularly beneficial in the detection of fraudulent activities. By scrutinizing transaction data, AI systems can flag anomalies that deviate from established patterns, alerting institutions to potential fraud. This proactive approach to fraud detection is far more efficient than traditional methods, which often involve manual review and can result in delayed responses.Moreover, AI plays a crucial role in credit risk assessment. Financial institutions rely on credit scores to evaluate the risk of lending to individuals or businesses. AI enhances this process by incorporating a broader range of data points, including non-traditional data such as social media activity or mobile phone usage patterns. This results in a more comprehensive risk profile, enabling lenders to make more informed decisions.In market risk management, AI's predictive analytics are invaluable. By analyzing market trends and external factors, AI can forecast market movements with a higher degree of accuracy. This allows financial institutions to adjust their investment strategies accordingly, minimizing potential losses due to market volatility.Operational risk, which encompasses the risk of loss from inadequate or failed internal processes, people, and systems, is another area where AI is making strides. Through the implementation of AI-powered automation and monitoring systems, institutions can reduce the likelihood of human error and system failures, which are common sources of operational risk.Liquidity risk management also benefits from AI's capabilities. AI systems can predict cash flow trends and liquidity needs, ensuring that financial institutions maintain adequate reserves to meet their obligations. This is crucial for maintaining solvency and preventing liquidity crises.The integration of AI into financial risk management not only enhances the efficiency and accuracy of risk assessment but also provides a competitive edge. Institutions that leverage AI technologies can offer more attractive terms to customers, as they have a better understanding of the risk involved. This can lead to increased customer satisfaction and loyalty, as well as improved financial performance for the institution.In conclusion, AI's role in financial risk management is transformative. It empowers institutions with advanced tools to tackle the complexities of risk in the modern financial landscape. As AI technology continues to evolve, its potential to further improve risk management is vast, promising a future where financial stability is bolstered by intelligent, data-driven decision-making. The synergy between AI and financial risk control is not just a technological advancement; it is a paradigm shift that redefines the boundaries of what is possible in finance. 。
题目：探究Hackerrank Prudential题库对技能测评的影响一、概述1.1 Hackerrank和Prudential介绍Hackerrank是一家专注于编程技能测评和招聘服务的公司，提供上线评测评台和编程挑战，帮助企业发掘技术人才。
Prudential是一家跨国金融服务公司，致力于为客户提供金融保障和投资解决方案。
1.2 题库的重要性在技能测评中，题库起着至关重要的作用。
题库设计合理与否不仅影响着测评结果的准确性，也直接关系到对技能的全面考量。
二、Hackerrank Prudential题库的设计2.1 题型多样性通过多种问题类型，包括编程题、算法题、逻辑题等，全面考察受测者的技能水平。
2.2 难度等级设置题库中包含不同难度等级的问题，从入门级到专业级，以满足不同技术水平人裙的需求。
三、Hackerrank Prudential题库的使用3.1 招聘流程中的应用Prudential在招聘技术人员时，可以利用Hackerrank提供的题库进行技能测评，更好地筛选出具备所需技能的候选人。
3.2 培训与培养Prudential内部也可以利用题库进行员工技能提升培训，根据题库结果制定培训方案，提高员工的技术能力。
四、题库对技能测评的影响4.1 准确性题库设计合理，能较为全面地评估受测者的技能水平，从而提高测评准确性。
4.2 公平性合理的难度设置和题型多样性，能够使测评更为公平，减少主观因素的干扰。
4.3 可信度题库经过合理设计和多次验证，使得测评结果更有可信度，为招聘和培训提供更可靠的依据。
五、结论Hackerrank Prudential题库通过合理的设计和应用，对技能测评起到重要的推动作用。
合理的题库设计能够提高技能测评的准确性、公平性和可信度，为企业招聘和培训提供更可靠的支持。
随着技术的不断发展，题库的不断更新和完善，将不断提升技能测评的效果和影响力。
六、参考文献[1] Hackerrank冠方全球信息站[2] Prudential冠方全球信息站[3] Beasley, John, John Ezell, and Doug Gerdes. "Creating an enhanced pre-hire skills assessment model." Industrial and Commercial Tr本人ning 49.3 (2017): 116-122.以上文章为知识答案格式的非Markdown格式的普通文本。
Cretaceous and Cenozoic cooling history across the ultrahighpressure Tongbai –Dabie belt,central China,fromapatite fission-track thermochronologyShengbiao Hu a,⁎,Barry P.Kohn b,⁎,Asaf Raza b ,Jiyang Wang a ,Andrew J.W.Gleadow ba Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China bSchool of Earth Sciences,The University of Melbourne,Melbourne,Victoria 3010,Australia Received 24June 2005;received in revised form 9March 2006;accepted 22March 2006AbstractThe crystalline terrane of the Tongbai –Dabie region,central China,comprising the Earth's largest ultrahigh-pressure (UHP)exposure was formed during Triassic collision between the Sino –Korean and Yangtze cratons.New apatite fission-track (AFT)data presented here from the UHP terrane,extends over a significantly greater area than reported in previous studies,and includes the (eastern)Dabie,the Hong'an (northwestern Dabie)and Tongbai regions.The new data yield ages ranging from 44±3to 142±36Ma and mean track lengths between ∼10and 14.4μm.Thermal history models based on the AFT data taken together with published 40Ar/39Ar,K –Ar,apatite and zircon (U –Th)/He and U –Pb data,exhibit a three-stage cooling pattern that is similar across the study region,commencing with an Early Cretaceous rapid cooling event,followed by a period of relative thermal stability during which rocks remained at temperatures within the AFT partial annealing zone (∼60–110°C)and ending with a possible renewed phase of accelerated cooling during Pliocene to Recent time.The first cooling phase followed large-scale transtensional deformation between ∼140and 110Ma and is related to Early Cretaceous eastward tectonic escape and Pacific back arc extension.Between this phase and the subsequent slow cooling phase,a transition period from ∼120to 80Ma (to ∼70to 45Ma along the Tan –Lu fault)was characterised by a relatively low cooling rate (∼3–5°C/Ma).This transition is likely related to a tectonic response associated with the mid-Cretaceous subduction of the Izanagi –Pacific plate as well as lithospheric extension and thinning in eastern Asia.The present regional AFT age pattern is therefore basically controlled by the Early Cretaceous rapid cooling event,but finally shaped through active Cenozoic faulting.Following the transition phase the subsequent slow cooling phase pattern implies a net reduction in horizontal compressional stress corresponding to increased extension rates along the continental margin due to the decrease in plate convergence.Modelling of the AFT data suggests a possible Pliocene –Recent cooling episode,which may be supported by increased rates of sedimentation observed in adjacent basins.This cooling phase may be interpreted as a response to the far-field effects of the frontal India –Eurasia collision to the west.Approximate estimates suggest that the total amount of post ∼120Ma denudation across the UHP orogen ranged from ∼2.4to 13.2km for different tectonic blocks and ranged from ∼0.8to 9.7km during the Cretaceous to between ∼1.7and 3.8km during the Cenozoic.©2006Elsevier B.V .All rights reserved.Keywords:Apatite fission-track thermochronology;Tectonics;Denudation;Ultrahigh pressure rocks;Tongbai –Dabie;CentralChinaTectonophysics 420(2006)409–429/locate/tecto⁎Corresponding authors.Shengbiao Hu is to be contacted at Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China.Tel.:+861062008116;fax:+861062010846.Barry P.Kohn,School of Earth Sciences,The University of Melbourne,Melbourne,Victoria 3010,Australia.Tel.:+61383447217;fax:+61383447761.E-mail addresses:sbhu@ (S.Hu),b.kohn@.au (B.P.Kohn).0040-1951/$-see front matter ©2006Elsevier B.V .All rights reserved.doi:10.1016/j.tecto.2006.03.0271.IntroductionThe Tongbai –Dabie ultrahigh-pressure (UHP)and high-pressure (HP)metamorphic belt,central China,is part of the 2000km long Qinling –Dabie orogen formed by deep subduction (>120km)during the Triassic of the Yangtze craton beneath the Sino –Korean craton (Fig.1)(e.g.,Li et al.,1989;Wang et al.,1989;Hacker et al.,1996;Ames et al.,1996;Liou et al.,1996;Rowley et al.,1997;Faure et al.,2001).The relatively large extent of the Tongbai –Dabie terrane and good exposures have formed a worldwide focal point for interdisciplinary studies related to the UHP metamor-phism and the exhumation of a deeply subducted continental slab.Most works have focused on pre-Cretaceous processes,but very few have addressed the Cretaceous to Recent thermo-tectonic evolution of the region,particularly in relation to the final exhumation history of the UHP rocks (Ratschbacher et al.,2000;Xu et al.,2001).As a temperature-sensitive thermochronological technique,apatite fission-track (AFT)analysis is a powerful tool for constraining the low-temperature history of rocks over a temperature range of ∼60–110°C (e.g.,Gallagher et al.,1998;Gleadow et al.,2002).These temperatures,depending on the geother-mal gradient,equate to a burial depth of ∼3–5km.Thus AFT thermochronology can be used to reconstruct the cooling history of rocks as they approached the surface in response to erosion and tectonic processes.Early fission-track studies reported from UHP rocks of the Tongbai –Dabie area (Chen et al.,1995;Wang and Yang,1998;Xu et al.,2001)lacked confined track-length data and therefore considerably weakened discussion of the Late Cretaceous –Cenozoic cooling history.Recently,Grimmer et al.(2002)combined detailed AFT and structural data in the eastern Dabie (in the vicinity of the Tan –Lu fault)and proposed a history related to the Tan –Lu fault involving an enhanced cooling episode for some samples at ∼45±10Ma.This episode was attributed to far-field tectonic effects related to the India –Asia collision and another episode at ∼25Ma to a regional unconformity,but not necessarily observed in the AFT data.Reiners et al.(2003)presented a wellconstrainedFig.1.Generalized geological map of Tongbai –Dabie (modified after Hacker et al.,1995,2000;Ratschbacher et al.,2000;Zhang et al.,2001),showing the location of apatite fission-track samples.Inset shows the Triassic collisional orogen in central China.410S.Hu et al./Tectonophysics 420(2006)409–429cooling history based on fewer samples from a similar area in the Dabie region,using zircon and apatite (U–Th)/He in addition to AFT data.Their interpretation differed significantly from that proposed by Grimmer et al.(2002)in that no significant variation in exhumation rates were recorded over the past∼115Ma(aside from slight differences between the core and the flanks of the Dabie Shan),except for a possible modest increase in exhumation between∼80and40Ma.The AFT data presented here complement these earlier data sets,in that the western part of the UHP orogen,including the Dongbai and Hong'an regions have now been covered (some∼50to200km west of the previously reported areas)as well as some areas overlapping with those from where previous results were reported in the eastern Dabie,thus providing a cross-check on the consistency of the results.Zhou et al.(2003)and Xu et al.(2005) have provided further constraints on the exhumation history of the Dabie region.The sampling strategy employed for this study was designed to address two main questions:1)Is there a discernible response in the cooling history style across the Tongbai–Dabie UHP region during the transition between the Cretaceous to Early Cenozoic which can be related to the widespread tectonism(lithospheric extension and thinning related to the Pacific subduction) at this time in the Eastern Asia region?2)What is the record of low temperature exhumation and cooling,if any,that can be related to major faults,such as the Tongbai Shear Zone,Shang–Ma fault,as well as the important Tan–Lu fault?2.Geological and geodynamic settingThe Tongbai–Dabie area in the Qinling–Dabie orogenic belt consists of three main areas,from west to east:the Tongbai,Hong'an(northwestern Dabie)and Dabie(Fig.1).The orogen comprises a penetratively deformed metamorphic core and is subdivided from south to north into the Yangtze Foreland Thrust Belt (YFTB),the Dabie metamorphic complex,including blueschist,HP amphibolite,quartz eclogite and coesite eclogite units,and“North Orthogneiss”Unit(NOU),the Luzhengguan and Foziling“Group”in the eastern Dabie,and in addition a variety of Paleozoic and Proterozoic groups along the northern rim of the Dongbai and northwestern Dabie,i.e.,North Huaiyang Foreland Belt(NHFB).Late Jurassic and younger volcano-sedimentary strata overlie the UHP orogen along its margins(Fig.1)(e.g.,Liu and Hao,1989; Okay et al.,1993;Hacker et al.,1996;Wang and Cong, 1998;Ratschbacher et al.,2000).The Dabie metamorphic complex formed in a northdipping subduction zone during Triassic collision andwas exhumed as a coherent slab.These rocks areexposed in the Hong'an and Dabie ranges as well asSulu to the east in the eastern Shandong Peninsula.Therocks have undergone retrograde greenschist andamphibolite-facies metamorphism,and all are intrudedby Cretaceous plutons(e.g.,Hacker et al.,1995;Webb etal.,1999;Eide and Liou,2000).The north dippingHuwan detachment zone and the Xiaotian–Mozitan(X–M)sinistral strike-slip fault define the northern topo-graphic limit of both regions and separate high-grademetamorphic rocks from greenschist-facies rocks of theFoziling and Luzhengguan groups in the NHFB(Hackeret al.,1996)(Fig.1).The Tan–Lu fault is a majorcontinental structure,which marks the eastern boundaryof the Tongbai–Dabie(Fig.1).On the basis of theapparent offset of the Dabie and Su–Lu UHP terranes,approximately500km of sinistral strike-slip motionduring the Triassic–Jurassic or the Cretaceous has beenpostulated along the fault(Xu,1984;Okay and Sengör,1992;Yin and Nie,1993;Zhu et al.,2001)(Fig.1).Different tectonic models have been suggested toexplain the exhumation of the UHP–HP orogen(Okayand Sengör,1992;Maruyama et al.,1994;Hacker et al.,1995;Wang and Cong,1999;Hacker et al.,2000)fromdepths of120to140km or greater(Zhang et al.,1999),but the geodynamics of the process are complicated andare still the subject of ongoing investigations.Based onthe available geochronological and geological data,thecooling path accompanying the exhumation is consid-ered to be multi-phased(e.g.,Cong et al.,1994;Hackeret al.,1998,2000;Li et al.,2000a,b;Suo et al.,2000)asfollows:1)210–200Ma:UHP rocks were quicklyreturned from mantle depth to middle–lower crust levelsby delamination of the overthickened lithosphericmantle due to collision between the Yangtze andSino–Korean cratons between240and210Ma;2)∼200–170Ma(probably to130Ma,Suo et al.,2000): the UHP and retrograded UHP rocks were graduallyexhumed to mid crustal depths due to lithosphericextension;3)∼130Ma to present-day:current UHPoutcrops were exhumed through differential uplift of thefault-bounded blocks and extensional collapse of themountain belt.Cretaceous extension and magmatism were wide-spread throughout eastern China(e.g.,Davis et al.,1996;Yin and Nie,1996)and have been attributed toeastward tectonic escape(Tapponnier et al.,1982),andPacific subduction(e.g.,Yin and Nie,1996).Litho-spheric delamination resulted from the subduction(e.g.,Menzies et al.,1993;Gao et al.,2002;Wu et al.,2005)411S.Hu et al./Tectonophysics420(2006)409–429as well as the Siberia–Mongolia–Sino–Korean collision (e.g.,Enkin et al.,1992).The Cenozoic evolution of eastern Asia has therefore been interpreted as the result of the combined effects of Pacific subduction and the India–Eurasia collision(e.g.,Molnar and Tapponnier, 1975;Yin and Nie,1996).Reheating,exhumation and cooling of the UHP orogen during Cretaceous reactiva-tion was characterized by thermal doming and strike-slip and normal faulting in the Tongbai–Dabie region which‘overprinted’the original orogenic structure.The major crust-shaping event at this time was the formation of the magmatic–metamorphic–structural dome of the NOU under general NW–SE subhorizonal extension and NE–SW contraction with activation of the X–M detachment fault zone(Fig.1),involving exhumation, magmatism and cooling controlled by deformation following a rolling hinge-isostatic rebound model (Ratschbacher et al.,2000).3.Sampling strategyA total of71samples weighing∼1–2kg were collected regionally from all the major tectonic units in the Tongbai–Dabie belt along three broad N–S profiles crossing the Tongbai,the northwestern Dabie(Hong'an) and the eastern Dabie,respectively(see Fig.1).Sample localities in the eastern Dabie overlapped with those from where AFT data have been reported by Grimmer et al.(2002)and Reiners et al.(2003),but the coverage was expanded westwards by∼50to200km,respec-tively,to the Hong'an and Tongbai areas.Although samples were taken over a range of elevations(105–1243m),they do not fulfill the requirements for a vertical fission-track sampling profile approach(e.g., Fitzgerald et al.,1999).A high-density fault network made it difficult to sample a non-disrupted vertical profile within one tectonic block(Fig.1).However,the wide areal and vertical distribution of samples does allow for differential cooling histories along the main faults to be evaluated.Most of the metamorphic rocks sampled,including the blueschist,HP amphibolite,quartz eclogite,coesite eclogite,and gneiss,in the Dabie metamorphic complex and metasandstone of greenschist-grade from the Luzhengguan and Foziling“Group”all yield sufficient apatite grains for analysis,but blueschist samples from the Susong“Group”lacked apatite.Samples were also collected from undeformed Cretaceous and deformed (Northwest Tongbai)Paleozoic(?)granites and grano-diorites,which crystallized at depths estimated from ∼3.5–16km(Zhang et al.,1996;Yang et al.,1999; Ratschbacher et al.,2000).Two samples(SU-1,SU-2)from basement of the Yangtze craton in the YFTB southof Tongbai(Fig.1)were also analysed.4.Low temperature thermochronology andmethodology4.1.ThermochronologyThe length reduction of confined tracks withinapatites due to thermal annealing leads to reduction ofthe AFT age,and the length distribution of the confinedtracks directly reflects the sample's thermal historysubsequent to the last time it cooled below∼110°C(Gallagher et al.,1998;Gleadow et al.,2002).Thus,thissystem is ideal for determining the timing of denudationin response to cooling of upper crustal rocks(Gleadowand Brown,2000).In this study the AFT data havemainly been interpreted using the system response(Green et al.,1989)based on an empirical kineticdescription of laboratory annealing data in Durangoapatite described by Laslett et al.(1987).Usefulsummaries of fission-track dating and annealing aregiven by Wagner and Van den Haute(1992);Gallagheret al.(1998),Dumitru(2000),Gleadow et al.(2002)andDonelick et al.(2005).AFT thermochronology does not provide wellconstrained estimates of cooling<∼60–70°C,char-acteristic of the shallowest crustal depths and hencemost closely related to the surface manifestation ofany tectonic movements and denudation.An importanttechnical development in recent years however,theadvent of(U–Th)/He apatite thermochronometry,asystem with an effective closure temperature of ∼70°C for a cooling rate of∼10°C/Myr and grain radius of∼100μm(e.g.,Wolf et al.,1996;Farley,2002)provides an opportunity to partially address thisissue.The(U–Th)/He apatite system is thus ideal forproviding complementary information on the lowesttemperature range possible to be constrained by AFTthermochronology.4.2.Methodology and modellingSample preparation and experimental methods usedfollowed those reported by Kohn et al.(1995)with theexception that neutron thermal fluences were monitoredusing muscovite attached to the CN-5standard glass(Bellemans et al.,1995).Briefly,neutron irradiation wascarried out in the well-thermalised X-7facility of theHIFAR reactor,New South Wales,Australian.Ageswere measured using the external detector method withBrazilian Ruby muscovite detectors to record induced412S.Hu et al./Tectonophysics420(2006)409–429track densities.In order to detect possible flux gradients, apatites were packed between two standard glasses during irradiation.Fission-tracks in each mount were counted in transmitted light using a dry objective at a magnification of×1250.A total of20individual crystals analyzed where possible and only fully etched and horizontal‘confined tracks’were measured in grains with polished surfaces parallel to prismatic crystal faces (Laslett et al.,1982).Suitable track lengths were measured using a projection tube and a digitising tablet calibrated using a stage micrometer.For this procedure, the same magnification as for counting was applied,and where possible,100horizontal confined track lengths were measured per sample.Ages were calculated using the zeta calibration method,and are expressed as central ages(essentially a weighted-mean age)(Galbraith and Laslett,1993).Errors were calculated using the conventional method(Green,1981)and are expressed at the±1σstandard deviation level.Because of the numerous possible time–temperature scenarios for a sample,an AFT age alone can be interpreted in a number of ways.However,when combined with fission-track length details the data allow more rigorous constraints to be placed on the interpretation of an observed age(Gleadow et al.,1986). As the AFT age and length data reflect a combination of the time over which tracks have been retained in apatite and the thermal history of the host material,integration of these two parameters can differentiate between different types of cooling histories below temperatures of∼110°C.In this study,data have been interpreted using the understanding of the AFT system response described by Green et al.(1989).This understanding is based on an empirical kinetic description of laboratory annealing data in Durango apatite(Green et al.,1986; Laslett et al.,1987).Modelling of t–T paths followed the procedure outlined by Gallagher(1995)which involves a quasi-inverse modelling approach using a stochastic search method for exploring a range of possible thermal histories with statistical testing of the predicted fission-track age and length parameters against the observed results.Since the possible solutions that satisfactorily match the observed data are not necessarily unique,a guided search by means of a genetic algorithm(Gallagher and Sambridge,1994)is used to sort through a large search(typically thousands) of potential thermal history histories.The suite of possible solutions was refined using the maximum likelihood or probability to assess each time–tempera-ture path,thus providing convergence towards an optimal fit of the observed data.The range of maximum palaeotemperatures is usually well defined in a model,while the timing is more uncertain.Furthermore,due to analytical errors associated with data acquisition and the relative insensitivity of apatite annealing kinetics at temperatures<∼60°C thermal histories should by no means be considered unique.For(U–Th)/He analysis protocols followed well-established laboratory routines for resistance furnace heating He extraction from multiple grains(Farley, 2000).For this study,each aliquot for the duplicate analysis of sample TB-11comprised4–6apatite crystals (see Table2).Only clear and euhedral grains with average grain radii in a close size range were selected. Grains were then immersed in ethanol and checked under polarised light to detect possible mineral inclu-sions.Samples were loaded into stainless steel capsules after grain characteristics had been imaged,measured and recorded.Samples were outgassed at∼870°C for 20min,spiked with3He and the gas volume determined using a Balzers quadrupole mass spectrometer.A hot blank was run after each sample to verify complete outgassing of the apatites.Following outgassing, samples were removed from their capsules and sent to CSIRO,North Ryde,Sydney for analysis.They were dissolved in HNO3and spiked with mixed Laser Spike (15ppb235U and15ppb230Th).U and Th contents were then determined at the University of Technology, Sydney with a Perkin Elmer Sciex5000a ICPMS using the isotope ratio application.Standards used contained25μl of Laser JM mixed standard,i.e.,238U Johnson Matthey(0.25ppm)and232Th Johnson Matthey (0.25ppm)with spike addition.Ages were calculated using the approach described by Farley et al.(1996). 5.Apatite fission-track and(U–Th)/He resultsOf71samples collected(Fig.1),17did not yield AFT data due to either lack of sufficient apatite grains, exceptionally low uranium concentration or spontane-ous tracks were not countable due to a high content of inclusions and dislocations making track identification difficult.Sample localities and analytical details for54 new AFT ages including45with some confined track length distribution information,covering all the major lithologies and tectonic sub-units in the study area,are presented in Table1.5.1.Fission-track dataThe54AFT apparent ages fall in the range∼44Ma (including samples DB-16,21,23,25and26in the UHP unit near the western side of the Tan–Lu fault,and DB-6 and TB8and19in the NOU and UHP units near the413S.Hu et al./Tectonophysics420(2006)409–429southern side of the X –M fault)to ∼142Ma (SU-1in the YFTB).An exception of 24±11Ma was measured in sample DB-3near the X –M fault zone,but it should be noted that tracks were measured in only two grains from this sample and no confined track lengths were recorded (see Table 1and Fig.2).This age is therefore not considered to be robust and will not be discussed further.The ages,lengths and radial plots for represen-tative samples are presented in Fig.3.The eastern Dabie samples overlap with the recently published AFT data by Grimmer et al.(2002)and by Reiners et al.(2003).The new data range from 44to 79Ma,consistent with the age range of ∼42–86Ma reported by Grimmer et al.(2002)and that of ∼44–86Ma reported by Reiners et al.(2003).The NHFB exhibits a slightly higher apparent AFT age range (60–79Ma)which is still markedly younger than the muscovite 40Ar/39Ar ages (150–120Ma)from a contact metamorphic gneiss (Foziling Group)(Ratschbacher et al.,2000)and the detrital K-feldspars 40Ar/39Ar ages (119–123Ma)from Cretaceous and Eocene non-metamorphic alluvial fan and fluvial deposits in the southern margin of Hefei basin north of NHFB (Hacker et al.,2000).Ages along the X –M fault are consistent with those in the NHFB,and therefore for discussion purposes they are combined.The NOU and HP –UHP units are also characterized by relatively young apparent AFT ages (44–77Ma).All these ages are also significantly younger than the Early Cretaceous (137to 125Ma)plutonism andformation of a thermal dome in these units as indicated by U –Pb zircon and 40Ar/39Ar K-feldspar ages,or reheating at 120–110Ma,or local reheating at ∼90–100Ma in the eastern and northwestern Dabie,particularly along the Tan –Lu fault and probably also along the Shang –Ma fault (Webb et al.,1999;Ratschbacher et al.,2000;Hacker et al.,2000;Xu et al.,2001).The apparent AFT ages show a regional trend in that they decrease towards the Tan –Lu fault.Two apparent AFT ages (133±20and 142±36Ma),the oldest measured in this study,were obtained on schist samples from the basement of the Yangtze Craton in the YFTB south of Tongbai.These ages are much older than those in the UHP unit,but similar to 40Ar/39Ar biotite ages (137.5±5Ma and 142.8±2Ma)from plutons in the YFTB south of Dabie (Ratschbacher et al.,2000)and to the AFT ages from the southern Qinling (80–128Ma)(Hu et al.unpublished data)to the west.All the apparent ages are significantly younger than those of the UHP metamorphism,and subsequent plutonism and reheating events,in the Early Cretaceous,strongly suggesting total annealing and resetting of the AFT clocks followed by later cooling (see Section 6).In this paper we present track length data for 34samples (i.e.,where 20or more track lengths were measured)(see Fig.3and Table 1).Mean track lengths for these samples in the UHP metamorphic belt range between 11.1μm and 13.2μm (TB-21)(Table 1).Relative to length data presented by Grimmer et al.(2002),a greater number of shorter lengths were measured in samples reported here.The track-length distributions presented in Fig.3are rather broad,negatively skewed with a tail of short tracks and show a relatively small number of tracks with lengths >14μm.These features are broadly indicative of slow cooling through the partial annealing zone (PAZ,equivalent to the partial stability zone of Wagner,1972).5.2.Relationship between AFT age,track length,elevation and chemical compositionSamples with either relatively young or old apparent AFT ages typically have longer mean track lengths than samples with intermediate ages (Gleadow et al.,1986;Green et al.,1989).This interpretation is supportedbyFig.2.Histogram of apparent AFT ages analysed from different sub-tectonic units.Fig.3.Apparent AFT ages,with histograms showing distribution of confined track lengths and radial plots for samples collected from:(a)the NHFB and NOU and (b)the HP –UHP and the YFTB.In radial plots (Galbraith,1990),the slope of a straight line from the origin (0)passing through an individual grain age is equivalent to the fission-track age read off the radial scale around the perimeter of the plot.The X value and the %relative error are a measure of the precision of each grain age.The further a point plots to the right of the origin,the more precise the individual grain age measurement.If all grains belong to a single age population then they should scatter within the ±2σage range about the central age (outlined by the shaded horizontal rectangle)which is read by extrapolating a straight line from the origin (0)at the left of the plot to intercept the radial scale around the perimeter of the plot shown on the Y axis.416S.Hu et al./Tectonophysics 420(2006)409–429417 S.Hu et al./Tectonophysics420(2006)409–429the relationships between the AFT age and mean track length,as well as AFT age and the standard deviation of track length distributions from the Tongbai –Dabie belt samples (Fig.4a,b).Samples with apparent AFT ages between ∼40and 50Ma and ∼70and 80Ma tend to have the longest mean lengths (>12.5μm)and the narrowest length distributions,whereas for ‘intermedi-ate ’AFT ages (between ∼50and 70Ma)samples tend to have shorter mean lengths and broader length distribu-tions.Such patterns are indicative of thermal annealing of fission tracks at elevated paleotemperatures,affecting the suite of samples to varying degrees within a common style of thermal history.The relationships observed can therefore result from the episodic cooling of a suite of rocks from various paleotemperatures,at approximately the same time.At the extreme ends of the trends shown,the mean track lengths are longer (but still <14μm)and the standard deviations relatively low.Following initial cooling of rocks from elevated paleotempera-tures >110°C,some rocks may remain at tempera-tures <60°C and retain long mean track lengths and narrow distributions.In rocks remaining at tempera-tures between ∼60and 110°C,tracks that form are progressively annealed and the track length distribu-tion broadens.In rocks remaining at paleotempera-tures >110°C following the initial episode of cooling,all tracks formed will be totally annealed and only tracks produced after subsequent cooling will be preserved (i.e.,the young AFT ages of ∼40to 50Ma).Thus,the track-length distributions will be narrow with long mean lengths,and the fission-track age will closely approximate the time of cooling.For the intermediate samples on the trend however,the AFT data show two track length components:a shortened component recording exposure to elevated paleotemperatures between ∼60and 110°C follow-ing initial cooling <110°C,and a longercomponent,Fig.4.Boomerang plots showing AFT data from across the Tongbai –Dabie UHP metamorphic belt.Plots shown are:(a)fission-track age (±2σ)versus mean confined horizontal fission-track length (±2σ),(b)fission-track age (±2σ)versus standard deviation of confined horizontal fission-track-length distribution,(c)fission-track age (±2σ)versus elevation,and (d)apatite chlorine weight percent versus fission-track age (±2σ).For plots showing mean track length (a and b),only samples with 20or more measured confined tracks are plotted.Trends indicated by stippled bands are discussed in text.418S.Hu et al./Tectonophysics 420(2006)409–429。
locon训练参数在locon训练过程中，需要调整和优化的参数有很多，以下是一些重要的参数：1. batch_size：这是指训练过程中每次迭代使用的样本数量。
2. text_encoder_lr：这是文本编码器的学习率。
3. scheduler：这是优化器的调度器类型，它决定了如何调整学习率。
4. cycle：在某些训练策略中，例如周期性学习率调整策略，这个参数很重要。
5. optimizer_type：这是优化器的类型，如SGD、Adam等。
6. shuffle_captions：这是指在训练过程中是否随机打乱图片和对应描述的顺序。
7. keep_tokens：在某些预处理步骤中，可能需要保留某些特定的标记或令牌。
8. unet_only / text_only：这些参数决定模型的结构，unet_only表示只使用U-Net结构，text_only表示只使用文本编码器。
9. noise_offset：在数据增强中，这个参数可以用来控制噪声的程度。
10. persistent_data_loader_workers：这是数据加载器的线程数，可以影响数据加载的速度。
11. max_data_loader_n_workers：数据加载器的工作进程数。
12. mixed_precision：这是指是否使用混合精度训练，可以加速训练并减少显存使用。
13. prior_loss_weight：在某些损失函数中，这个参数可以用来调整先验损失和真实损失的权重。
14. gradient_checkpointing：这个参数决定是否使用梯度检查点，可以加速训练并减少显存使用。
15. --xformers / --mem_eff_attn：这些是特定于实现的参数，可能与模型的内部实现有关。
16. cache_latents：是否缓存中间表示（latents）以提高性能。
17. enable_bucket：是否启用桶排序来加速数据加载。
