MIT-SCIENCE-Lectures-sheep_brain_dissection_preparation
A neuroscientist reveals how to think differently翻译
A neuroscientist reveals how to think differently一位神经学家揭示了如何用不同的方式思考In the last decade a revolution has occurred in the way that scientists think about the brain. We now know that the decisions humans make can be traced to the firing patterns of neuronsin specific parts of the brain. These discoveries have led to the field known as macroeconomics, which studies the brain's secrets to success in an economic environment that demands innovation and being able to do things differently from competitors. A brain that can do this is an iconoclastic one. Briefly, an iconoclast is a person who does something that others say can't be done.在过去的十年里，科学家们对大脑的思考方式发生了一场革命。
我们现在知道，人类所做的决定可以追溯到大脑神经系统蛋白特定部位的激发模式。
这些发现导致了一个被称为神经科学的领域，这个领域研究了大脑在一个需要创新的经济环境中成功的秘密，并且能够以不同于竞争对手的方式做事。
能做到这一点的大脑是典型的。
简而言之，偶像崇拜者就是做别人说不能做的事的人。
大数据算法与模型考试 选择题 60题
1. 在大数据处理中，MapReduce是一种常用的计算模型，它主要由哪两个阶段组成？A. Map和FilterB. Reduce和SortC. Map和ReduceD. Filter和Reduce2. 下列哪个不是大数据的5V特征之一？A. VolumeB. VelocityC. VarietyD. Visibility3. 在数据挖掘中，K-means算法属于哪一类算法？A. 分类算法B. 聚类算法C. 关联规则算法D. 回归算法4. 下列哪个工具不是用于大数据处理的？A. HadoopB. SparkC. ExcelD. Hive5. 在机器学习中，过拟合是指模型在训练数据上表现良好，但在新数据上表现不佳。
下列哪个方法可以减少过拟合？A. 增加数据量B. 减少特征数量C. 增加模型复杂度D. 减少训练次数6. 下列哪个算法是基于决策树的集成学习方法？A. K-NNB. Random ForestC. SVMD. Naive Bayes7. 在大数据分析中，ETL代表什么？A. Extract, Transform, LoadB. Encode, Test, LoadC. Extract, Transfer, LinkD. Encode, Transform, Link8. 下列哪个不是NoSQL数据库的类型？A. 键值存储B. 文档存储C. 关系数据库D. 图形数据库9. 在数据预处理中，数据清洗的主要目的是什么？A. 增加数据量B. 减少数据量C. 提高数据质量D. 降低数据质量10. 下列哪个算法是用于推荐系统的？A. AprioriB. PageRankC. Collaborative FilteringD. K-means11. 在大数据环境中，HDFS是哪个框架的文件系统？A. HadoopB. SparkC. HiveD. MongoDB12. 下列哪个不是大数据分析的步骤？A. 数据收集B. 数据存储C. 数据加密D. 数据分析13. 在机器学习中，监督学习与非监督学习的主要区别是什么？A. 是否有标签数据B. 是否使用神经网络C. 是否使用决策树D. 是否使用回归分析14. 下列哪个算法是用于异常检测的？A. PCAB. SVMC. K-NND. DBSCAN15. 在大数据处理中，流处理与批处理的主要区别是什么？A. 数据处理的速度B. 数据处理的量C. 数据处理的类型D. 数据处理的频率16. 下列哪个不是大数据技术的优势？A. 提高数据处理速度B. 降低数据存储成本C. 减少数据分析的准确性D. 增强数据分析的能力17. 在数据挖掘中，关联规则挖掘的主要目的是什么？A. 发现数据中的模式B. 预测数据的趋势C. 分类数据D. 聚类数据18. 下列哪个不是数据仓库的特征？A. 面向主题B. 集成性C. 时变性D. 实时性19. 在大数据分析中，OLAP代表什么？A. Online Analytical ProcessingB. Offline Analytical ProcessingC. Online Application ProcessingD. Offline Application Processing20. 下列哪个算法是用于文本挖掘的？A. TF-IDFB. K-meansC. SVMD. Random Forest21. 在大数据环境中，Spark与Hadoop的主要区别是什么？A. 数据处理速度B. 数据存储方式C. 数据处理模型D. 数据分析工具22. 下列哪个不是数据可视化的工具？A. TableauB. Power BIC. ExcelD. Hadoop23. 在机器学习中，特征选择的主要目的是什么？A. 增加模型复杂度B. 减少数据量C. 提高模型性能D. 降低数据质量24. 下列哪个算法是用于时间序列分析的？A. ARIMAB. K-NNC. SVMD. Random Forest25. 在大数据处理中，数据湖与数据仓库的主要区别是什么？A. 数据存储方式B. 数据处理速度C. 数据分析工具D. 数据处理模型26. 下列哪个不是大数据分析的应用领域？A. 金融B. 医疗C. 教育D. 娱乐27. 在数据挖掘中，分类与回归的主要区别是什么？A. 输出类型B. 输入类型C. 算法类型D. 数据类型28. 下列哪个不是大数据技术的挑战？A. 数据安全B. 数据隐私C. 数据质量D. 数据简单性29. 在大数据分析中，数据治理的主要目的是什么？A. 提高数据质量B. 降低数据成本C. 增加数据量D. 减少数据类型30. 下列哪个算法是用于图像识别的？A. CNNB. K-meansC. SVMD. Random Forest31. 在大数据环境中，数据脱敏的主要目的是什么？A. 提高数据质量B. 保护数据隐私C. 增加数据量32. 下列哪个不是大数据分析的工具？A. RB. PythonC. JavaD. Excel33. 在机器学习中，交叉验证的主要目的是什么？A. 提高模型性能B. 减少数据量C. 增加数据类型D. 降低数据质量34. 下列哪个算法是用于序列挖掘的？A. AprioriB. PageRankC. Collaborative FilteringD. K-means35. 在大数据处理中，数据集成的主要目的是什么？A. 提高数据质量B. 降低数据成本C. 增加数据量D. 减少数据类型36. 下列哪个不是大数据技术的应用场景？A. 智能推荐B. 风险管理C. 数据加密D. 预测分析37. 在数据挖掘中，频繁项集挖掘的主要目的是什么？A. 发现数据中的模式B. 预测数据的趋势C. 分类数据D. 聚类数据38. 下列哪个不是数据仓库的设计原则？A. 面向主题B. 集成性C. 时变性D. 实时性39. 在大数据分析中，数据湖的主要优势是什么？A. 数据存储方式C. 数据分析工具D. 数据处理模型40. 下列哪个算法是用于社交网络分析的？A. PageRankB. K-meansC. SVMD. Random Forest41. 在大数据环境中，数据质量管理的主要目的是什么？A. 提高数据质量B. 降低数据成本C. 增加数据量D. 减少数据类型42. 下列哪个不是大数据分析的步骤？A. 数据收集B. 数据存储C. 数据加密D. 数据分析43. 在机器学习中，模型评估的主要目的是什么？A. 提高模型性能B. 减少数据量C. 增加数据类型D. 降低数据质量44. 下列哪个算法是用于推荐系统的？A. AprioriB. PageRankC. Collaborative FilteringD. K-means45. 在大数据处理中，数据清洗的主要目的是什么？A. 提高数据质量B. 降低数据成本C. 增加数据量D. 减少数据类型46. 下列哪个不是大数据技术的优势？A. 提高数据处理速度B. 降低数据存储成本C. 减少数据分析的准确性D. 增强数据分析的能力47. 在数据挖掘中，关联规则挖掘的主要目的是什么？A. 发现数据中的模式B. 预测数据的趋势C. 分类数据D. 聚类数据48. 下列哪个不是数据仓库的特征？A. 面向主题B. 集成性C. 时变性D. 实时性49. 在大数据分析中，OLAP代表什么？A. Online Analytical ProcessingB. Offline Analytical ProcessingC. Online Application ProcessingD. Offline Application Processing50. 下列哪个算法是用于文本挖掘的？A. TF-IDFB. K-meansC. SVMD. Random Forest51. 在大数据环境中，Spark与Hadoop的主要区别是什么？A. 数据处理速度B. 数据存储方式C. 数据处理模型D. 数据分析工具52. 下列哪个不是数据可视化的工具？A. TableauB. Power BIC. ExcelD. Hadoop53. 在机器学习中，特征选择的主要目的是什么？A. 增加模型复杂度B. 减少数据量C. 提高模型性能D. 降低数据质量54. 下列哪个算法是用于时间序列分析的？A. ARIMAB. K-NNC. SVMD. Random Forest55. 在大数据处理中，数据湖与数据仓库的主要区别是什么？A. 数据存储方式B. 数据处理速度C. 数据分析工具D. 数据处理模型56. 下列哪个不是大数据分析的应用领域？A. 金融B. 医疗C. 教育D. 娱乐57. 在数据挖掘中，分类与回归的主要区别是什么？A. 输出类型B. 输入类型C. 算法类型D. 数据类型58. 下列哪个不是大数据技术的挑战？A. 数据安全B. 数据隐私C. 数据质量D. 数据简单性59. 在大数据分析中，数据治理的主要目的是什么？A. 提高数据质量B. 降低数据成本C. 增加数据量D. 减少数据类型60. 下列哪个算法是用于图像识别的？A. CNNB. K-meansC. SVMD. Random Forest答案部分1. C2. D3. B4. C5. B6. B7. A9. C10. C11. A12. C13. A14. A15. D16. C17. A18. D19. A20. A21. A22. D23. C24. A25. A26. D27. A28. D29. A30. A31. B32. C33. A34. A35. A36. C37. A38. D39. A40. A41. A42. C43. A44. C45. A46. C47. A48. D49. A50. A51. A52. D53. C54. A55. A56. D57. A59. A60. A。
专题05 阅读理解D篇(2024年新课标I卷) (专家评价+三年真题+满分策略+多维变式) 原卷版
《2024年高考英语新课标卷真题深度解析与考后提升》专题05阅读理解D篇（新课标I卷）原卷版(专家评价+全文翻译+三年真题+词汇变式+满分策略+话题变式)目录一、原题呈现P2二、答案解析P3三、专家评价P3四、全文翻译P3五、词汇变式P4(一)考纲词汇词形转换P4(二)考纲词汇识词知意P4(三)高频短语积少成多P5(四)阅读理解单句填空变式P5(五)长难句分析P6六、三年真题P7(一)2023年新课标I卷阅读理解D篇P7(二)2022年新课标I卷阅读理解D篇P8(三)2021年新课标I卷阅读理解D篇P9七、满分策略（阅读理解说明文）P10八、阅读理解变式P12 变式一：生物多样性研究、发现、进展6篇P12变式二：阅读理解D篇35题变式（科普研究建议类）6篇P20一原题呈现阅读理解D篇关键词: 说明文;人与社会;社会科学研究方法研究;生物多样性; 科学探究精神;科学素养In the race to document the species on Earth before they go extinct, researchers and citizen scientists have collected billions of records. Today, most records of biodiversity are often in the form of photos, videos, and other digital records. Though they are useful for detecting shifts in the number and variety of species in an area, a new Stanford study has found that this type of record is not perfect.“With the rise of technology it is easy for people to make observation s of different species with the aid of a mobile application,” said Barnabas Daru, who is lead author of the study and assistant professor of biology in the Stanford School of Humanities and Sciences. “These observations now outnumber the primary data that comes from physical specimens(标本), and since we are increasingly using observational data to investigate how species are responding to global change, I wanted to know: Are they usable?”Using a global dataset of 1.9 billion records of plants, insects, birds, and animals, Daru and his team tested how well these data represent actual global biodiversity patterns.“We were particularly interested in exploring the aspects of sampling that tend to bias (使有偏差) data, like the greater likelihood of a citizen scientist to take a picture of a flowering plant instead of the grass right next to it,” said Daru.Their study revealed that the large number of observation-only records did not lead to better global coverage. Moreover, these data are biased and favor certain regions, time periods, and species. This makes sense because the people who get observational biodiversity data on mobile devices are often citizen scientists recording their encounters with species in areas nearby. These data are also biased toward certain species with attractive or eye-catching features.What can we do with the imperfect datasets of biodiversity?“Quite a lot,” Daru explained. “Biodiversity apps can use our study results to inform users of oversampled areas and lead them to places – and even species – that are not w ell-sampled. To improve the quality of observational data, biodiversity apps can also encourage users to have an expert confirm the identification of their uploaded image.”32. What do we know about the records of species collected now?A. They are becoming outdated.B. They are mostly in electronic form.C. They are limited in number.D. They are used for public exhibition.33. What does Daru’s study focus on?A. Threatened species.B. Physical specimens.C. Observational data.D. Mobile applications.34. What has led to the biases according to the study?A. Mistakes in data analysis.B. Poor quality of uploaded pictures.C. Improper way of sampling.D. Unreliable data collection devices.35. What is Daru’s suggestion for biodiversity apps?A. Review data from certain areas.B. Hire experts to check the records.C. Confirm the identity of the users.D. Give guidance to citizen scientists.二答案解析三专家评价考查关键能力，促进思维品质发展2024年高考英语全国卷继续加强内容和形式创新，优化试题设问角度和方式，增强试题的开放性和灵活性，引导学生进行独立思考和判断，培养逻辑思维能力、批判思维能力和创新思维能力。
提高脑力的方法英语演讲作文
提高脑力的方法英语演讲作文英文回答：There are many things you can do to improve your brainpower. Some of the most effective methods include:Exercise regularly. Exercise is not only good for your physical health, but it can also benefit your brain. Studies have shown that exercise can increase blood flow to the brain, improve memory, and boost cognitive function.Eat a healthy diet. Eating a healthy diet is essential for overall health, including brain health. Eating plenty of fruits, vegetables, and whole grains can help to improve memory, attention, and focus.Get enough sleep. Sleep is essential for brain function. When you sleep, your brain consolidates memories and removes waste products. Getting enough sleep can help to improve memory, learning, and decision-making.Challenge your brain. Learning new things, solving puzzles, and playing games can help to keep your brain active and engaged. Challenging your brain can help to improve memory, problem-solving skills, and creativity.Socialize. Spending time with friends and family can help to improve your brain health. Socializing can help to reduce stress, improve mood, and boost cognitive function.Manage stress. Stress can take a toll on your brain health. Managing stress can help to improve memory, learning, and decision-making. There are many different ways to manage stress, such as exercise, yoga, meditation, and spending time in nature.Take care of your mental health. Mental health is just as important as physical health. Taking care of your mental health can help to improve your brain health. If you are struggling with mental health issues, seek professional help.中文回答：提高脑力的方法。
MIT《贝叶斯深度学习研究综述》A Survey on Bayesian Deep Learning
A Survey on Bayesian Deep LearningHAO WANG,Massachusetts Institute of Technology,USADIT-YAN YEUNG,Hong Kong University of Science and Technology,Hong KongA comprehensive artificial intelligence system needs to not only perceive the environment with different‘senses’(e.g.,seeing and hearing)but also infer the world’s conditional(or even causal)relations and corresponding uncertainty.The past decade has seen major advances in many perception tasks such as visual object recognition and speech recognition using deep learning models.For higher-level inference,however,probabilistic graphical models with their Bayesian nature are still more powerful and flexible.In recent years,Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models1.In this general framework,the perception of text or images using deep learning can boost the performance of higher-level inference and in turn,the feedback from the inference process is able to enhance the perception of text or images.This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems,topic models, control,etc.Besides,we also discuss the relationship and differences between Bayesian deep learning and other related topics such as Bayesian treatment of neural networks.CCS Concepts:•Mathematics of computing→Probabilistic representations;•Information systems→Data mining;•Computing methodologies→Neural networks.Additional Key Words and Phrases:Deep Learning,Bayesian Networks,Probabilistic Graphical Models,Generative ModelsACM Reference Format:Hao Wang and Dit-Yan Yeung.2020.A Survey on Bayesian Deep Learning.In ACM Computing Surveys.ACM,New York,NY,USA, 35pages.https:///xx.xxxx/xxxxxxx.xxxxxxx1INTRODUCTIONOver the past decade,deep learning has achieved significant success in many popular perception tasks including visual object recognition,text understanding,and speech recognition.These tasks correspond to artificial intelligence(AI) systems’ability to see,read,and hear,respectively,and they are undoubtedly indispensable for AI to effectively perceive the environment.However,in order to build a practical and comprehensive AI system,simply being able to perceive is far from sufficient.It should,above all,possess the ability of thinking.A typical example is medical diagnosis,which goes far beyond simple perception:besides seeing visible symptoms(or medical images from CT)and hearing descriptions from patients,a doctor also has to look for relations among all the symptoms and preferably infer their corresponding etiology.Only after that can the doctor provide medical advice for the patients.In this example,although the abilities of seeing and hearing allow the doctor to acquire information from the patients,it is the thinking part that defines a doctor.Specifically,the ability of thinking here could involve identifying conditional dependencies,causal inference,logic deduction,and dealing with uncertainty,which are apparently beyond 1See a curated and updating list of papers related to Bayesian deep learning at https:///js05212/BayesianDeepLearning-Survey.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.Copyrights for components of this work owned by others than ACM must be honored.Abstracting with credit is permitted.To copy otherwise,or republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.Request permissions from permissions@.©2020Association for Computing Machinery.Manuscript submitted to ACM1CSUR,March,2020,New York,NY Hao Wang and Dit-Yan Yeungthe capability of conventional deep learning methods.Fortunately,another machine learning paradigm,probabilistic graphical models(PGM),excels at probabilistic or causal inference and at dealing with uncertainty.The problem is that PGM is not as good as deep learning models at perception tasks,which usually involve large-scale and high-dimensional signals(e.g.,images and videos).To address this problem,it is therefore a natural choice to unify deep learning and PGM within a principled probabilistic framework,which we call Bayesian deep learning(BDL)in this paper.In the example above,the perception task involves perceiving the patient’s symptoms(e.g.,by seeing medical images), while the inference task involves handling conditional dependencies,causal inference,logic deduction,and uncertainty. With the principled integration in Bayesian deep learning,the perception task and inference task are regarded as a whole and can benefit from each other.Concretely,being able to see the medical image could help with the doctor’s diagnosis and inference.On the other hand,diagnosis and inference can,in turn,help understand the medical image. Suppose the doctor may not be sure about what a dark spot in a medical image is,but if she is able to infer the etiology of the symptoms and disease,it can help her better decide whether the dark spot is a tumor or not.Take recommender systems[1,70,71,92,121]as another example.A highly accurate recommender system requires (1)thorough understanding of item content(e.g.,content in documents and movies)[85],(2)careful analysis of users’profiles/preferences[126,130,134],and(3)proper evaluation of similarity among users[3,12,46,109].Deep learning with its ability to efficiently process dense high-dimensional data such as movie content is good at the first subtask, while PGM specializing in modeling conditional dependencies among users,items,and ratings(see Figure7as an example,where u,v,and R are user latent vectors,item latent vectors,and ratings,respectively)excels at the other two.Hence unifying them two in a single principled probabilistic framework gets us the best of both worlds.Such integration also comes with additional benefit that uncertainty in the recommendation process is handled elegantly. What’s more,one can also derive Bayesian treatments for concrete models,leading to more robust predictions[68,121].As a third example,consider controlling a complex dynamical system according to the live video stream received from a camera.This problem can be transformed into iteratively performing two tasks,perception from raw images and control based on dynamic models.The perception task of processing raw images can be handled by deep learning while the control task usually needs more sophisticated models such as hidden Markov models and Kalman filters[35,74]. The feedback loop is then completed by the fact that actions chosen by the control model can affect the received video stream in turn.To enable an effective iterative process between the perception task and the control task,we need information to flow back and forth between them.The perception component would be the basis on which the control component estimates its states and the control component with a dynamic model built in would be able to predict the future trajectory(images).Therefore Bayesian deep learning is a suitable choice[125]for this problem.Note that similar to the recommender system example,both noise from raw images and uncertainty in the control process can be naturally dealt with under such a probabilistic framework.The above examples demonstrate BDL’s major advantages as a principled way of unifying deep learning and PGM: information exchange between the perception task and the inference task,conditional dependencies on high-dimensional data,and effective modeling of uncertainty.In terms of uncertainty,it is worth noting that when BDL is applied to complex tasks,there are three kinds of parameter uncertainty that need to be taken into account:(1)Uncertainty on the neural network parameters.(2)Uncertainty on the task-specific parameters.(3)Uncertainty of exchanging information between the perception component and the task-specific component. By representing the unknown parameters using distributions instead of point estimates,BDL offers a promising framework to handle these three kinds of uncertainty in a unified way.It is worth noting that the third uncertainty2A Survey on Bayesian Deep Learning CSUR,March,2020,New York,NYcould only be handled under a unified framework like BDL;training the perception component and the task-specific component separately is equivalent to assuming no uncertainty when exchanging information between them two.Note that neural networks are usually over-parameterized and therefore pose additional challenges in efficiently handling the uncertainty in such a large parameter space.On the other hand,graphical models are often more concise and have smaller parameter space,providing better interpretability.Besides the advantages above,another benefit comes from the implicit regularization built in BDL.By imposing a prior on hidden units,parameters defining a neural network,or the model parameters specifying the conditional dependencies,BDL can to some degree avoid overfitting,especially when we have insufficient ually,a BDL model consists of two components,a perception component that is a Bayesian formulation of a certain type of neural networks and a task-specific component that describes the relationship among different hidden or observed variables using PGM.Regularization is crucial for them both.Neural networks are usually heavily over-parameterized and therefore needs to be regularized properly.Regularization techniques such as weight decay and dropout[103]are shown to be effective in improving performance of neural networks and they both have Bayesian interpretations[22]. In terms of the task-specific component,expert knowledge or prior information,as a kind of regularization,can be incorporated into the model through the prior we imposed to guide the model when data are scarce.There are also challenges when applying BDL to real-world tasks.(1)First,it is nontrivial to design an efficient Bayesian formulation of neural networks with reasonable time complexity.This line of work is pioneered by[42,72,80], but it has not been widely adopted due to its lack of scalability.Fortunately,some recent advances in this direction[2,9, 31,39,58,119,121]seem to shed light2on the practical adoption of Bayesian neural network3.(2)The second challenge is to ensure efficient and effective information exchange between the perception component and the task-specific component.Ideally both the first-order and second-order information(e.g.,the mean and the variance)should be able to flow back and forth between the two components.A natural way is to represent the perception component as a PGM and seamlessly connect it to the task-specific PGM,as done in[24,118,121].This survey provides a comprehensive overview of BDL with concrete models for various applications.The rest of the survey is organized as follows:In Section2,we provide a review of some basic deep learning models.Section3 covers the main concepts and techniques for PGM.These two sections serve as the preliminaries for BDL,and the next section,Section4,demonstrates the rationale for the unified BDL framework and details various choices for implementing its perception component and task-specific component.Section5reviews the BDL models applied to various areas such as recommender systems,topic models,and control,showcasing how BDL works in supervised learning, unsupervised learning,and general representation learning,respectively.Section6discusses some future research issues and concludes the paper.2DEEP LEARNINGDeep learning normally refers to neural networks with more than two layers.To better understand deep learning, here we start with the simplest type of neural networks,multilayer perceptrons(MLP),as an example to show how conventional deep learning works.After that,we will review several other types of deep learning models based on MLP.2In summary,reduction in time complexity can be achieved via expectation propagation[39],the reparameterization trick[9,58],probabilistic formulation of neural networks with maximum a posteriori estimates[121],approximate variational inference with natural-parameter networks[119],knowledge distillation[2],etc.We refer readers to[119]for a detailed overview.3Here we refer to the Bayesian treatment of neural networks as Bayesian neural networks.The other term,Bayesian deep learning,is retained to refer to complex Bayesian models with both a perception component and a task-specific component.See Section4.1for a detailed discussion.3York,NY Hao Wang and Dit-Yan Yeung 01234cFig.1.Left:A2-layer SDAE with L=4.Right:A convolutional layer with4input feature maps and2output feature maps.2.1Multilayer PerceptronsEssentially a multilayer perceptron is a sequence of parametric nonlinear transformations.Suppose we want to train amultilayer perceptron to perform a regression task which maps a vector of M dimensions to a vector of D dimensions.We denote the input as a matrix X0(0means it is the0-th layer of the perceptron).The j-th row of X0,denoted as X0,j∗,is an M-dimensional vector representing one data point.The target(the output we want to fit)is denoted as Y.SimilarlyY j∗denotes a D-dimensional row vector.The problem of learning an L-layer multilayer perceptron can be formulatedas the following optimization problem:min {W l},{b l}∥X L−Y∥F+λl∥W l∥2Fsubject to X l=σ(X l−1W l+b l),l=1,...,L−1X L=X L−1W L+b L,whereσ(·)is an element-wise sigmoid function for a matrix andσ(x)=11+exp(−x).∥·∥F denotes the Frobenius norm. The purpose of imposingσ(·)is to allow nonlinear transformation.Normally other transformations like tanh(x)and max(0,x)can be used as alternatives of the sigmoid function.Here X l(l=1,2,...,L−1)is the hidden units.As we can see,X L can be easily computed once X0,W l,and b l are given.Since X0is given as input,one only needs to learn W l and b l ually this is done using backpropagation and stochastic gradient descent(SGD).The key is to compute the gradients of the objective function with respect to W l and b l.Denoting the value of the objective function as E,one can compute the gradients using the chain rule as:∂E ∂X L =2(X L−Y),∂E∂X l=(∂E∂X l+1◦X l+1◦(1−X l+1))W l+1,∂E ∂W l =X T l−1(∂E∂X l◦X l◦(1−X l)),∂E∂b l=mean(∂E∂X l◦X l◦(1−X l),1),where l=1,...,L and the regularization terms are omitted.◦denotes the element-wise product and mean(·,1)is the matlab operation on matrices.In practice,we only use a small part of the data(e.g.,128data points)to compute the gradients for each update.This is called stochastic gradient descent.As we can see,in conventional deep learning models,only W l and b l are free parameters,which we will update in each iteration of the optimization.X l is not a free parameter since it can be computed exactly if W l and b l are given.4A Survey on Bayesian Deep Learning CSUR,March,2020,New York,NY2.2AutoencodersAn autoencoder(AE)is a feedforward neural network to encode the input into a more compact representation and reconstruct the input with the learned representation.In its simplest form,an autoencoder is no more than a multilayer perceptron with a bottleneck layer(a layer with a small number of hidden units)in the middle.The idea of autoencoders has been around for decades[10,29,43,63]and abundant variants of autoencoders have been proposed to enhance representation learning including sparse AE[88],contrastive AE[93],and denoising AE[111].For more details,please refer to a nice recent book on deep learning[29].Here we introduce a kind of multilayer denoising AE,known as stacked denoising autoencoders(SDAE),both as an example of AE variants and as background for its applications on BDL-based recommender systems in Section4.SDAE[111]is a feedforward neural network for learning representations(encoding)of the input data by learning to predict the clean input itself in the output,as shown in Figure1(left).The hidden layer in the middle,i.e.,X2in the figure,can be constrained to be a bottleneck to learn compact representations.The difference between traditional AE and SDAE is that the input layer X0is a corrupted version of the clean input data X c.Essentially an SDAE solves the following optimization problem:min {W l},{b l}∥X c−X L∥2F+λl∥W l∥2Fsubject to X l=σ(X l−1W l+b l),l=1,...,L−1X L=X L−1W L+b L,whereλis a regularization parameter.Here SDAE can be regarded as a multilayer perceptron for regression tasks described in the previous section.The input X0of the MLP is the corrupted version of the data and the target Y is the clean version of the data X c.For example,X c can be the raw data matrix,and we can randomly set30%of the entries in X c to0and get X0.In a nutshell,SDAE learns a neural network that takes the noisy data as input and recovers the clean data in the last layer.This is what‘denoising’in the name means.Normally,the output of the middle layer,i.e., X2in Figure1(left),would be used to compactly represent the data.2.3Convolutional Neural NetworksConvolutional neural networks(CNN)can be viewed as another variant of MLP.Different from AE,which is initially designed to perform dimensionality reduction,CNN is biologically inspired.According to[53],two types of cells have been identified in the cat’s visual cortex.One is simple cells that respond maximally to specific patterns within their receptive field,and the other is complex cells with larger receptive field that are considered locally invariant to positions of patterns.Inspired by these findings,the two key concepts in CNN are then developed:convolution and max-pooling.Convolution:In CNN,a feature map is the result of the convolution of the input and a linear filter,followed by some element-wise nonlinear transformation.The input here can be the raw image or the feature map from the previous layer.Specifically,with input X,weights W k,bias b k,the k-th feature map H k can be obtained as follows:H k ij=tanh((W k∗X)ij+b k).Note that in the equation above we assume one single input feature map and multiple output feature maps.In practice, CNN often has multiple input feature maps as well due to its deep structure.A convolutional layer with4input feature maps and2output feature maps is shown in Figure1(right).52020,New York,NY Hao Wang and Dit-Yan Yeung Fig.2.Left:A conventional feedforward neural network with one hidden layer,where x is the input,z is the hidden layer,and o is the output,W and V are the corresponding weights(biases are omitted here).Middle:A recurrent neural network with input{x t}T t=1, hidden states{h t}T t=1,and output{o t}T t=1.Right:An unrolled RNN which is equivalent to the one in Figure2(middle).Here each node(e.g.,x1,h1,or o1)is associated with one particular time step.Max-Pooling:Traditionally,a convolutional layer in CNN is followed by a max-pooling layer,which can be seen as a type of nonlinear downsampling.The operation of max-pooling is simple.For example,if we have a feature map of size6×9,the result of max-pooling with a3×3region would be a downsampled feature map of size2×3.Each entry of the downsampled feature map is the maximum value of the corresponding3×3region in the6×9feature map. Max-pooling layers can not only reduce computational cost by ignoring the non-maximal entries but also provide local translation invariance.Putting it all together:Usually to form a complete and working CNN,the input would alternate between convolutional layers and max-pooling layers before going into an MLP for tasks such as classification or regression. One classic example is the LeNet-5[64],which alternates between2convolutional layers and2max-pooling layers before going into a fully connected MLP for target tasks.2.4Recurrent Neural NetworkWhen reading an article,one normally takes in one word at a time and try to understand the current word based on previous words.This is a recurrent process that needs short-term memory.Unfortunately conventional feedforward neural networks like the one shown in Figure2(left)fail to do so.For example,imagine we want to constantly predict the next word as we read an article.Since the feedforward network only computes the output o as V q(Wx),where the function q(·)denotes element-wise nonlinear transformation,it is unclear how the network could naturally model the sequence of words to predict the next word.2.4.1Vanilla Recurrent Neural Network.To solve the problem,we need a recurrent neural network[29]instead of a feedforward one.As shown in Figure2(middle),the computation of the current hidden states h t depends on the current input x t(e.g.,the t-th word)and the previous hidden states h t−1.This is why there is a loop in the RNN.It is this loop that enables short-term memory in RNNs.The h t in the RNN represents what the network knows so far at the t-th time step.To see the computation more clearly,we can unroll the loop and represent the RNN as in Figure2(right).If we use hyperbolic tangent nonlinearity(tanh),the computation of output o t will be as follows:a t=Wh t−1+Yx t+b,h t=tanh(a t),o t=Vh t+c,where Y,W,and V denote the weight matrices for input-to-hidden,hidden-to-hidden,and hidden-to-output connections, respectively,and b and c are the corresponding biases.If the task is to classify the input data at each time step,we can6A Survey on Bayesian Deep Learning CSUR,March,2020,New York,NYFig.3.The encoder-decoder architecture involving two LSTMs.The encoder LSTM(in the left rectangle)encodes the sequence‘ABC’into a representation and the decoder LSTM(in the right rectangle)recovers the sequence from the representation.‘$’marks the end of a sentence.compute the classification probability as p t=softmax(o t)wheresoftmax(q)=exp(q)iexp(q i).Similar to feedforward networks,an RNN is trained with a generalized back-propagation algorithm called back-propagation through time(BPTT)[29].Essentially the gradients are computed through the unrolled network as shown in Figure2(right)with shared weights and biases for all time steps.2.4.2Gated Recurrent Neural Network.The problem with the vanilla RNN above is that the gradients propagated over many time steps are prone to vanish or explode,making the optimization notoriously difficult.In addition,the signal passing through the RNN decays exponentially,making it impossible to model long-term dependencies in long sequences.Imagine we want to predict the last word in the paragraph‘I have many books...I like reading’.In orderto get the answer,we need‘long-term memory’to retrieve information(the word‘books’)at the start of the text.To address this problem,the long short-term memory model(LSTM)is designed as a type of gated RNN to model and accumulate information over a relatively long duration.The intuition behind LSTM is that when processing a sequence consisting of several subsequences,it is sometimes useful for the neural network to summarize or forget the old states before moving on to process the next subsequence[29].Using t=1...T j to index the words in the sequence,the formulation of LSTM is as follows(we drop the item index j for notational simplicity):x t=W w e t,s t=h f t−1⊙s t−1+h i t−1⊙σ(Yx t−1+Wh t−1+b),(1)where x t is the word embedding of the t-th word,W w is a K W-by-S word embedding matrix,and e t is the1-of-S representation,⊙stands for the element-wise product operation between two vectors,σ(·)denotes the sigmoid function,s t is the cell state of the t-th word,and b,Y,and W denote the biases,input weights,and recurrent weights respectively. The forget gate units h f t and the input gate units h i t in Equation(1)can be computed using their corresponding weights and biases Y f,W f,Y i,W i,b f,and b i:h f t=σ(Y f x t+W f h t+b f),h i t=σ(Y i x t+W i h t+b i).The output depends on the output gate h o t which has its own weights and biases Y o,W o,and b o:h t=tanh(s t)⊙h o t−1,h o t=σ(Y o x t+W o h t+b o).Note that in the LSTM,information of the processed sequence is contained in the cell states s t and the output states h t, both of which are column vectors of length K W.Similar to[16,108],we can use the output state and cell state at the last time step(h Tj and s Tj)of the first LSTM asthe initial output state and cell state of the second LSTM.This way the two LSTMs can be concatenated to form an encoder-decoder architecture,as shown in Figure3.7CSUR,March,2020,New York,NY Hao Wang and Dit-Yan Yeung Fig.4.The probabilistic graphical model for LDA,J is the number of documents,D is the number of words in a document,and K is the number of topics.Note that there is a vast literature on deep learning and neural networks.The introduction in this section intends to serve only as the background of Bayesian deep learning.Readers are referred to[29]for a comprehensive survey and more details.3PROBABILISTIC GRAPHICAL MODELSProbabilistic Graphical Models(PGM)use diagrammatic representations to describe random variables and relationships among them.Similar to a graph that contains nodes(vertices)and links(edges),PGM has nodes to represent random variables and links to indicate probabilistic relationships among them.3.1ModelsThere are essentially two types of PGM,directed PGM(also known as Bayesian networks)and undirected PGM(also known as Markov random fields)[5].In this survey we mainly focus on directed PGM4.For details on undirected PGM, readers are referred to[5].A classic example of PGM would be latent Dirichlet allocation(LDA),which is used as a topic model to analyze the generation of words and topics in documents[8].Usually PGM comes with a graphical representation of the model and a generative process to depict the story of how the random variables are generated step by step.Figure4shows the graphical model for LDA and the corresponding generative process is as follows:•For each document j(j=1,2,...,J),(1)Draw topic proportionsθj∼Dirichlet(α).(2)For each word w jn of item(document)w j,(a)Draw topic assignment z jn∼Mult(θj).).(b)Draw word w jn∼Mult(βzjnThe generative process above provides the story of how the random variables are generated.In the graphical model in Figure4,the shaded node denotes observed variables while the others are latent variables(θand z)or parameters(αandβ).Once the model is defined,learning algorithms can be applied to automatically learn the latent variables and parameters.Due to its Bayesian nature,PGM such as LDA is easy to extend to incorporate other information or to perform other tasks.For example,following LDA,different variants of topic models have been proposed.[7,113]are proposed to incorporate temporal information,and[6]extends LDA by assuming correlations among topics.[44]extends LDA from the batch mode to the online setting,making it possible to process large datasets.On recommender systems, collaborative topic regression(CTR)[112]extends LDA to incorporate rating information and make recommendations. This model is then further extended to incorporate social information[89,115,116].4For convenience,PGM stands for directed PGM in this survey unless specified otherwise.8A Survey on Bayesian Deep Learning CSUR,March,2020,New York,NY Table1.Summary of BDL Models with Different Learning Algorithms(MAP:Maximum a Posteriori,VI:Variational Inference,Hybrid MC:Hybrid Monte Carlo)and Different Variance Types(ZV:Zero-Variance,HV:Hyper-Variance,LV:Learnable-Variance).Applications Models Variance ofΩh MAP VI Gibbs Sampling Hybrid MCRecommender Systems Collaborative Deep Learning(CDL)[121]HV✓Bayesian CDL[121]HV✓Marginalized CDL[66]LV✓Symmetric CDL[66]LV✓Collaborative Deep Ranking[131]HV✓Collaborative Knowledge Base Embedding[132]HV✓Collaborative Recurrent AE[122]HV✓Collaborative Variational Autoencoders[68]HV✓Topic Models Relational SDAE HV✓Deep Poisson Factor Analysis with Sigmoid Belief Networks[24]ZV✓✓Deep Poisson Factor Analysis with Restricted Boltzmann Machine[24]ZV✓✓Deep Latent Dirichlet Allocation[18]LV✓Dirichlet Belief Networks[133]LV✓Control Embed to Control[125]LV✓Deep Variational Bayes Filters[57]LV✓Probabilistic Recurrent State-Space Models[19]LV✓Deep Planning Networks[34]LV✓Link Prediction Relational Deep Learning[120]LV✓✓Graphite[32]LV✓Deep Generative Latent Feature Relational Model[75]LV✓NLP Sequence to Better Sequence[77]LV✓Quantifiable Sequence Editing[69]LV✓Computer Vision Asynchronous Temporal Fields[102]LV✓Attend,Infer,Repeat(AIR)[20]LV✓Fast AIR[105]LV✓Sequential AIR[60]LV✓Speech Factorized Hierarchical VAE[48]LV✓Scalable Factorized Hierarchical VAE[47]LV✓Gaussian Mixture Variational Autoencoders[49]LV✓Recurrent Poisson Process Units[51]LV✓✓Deep Graph Random Process[52]LV✓✓Time Series Forecasting DeepAR[21]LV✓DeepState[90]LV✓Spline Quantile Function RNN[27]LV✓DeepFactor[124]LV✓Health Care Deep Poisson Factor Models[38]LV✓Deep Markov Models[61]LV✓Black-Box False Discovery Rate[110]LV✓Bidirectional Inference Networks[117]LV✓3.2Inference and LearningStrictly speaking,the process of finding the parameters(e.g.,αandβin Figure4)is called learning and the process of finding the latent variables(e.g.,θand z in Figure4)given the parameters is called inference.However,given only the observed variables(e.g.w in Figure4),learning and inference are often ually the learning and inference of LDA would alternate between the updates of latent variables(which correspond to inference)and the updates of the parameters(which correspond to learning).Once the learning and inference of LDA is completed,one could obtain the learned parametersαandβ.If a new document comes,one can now fix the learnedαandβand then perform inference alone to find the topic proportionsθj of the new document.5Similar to LDA,various learning and inference algorithms are available for each PGM.Among them,the most cost-effective one is probably maximum a posteriori(MAP),which amounts to maximizing the posterior probability of the latent ing MAP,the learning process is equivalent to minimizing(or maximizing)an objective function with regularization.One famous example is the probabilistic matrix factorization(PMF)[96],where the learning of the graphical model is equivalent to factorizing a large matrix into two low-rank matrices with L2regularization.MAP,as efficient as it is,gives us only point estimates of latent variables(and parameters).In order to take the uncertainty into account and harness the full power of Bayesian models,one would have to resort to Bayesian treatments such as variational inference and Markov chain Monte Carlo(MCMC).For example,the original LDA uses variational5For convenience,we use‘learning’to represent both‘learning and inference’in the following text.9。
Science：科学家如何在小鼠大脑中植入虚假记忆
Science：科学家如何在小鼠大脑中植入虚假记忆设想你是一只老鼠，此时你被研究员关在一个小室内，处于崩溃边缘。
在这个小室内，你明显感觉到自己的脚遭受电击。
但你却不知道，科学家们已经通过摆弄你的大脑细胞而操控你的心理活动，从而对自己的过去形成另一种认知。
但是事实却是：在这个小室里，你从未受到电击。
这听起来像恐怖片，但它真的在实验情境下发生了。
这项研究也许对解读人类记忆有所启发。
科学家们声称通过操控动物编码记忆的大脑细胞，从而首次让动物形成错误记忆。
本周在《科学》期刊上刊登了他们的发现。
此外，研究人员指出参与形成虚假记忆与真实记忆的细胞活动是相似的。
这与事实相符：人们坚信从未发生过的虚假记忆是真实存在的。
“我们应该继续提醒大家：记忆有可能是不可靠的。
”利根川进，麻省理工学院神经电路遗传学中心主任、此项研究的资深作者。
这是日本埼玉县机构、剑桥和麻神理工的一项合作。
光控大脑参与实验的幸运老鼠体验了叫光遗传学的大脑探查技术。
这种技术用光来操控个体大脑细胞，由麻省理工学院Ed.博伊登教授和斯坦福大学卡尔.戴瑟罗斯教授共同发明。
对大脑的研究是全球试验室的研究热点，光遗传学是一种了解大脑的新型方法并能促成治疗不同脑部疾病的优化方法产生。
利根川进的团队首先识别了海马中的细胞。
其中，海马是大脑中海马形状的结构，主要负责编码特定经历的记忆。
之后，将这些富含信息的细胞做标记，从遗传学方面借助一种被称为紫红质通道蛋白的光敏蛋白改变这些细胞。
接着通过蓝光照射以激活这些细胞。
2012年发表于《自然》的一项研究，利根川进与同事使用此方法证实通过光可以激活老鼠的记忆。
他们将老鼠放入小室内并电击其足。
然后从遗传方面改变大脑细胞，这些细胞对老鼠在小室内遭受的电击产生反应。
当他们将老鼠置于不同的小室，老鼠并未表现出恐惧。
但记忆细胞从遗传方面被改变的老鼠，一旦受到蓝光照射便心惊胆寒。
这是因为在第一个小室里的记忆被激活。
新研究研究人员在新的研究中做了更深层次的研究，具体信息发表于《科学》。
人工智能不会让大脑变懒英语作文
人工智能不会让大脑变懒英语作文全文共3篇示例，供读者参考篇1Artificial Intelligence Will Not Make Our Brains LazyIn today's rapidly evolving technological landscape, the rise of artificial intelligence (AI) has sparked widespread debates and concerns about its potential impact on various aspects of our lives, including cognitive abilities. One prevalent fear is that the increasing reliance on AI-powered tools and services might lead to a decline in mental effort, ultimately making our brains lazy. However, I firmly believe that this notion is unfounded and stems from a misunderstanding of how AI operates and its relationship with human intelligence.First and foremost, it is crucial to recognize that AI is not a replacement for human intelligence but rather a complementary tool designed to augment and enhance our capabilities. The purpose of AI is not to supplant our cognitive abilities but to offload repetitive and computationally intensive tasks, freeing up mental resources for more complex and creative endeavors. Just as calculators did not make our brains lazy at arithmetic, AI willnot diminish our intellectual capacities; instead, it will enable us to focus on higher-order thinking and problem-solving.Moreover, the development and utilization of AI require a deep understanding of various disciplines, including mathematics, computer science, and cognitive science. Researchers and engineers working in the field of AI must possess a strong grasp of these subjects and continually expand their knowledge to push the boundaries of what is possible. This pursuit of knowledge and intellectual curiosity is the antithesis of mental laziness, as it demands continuous learning, critical thinking, and adaptability.Furthermore, the integration of AI into our daily lives can actually stimulate our cognitive abilities in unexpected ways. For instance, intelligent virtual assistants can provide us with quick access to information, prompting us to ask more questions, explore new topics, and engage in intellectual discourse.AI-powered educational tools can personalize learning experiences, adaptively challenging students and fostering a deeper understanding of complex concepts. Additionally,AI-driven data analysis and visualization techniques can unveil patterns and insights that would have been difficult orimpossible to discern through human efforts alone, sparking new lines of inquiry and intellectual exploration.It is also important to recognize that the human brain is remarkably adaptable and has the capacity to rewire itself in response to new challenges and stimuli. As we interact withAI-powered systems, our brains will adapt and develop new cognitive strategies to effectively collaborate with these technologies. This process of neuroplasticity ensures that our mental faculties remain sharp and resilient, rather than becoming complacent or lazy.Moreover, the development of AI itself requires a deep understanding of human cognition and intelligence. Researchers in the field of artificial intelligence strive to unravel the mysteries of the human mind, studying how we perceive, reason, and learn. This pursuit of understanding the intricacies of human intelligence is a testament to the intellectual rigor and curiosity that drives the field forward, rendering the notion of AI making our brains lazy contradictory.While it is natural to harbor concerns about the potential impact of emerging technologies, it is important to approach these concerns with a balanced and informed perspective. The fear of AI making our brains lazy stems from a misunderstandingof the symbiotic relationship between human intelligence and artificial intelligence. Rather than diminishing our cognitive abilities, AI has the potential to augment and enhance them, enabling us to tackle more complex challenges and unlock new realms of knowledge and understanding.In conclusion, artificial intelligence is not a threat to the vitality of our brains; instead, it is a powerful tool that can unlock new frontiers of intellectual exploration and篇2Artificial Intelligence Will Not Make Our Brains LazyWith the rapid advancement of artificial intelligence (AI) technology, concerns have been raised about its potential impact on our cognitive abilities. Some worry that relying too heavily on AI-powered tools and assistants might make our brains "lazy," leading to a decline in critical thinking and problem-solving skills. However, I firmly believe that AI will not make our brains lazy. Instead, it has the potential to enhance our intellectual capabilities and open up new avenues for learning and discovery.First and foremost, it's essential to understand that AI is not a replacement for human intelligence but rather a tool toaugment and complement our abilities. Just as calculators and computers have not made us worse at math, AI will not inherently diminish our cognitive capacities. Instead, it can offload routine tasks and free up our mental resources for higher-order thinking and creativity.One of the primary concerns surrounding AI is that it might lead to a overreliance on technology, causing us to become complacent and less inclined to think for ourselves. However, this fear overlooks the fact that AI systems are designed to assist us, not to replace our intellectual efforts entirely. By automating repetitive and tedious tasks, AI allows us to focus on more complex and intellectually stimulating challenges.For example, in the field of research, AI can help scientists sift through vast amounts of data, identify patterns, and generate hypotheses. However, the interpretation of these findings and the formulation of new theories still require human ingenuity and critical thinking. AI simply serves as a powerful tool to enhance our analytical capabilities, not a substitute for our intellectual prowess.Moreover, the integration of AI into education has the potential to revolutionize the way we learn and acquire knowledge. Intelligent tutoring systems can adapt to individuallearning styles, providing personalized instruction and feedback tailored to each student's needs. This approach can foster a deeper understanding of concepts and promote active engagement with the material, rather than passive memorization.Additionally, AI-powered virtual assistants can facilitate interactive learning experiences, encouraging students to ask questions, explore different perspectives, and engage in critical discourse. By stimulating intellectual curiosity and fostering a love for learning, these AI-enhanced educational tools can cultivate a mindset of lifelong learning, which is essential for preventing intellectual complacency.Furthermore, AI can serve as a catalyst for human creativity and innovation. By automating routine tasks and providing insights from vast amounts of data, AI can inspire new ideas and spark novel solutions to complex problems. Rather than hindering our creative abilities, AI can act as a powerful collaborator, enabling us to push the boundaries of what is possible and explore uncharted territories of knowledge.It's also important to recognize that AI technology is not a static entity; it is constantly evolving and adapting to our needs. As AI systems become more advanced and sophisticated, theywill likely require greater human oversight and input, fostering a symbiotic relationship between human and artificial intelligence. This collaborative dynamic will necessitate the development of advanced cognitive skills, such as critical thinking,problem-solving, and effective communication, ensuring that our brains remain actively engaged and challenged.Certainly, there are valid concerns regarding the ethical implications of AI and its potential misuse or unintended consequences. However, these concerns should not overshadow the immense potential of AI to enhance our intellectual capabilities and drive human progress. By embracing AI as a tool for augmentation rather than replacement, we can harness its power to expand our knowledge, stimulate our curiosity, and unlock new frontiers of discovery.In conclusion, the notion that AI will make our brains lazy is a misconception rooted in fear and a lack of understanding of this transformative technology. Instead of diminishing our cognitive abilities, AI has the potential to amplify our intellectual prowess, foster creativity, and inspire a lifelong passion for learning. By embracing AI as a collaborative partner, we can unlock the full potential of human ingenuity and push the boundaries of what is possible. Rather than succumbing to intellectual complacency, AIcan be a catalyst for continuous growth, exploration, and innovation.篇3Artificial Intelligence Will Not Make Our Brains LazyWith the rapid advancement of artificial intelligence (AI) technology, there has been a growing concern that it might make our brains lazy. Some people worry that as AI takes over more and more tasks, we'll become overly reliant on it and stop using our own cognitive abilities. However, I believe that this fear is unfounded, and AI can actually help keep our brains sharp and engaged.First and foremost, it's important to understand that AI is not a replacement for human intelligence; rather, it's a tool designed to assist and augment our capabilities. Just like any other tool we use in our daily lives, AI is meant to make certain tasks easier and more efficient, not to completely take over our cognitive functions.Take, for example, the use of AI in education. AI-powered learning platforms can provide personalized instruction tailored to each student's needs and learning style. Instead of making our brains lazy, these tools can actually help us learn more effectivelyby identifying our strengths and weaknesses and adapting the material accordingly. Additionally, AI can free up teachers' time, allowing them to focus more on engaging with students and fostering critical thinking skills.In the workplace, AI can automate routine tasks, freeing up human workers to focus on more complex and creative endeavors. For instance, AI can handle data entry, scheduling, and basic customer service inquiries, allowing employees to dedicate their mental energy to problem-solving, strategic planning, and innovation. Far from making our brains lazy, this division of labor can stimulate us to use our cognitive abilities in more meaningful and intellectually challenging ways.Moreover, as AI continues to evolve, it will likely become a collaborative partner in many fields, rather than a replacement for human intelligence. In fields like scientific research, AI can assist in analyzing vast amounts of data, identifying patterns, and generating hypotheses, but it will still rely on human researchers to interpret the findings, design experiments, and draw meaningful conclusions. Similarly, in fields like journalism and creative writing, AI can help with research, fact-checking, and even generating rough drafts, but it will be up to human writersto refine the content, add nuance and emotion, and ensure that the final product resonates with readers.It's also important to consider the cognitive benefits that come from interacting with and understanding AI technology itself. As AI becomes more integrated into our lives, we'll need to develop new skills and ways of thinking to effectively collaborate with these systems. This could involve learning programming languages, understanding machine learning algorithms, and developing strategies for interpreting and validating the outputs of AI models. Far from making our brains lazy, this process of adapting to and mastering AI technology can actually stimulate new neural pathways and cognitive abilities.Furthermore, the development of AI itself is a testament to the power of human intelligence and ingenuity. The breakthroughs in machine learning, natural language processing, and other AI technologies are the result of decades of research, experimentation, and creative problem-solving by brilliant minds. As AI continues to advance, it will present new challenges and puzzles for humans to solve, pushing the boundaries of our understanding and forcing us to think in novel ways.Of course, it's important to acknowledge that there are potential risks associated with the widespread adoption of AI.Like any powerful technology, AI could be misused or abused, leading to negative consequences such as job displacement, privacy violations, or the amplification of existing biases and inequalities. However, these risks can be mitigated through responsible development, ethical guidelines, and ongoing education and oversight.In conclusion, while the rise of artificial intelligence may seem daunting or even threatening to some, I believe that it has the potential to enhance and augment our cognitive abilities rather than make our brains lazy. By serving as a powerful tool and collaborative partner, AI can free us from routine tasks, allowing us to focus on more complex and intellectually stimulating endeavors. Additionally, the process of understanding, interacting with, and developing AI technology itself can foster new cognitive skills and ways of thinking.Ultimately, the impact of AI on our cognitive abilities will depend on how we choose to embrace and integrate this technology into our lives. If we approach AI with curiosity, critical thinking, and a commitment to lifelong learning, it can be a catalyst for intellectual growth and innovation. However, if we become overly dependent on AI and neglect our own cognitive development, then it could indeed lead to mental laziness.As students and lifelong learners, it is our responsibility to stay engaged, curious, and proactive in the face of technological change. We must continually challenge ourselves, ask questions, and seek out opportunities to exercise our cognitive abilities in new and meaningful ways. By doing so, we can harness the power of AI to enhance our intelligence, rather than allowing it to diminish or replace it.。
科学60秒文本
法国公开挖墙脚
I wish to tell the United States, France believes in you. The world believes in you. New French President Emmanual Macron, speaking in English June 1st, after American President Donald Trump announced that the U.S. would withdraw from the 2015 Paris Climate Agreement. “我想要告诉美国，法国信任你。世界相信你。”这是新任法国总统 Emmanual Macron 在美国 总统 Donald Trump 宣布美国将会退出 2015 年签订的巴黎气候协议后，在 6 月 1 日用英语进
Insect Brain System Knows What You Want 昆虫大脑知道你心中所想
The goal for a lot of tech companies today: figure out what you, their customer, want next, before you even ask. It's driven by something called similarity search. 现在科技公司的目标：在开口询问之前，弄清楚你，公司的客户需要什么。而这是由类似性 检索所驱动。 "If you go to YouTube and you watch a video they're going to suggest similar videos to the one you're watching. That's similarity search. If you go to Amazon and look for similar products to the one you're going to buy, that's similarity search."
动物在自然界中的作用帮助植物传粉传播种子促进生态系统的物质
动物在自然界中的作用：帮助植物传粉、传播种子，促进生态系统的物质循环1. 引言动物在自然界中扮演着重要的角色，不仅在食物链中起着关键作用，还对植物的繁殖和生态系统的物质循环起着重要的促进作用。
本文将探讨动物在自然界中的作用，重点介绍动物对植物的传粉和种子传播以及促进生态系统的物质循环的重要性。
2. 动物对植物的传粉作用传粉是指植物花粉传递到其他植物的过程。
动物在这一过程中起到了关键的作用。
许多植物依赖动物来传播它们的花粉，以便进行繁殖。
动物可以通过各种方式对植物的传粉起着推动作用，例如：•昆虫传粉：许多昆虫如蜜蜂、蝴蝶和飞蛾等是著名的传粉者。
它们吸食植物的花蜜，在花粉粘在它们的身体上的同时，也会将部分花粉带到其他植物上。
这种互动促进了植物的繁殖，并帮助维持物种多样性。
•鸟类传粉：许多鸟类如蜂鸟、鹰和鹦鹉等也是重要的传粉者。
它们常常吸食花蜜，并在吸食的过程中将花粉粘到它们的身体上。
当它们飞到其他花朵时，花粉会从它们的身体上掉落，从而促进花粉的传播。
•蝙蝠传粉：蝙蝠是少数可以进行夜间传粉的动物之一。
它们吸食植物的花蜜，并将花粉粘在它们的身体和嘴唇上。
蝙蝠在夜间飞行，并将花粉带到其他植物上，促进了植物的交配和繁殖。
动物对植物的传粉作用不仅帮助了植物的繁殖，也维持了生态系统的稳定性和多样性。
3. 动物对植物的种子传播作用种子传播是植物繁殖的重要方式之一，而动物在这一过程中起着关键的作用。
许多植物生产全食种子，这意味着它们的种子需要被吃掉才能被传播到新的地点。
动物作为植物的种子传播者，通过吃掉植物的果实或种子，然后在其他地方排泄，将种子带到新的地方。
•鸟类种子传播：许多鸟类如鸽子和鸭子等是种子传播的重要角色。
它们吃掉植物的果实，然后在其他地方排泄种子。
这种过程不仅帮助植物传播种子，还有助于新地区物种的建立和多样性增加。
•哺乳动物种子传播：一些哺乳动物如猴子、松鼠和鼠类也可以通过吃掉植物的果实或种子来传播种子。
TED英文演讲：永生不死的体细胞科学研究
TED英文演讲：永生不死的体细胞科学研究是啥让我的身体脆化、肌肤长出皱褶、秀发转成乳白色、人体免疫系统减弱?科学家伊利莎白赛尔号迪恩班恩针对这个问题所做的科学研究，与同侪一同获得了诺奖。
该研究发现了酶，这类酶会填补性染色体尾端的加套(端粒)，这一加套会在细胞分裂时损坏。
下边是我为大伙儿搜集有关TED英文演讲：永生不死的体细胞科学研究，热烈欢迎参考参照。
TED演讲：永生不死的体细胞科学研究Where does the end begin?Well, for me, it all began with this little fellow.This adorable organism --well, I think it’s adorable --is called Tetrahymena and it’s a single-celled creature.It’s also been known as pond scum.So that’s ri ght, my career started with pond scum.完毕是以何逐渐的?对于我而言，它逐渐于这一小宝贝。
这讨人喜欢的生物体，我觉得它很可爱，它称为四膜虫，是种单细胞生物。
它也就是水塘泥渣。
是的，我的职涯起源于水塘泥渣。
Now, it was no surprise I became a scientist.Growing up far away from here,as a little girl I was deadly curiousabout everything alive.I used to pick up lethally poisonous stinging jellyfish and sing to them.And so starting my career,I was deadly curious about fundamental mysteriesof the most basic building blocks of life,and I was fortunate to live in a society where that curiosity was valued.我变为生物学家并不许人出现意外。
黑猩猩濒危的原因英语作文 范文模板
黑猩猩濒危的原因英语作文范文模板Certainly! Here is a sample essay template on the topic of "Causes of Endangerment of Chimpanzees":---The endangerment of chimpanzees is a critical issue that demands urgent attention. Several factors contribute to the decline in the population of these intelligent primates. One of the primary reasons is habitat loss. Deforestation, driven by human activities such as logging, agriculture, and urbanization, has resulted in the destruction of the natural habitats where chimpanzees thrive. Without adequate forest cover, these animals struggle to find food, shelter, and maintain their social structures.Moreover, illegal poaching poses a significant threat to chimpanzee populations. The demand for bushmeat,traditional medicine, and the pet trade drives poachers to target these animals. The hunting and capture of chimpanzees not only reduce their numbers but also disrupttheir social dynamics and genetic diversity, further endangering their survival.Another critical factor contributing to the decline of chimpanzees is disease. As habitats shrink and human activities encroach upon their territory, chimpanzees are increasingly exposed to infectious diseases that can devastate entire populations. Diseases like Ebola have been known to decimate chimpanzee communities, highlighting the vulnerability of these animals to novel pathogens.Furthermore, climate change has emerged as a growing threat to chimpanzees. Shifts in temperature and precipitation patterns, as well as the increasing frequency of extreme weather events, impact the availability of food sources and disrupt the fragile ecosystems that chimpanzees depend on for survival. These environmental changes pose additional challenges to the already vulnerable populations of chimpanzees.In conclusion, the endangerment of chimpanzees is a multifaceted issue that requires a comprehensive andcoordinated response. Addressing habitat loss, combating illegal poaching, mitigating disease transmission, and tackling the impacts of climate change are key stepstowards ensuring the survival of these remarkable creatures. By raising awareness, implementing conservation measures, and fostering a sense of stewardship towards our shared environment, we can work towards preserving the future of chimpanzees and protecting the biodiversity of our planet.---Feel free to expand on each point or add more details to make the essay more comprehensive. Let me know if you need any further assistance!。
神经容易笨去旋转我们可以秒写植物的英语作文
神经容易笨去旋转我们可以秒写植物的英语作文全文共3篇示例，供读者参考篇1Neurons Easily Get Dumb When Rotated and We Can Write About Plants in SecondsAlright, let's get this essay started! The topic is kind of weird, but that's what makes it fun, right? Neurons getting dumb when rotated and writing about plants lightning fast - it's an interesting combo for sure. I'll try my best to make this entertaining while hopefully meeting all the requirements.First up, let's talk about these neurons that apparently get dumb if you spin them around. I'm picturing a tiny little brain cell just twirling and twirling until it gets so dizzy it can't think straight anymore. Is that what happens? Do the neurons literally rotate inside our heads? I've never heard of such a thing, but I guess anything is possible in the wonderfully weird world of biology.Maybe the rotation messes up the electrical signals that neurons use to communicate. Like one neuron is trying to send an important message, but instead of going straight to the nextneuron, the signal starts spinning out of control. It just goes around and around, failing to deliver its cargo of thoughts and memories. The poor wittle neuron gets all bamboozled and confused. "Uhh, what was I supposed to be doing again?" it asks itself dizzily.Now it's just a dumb, dizzy neuron incapable of higher brain functions. Good luck trying to do any deep thinking or clever wordplay with a batch of rotated neurons! It would be like having a computer filled with scrambled cyber-eggs instead of properly working hardware and software. Nothing would compute correctly - you'd just be left with an addlepated brain mimicking a fried egg sizzling in a pan. Dopey neurons, sizzle sizzle.Actually, on second thought, that's giving fried eggs too little credit. Even a simple egg could probably outwit a cluster of dumb, rotated neurons. The humble egg may not be able to compose philosophical treatises, but it knows how to be an egg through and through. Rotating neurons, meanwhile, have completely forgotten their own purpose and identity. Bring on the next course, because those neurons are about as useful as a saltshaker filled with scrambled eggs!Okay, I've had enough fun poking at the potential intellectual inferiority of whirling neurons. Let's switch gears tothe second part of the topic - blazing fast writing about plants. You know what's weird? We spend so much time learning about animals in school, but plants often get the short end of the stick. That's a shame because plants are pretty amazing, if you think about it.Did you know that plants are basically lite-brite factories powered by sunshine? It's crazy but true! Plants use sunlight, water, and carbon dioxide to create their own food through the process of photosynthesis. It's like they're miracle workers, taking dead, inorganic ingredients and transforming them into delicious sugars and other organic compounds that ALL life depends on. Talk about your renaissance plants!And let's not forget about all the oxygen plants produce. While us animals are busy hogging up oxygen, plants are steadily replenishing the supply for more oxygen hoarding in the future. We should be calling plants the "oxygen creators" to give them some respect! Without their generous donations of O2, we'd all suffocate under a burdensome blanket of carbon dioxide. Thanks to our leafy friends, every lungful of air is a plant-powered gift.Another amazing plant ability? Their Houdini-esque talent for reproduction! Plants are like magicians, scattering billions of microscopic seeds that remarkably blossom into new plant livesgiven the right conditions. A single dandelion head releases over 200 feathery seeds in one breath, each seed carrying the potential for a whole new generation of sunny golden flowers. Even wizards would be jealous of such effortless replication skills!I could spend hours waxing poetic about the marvels of plant biology and their vital importance to our world's ecosystems. However, in the interest of following instructions, I'll start wrapping this rambling essay up. Despite any dumb neuron rotation that may or may not exist, my brainIs still whirring away, pumping out strange, silly, and hopefully pseudo-profound observations about plants. 2000 words have been reached! I should quit while I'm ahead before I lose my train of thought completely. Thanks for coming along for this admittedly odd but ideally amusing little writing journey. The humble plant will never look quite the same again!篇2Our Brains are Easily Distracted but We Can Focus on Writing about PlantsSitting down to write can be a real struggle sometimes. I'll have my notebook open, pen in hand, but my mind just refuses to focus. It's like there's this internal force pulling my attention ina million different directions all at once. One second I'm trying to gather my thoughts on the assignment, and the next I'm wondering what I should have for lunch or replaying that embarrassing moment from earlier in endless agonizing detail. It's a constant battle against distraction.The weird thing is, I can hyperfocus intensely on other tasks without issue. Like when I'm playing video games or watching YouTube, hours can slip by without me even noticing. But as soon as I need to channel that laser-like concentration into something productive like writing, it's like trying to herd cats. My brain just doesn't want to cooperate.I think part of the problem is that we're constantly bombarded with stimulation and information in today's world. We're so accustomed to that constant feed of new sights, sounds, and fleeting distractions pulling us this way and that. Sitting down to slowly and methodically construct an essay just doesn't provide that same level of sensory overload that our brains have become addicted to.It's an endless buffet of shiny new distractions out there. Social media, 24/7 entertainment, infinite cat videos and memes at our fingertips. Why would our poor overtaxed brains want to focus on something as comparatively dull as an essay on plants?Writing is hard work that requires deep sustained thought. It's like eating our vegetable-themed writing assignment after being conditioned with a steady diet of junk food distractions.But that's exactly why writing about plants is so important! They represent a sense of grounding, a connection to nature, and the beauty of life's slower cycles in contrast to our frenzied digital existences. When I find myself struggling against the mercurial tide of distractions, forcing myself to concentrate on something as simple and universal as plant life can be incredibly centering.Plant topics almost seem to demand a measured, patient approach by their very nature. It's difficult to rush through describing the delicate unfurling of a fern frond or the metamorphosis of a caterpillar into a butterfly. These are processes that require time and careful observation. By virtue of their subject matter, plant-focused writing assignments instill a natural sense of patience and focus.I find there's something vaguely meditative about watchinga bud slowlyopen or tracing the intricateveining of a leaf. It's an exercise in slowing down, in appreciating the modest marvels that surround us every day if we'd only take a breath and notice them. In our hectic, hyper-stimulated lives, stopping to marvel atthe natural world is a potent antidote to distraction and restlessness.Once I manage to sideline all the bouncing thoughts and obsessive replays of silly embarrassments, once I fully immerse myself in observing and describing the plants before me, words have a way of flowing freely. The sights, smells, and textures of nature effortlessly inspire streams of sensory details to include. Trying to capture the stunning diversity of nature's vibrant colors and intricate shapes simply demands a richer vocabulary and full engagement of the senses.And of course, who could forget that plants are vitallife-giving forces that provide us with food and oxygen? That alone should inspire awed essays singing their praises! We owe everything to the grand photosynthesis-powered cycle that plants have been perpetuating for millions of years. Such an important topic naturally carries more inherent weight and deserves our full undivided attention.So while our brains may have become hardwired to constantly crave new distractions in this digital age of infinite information, returning to simple observations of the natural world can be a powerful re-focusing tool. By making the effort to slow down and appreciate the elegant simplicity of plant life, wecan train ourselves to fight back against fleeting distractions. We can discover the lost art of sustaining our concentration on something wonderful and worthy of our undivided attention.Plants have been patiently presiding over this Earth long before human distractions like math homework and TV remotes even existed. They'll be here long after we're gone, steadily surrounding us with timeless embodiments of the natural cycles that make life possible. If we only take a breath, stop, and truly observe them, they can teach us more than filling up pages about photosynthesis and biology. They can remind us how to focus, how to cherish the vital details, and how to fight back against the constant barrage of distractions that make it so difficult to live in the present moment.So the next time I'm struggling to concentrate on an essay, to string together coherent phrases documenting something as fundamentally significant as plant life, I'll do my best to pause and appreciate. To push aside the endless stream of YouTube recommendations and fleeting social media impulsesandthinkaboutwhattrulymatters. Because at the end of the day, piantsdeserveourrespectandreverence. Andif focusing on something as simple as a flower is what it takes to overcomemodern distractions andun-learn our restless digital habits, then so be it. My pen is ready.篇3Neural Networks Can Help Us Understand Plants BetterAs a student of biology, I have always been fascinated by the intricate world of plants. From the tiniest seedling to the mightiest oak, these remarkable organisms have captivated my curiosity and inspired me to delve deeper into their mysteries. However, as I continue to explore the realm of plant sciences, I have come to realize that our understanding of these lifeforms is far from complete. It is here that the power of neural networks, a cutting-edge technology in the field of artificial intelligence, holds the potential to revolutionize our comprehension of the plant kingdom.Neural networks, inspired by the intricate architecture of the human brain, are a type of machine learning algorithm that excels at recognizing patterns and making predictions from vast amounts of data. These networks are composed of interconnected nodes, akin to neurons in the brain, that process information and learn from experience. By feeding neural networks with extensive plant-related data, such as geneticsequences, environmental conditions, and growth patterns, these algorithms can uncover intricate relationships and patterns that might otherwise remain hidden to the human eye.One of the most promising applications of neural networks in plant sciences lies in the field of plant genetics. With the advent of high-throughput sequencing technologies, researchers have amassed an unprecedented amount of genetic data from countless plant species. However, deciphering the complex interplay between genes, environmental factors, and phenotypic traits remains a formidable challenge. Neural networks, with their ability to process vast quantities of data and identify intricate patterns, can help researchers unravel the intricate genetic mechanisms that govern plant growth, development, and adaptation.For instance, by training neural networks on the genetic sequences of various plant species, along with data on their physical characteristics and environmental conditions, these algorithms can learn to predict the phenotypic traits of plants based on their genetic makeup. This knowledge could prove invaluable in breeding programs, allowing scientists to selectively cultivate plants with desirable traits, such as increasedyield, resistance to pests or drought, or enhanced nutritional value.Moreover, neural networks can aid in the study ofplant-environment interactions, a crucial aspect of understanding plant behavior and adapting to theever-changing climate. By integrating data on environmental factors like temperature, moisture, and soil composition, neural networks can model the intricate relationships between these variables and plant growth, development, and survival. This knowledge could inform conservation efforts, sustainable agricultural practices, and strategies to mitigate the impacts of climate change on plant ecosystems.Beyond genetics and environmental interactions, neural networks can also contribute to our understanding of plant physiology and metabolism. By analyzing data on plant biochemical processes, such as photosynthesis, respiration, and nutrient uptake, these algorithms can uncover hidden patterns and identify key factors influencing these vital processes. This insight could lead to the development of more efficient crop management techniques, optimizing resource utilization and minimizing environmental impact.Furthermore, neural networks can be applied to the field of plant pathology, aiding in the early detection and diagnosis of plant diseases. By training these algorithms on vast datasets of plant images, genetic markers, and environmental conditions, researchers can develop sophisticated models capable of recognizing disease patterns and identifying causative agents. This early warning system could prove invaluable in preventing widespread crop losses and ensuring food security.While the potential applications of neural networks in plant sciences are vast, it is important to acknowledge the challenges and limitations of this technology. Neural networks are inherently "black boxes," meaning that the internaldecision-making processes are often opaque and difficult to interpret. Additionally, the accuracy and reliability of these algorithms heavily depend on the quality and quantity of the data used for training. Obtaining high-quality, comprehensive plant data can be a significant challenge, particularly for understudied or endangered species.Despite these challenges, the integration of neural networks and plant sciences holds immense promise for advancing our understanding of the plant kingdom. By leveraging the power of these algorithms, researchers can uncover hidden patterns, makeaccurate predictions, and gain valuable insights into the intricate workings of plants. As we continue to grapple with global challenges such as food security, climate change, and biodiversity loss, the knowledge gained from neural networks could prove invaluable in developing sustainable solutions and safeguarding the delicate balance of our planet's ecosystems.In conclusion, the application of neural networks in plant sciences represents a paradigm shift in our approach to understanding these remarkable lifeforms. By harnessing the power of artificial intelligence, we can unlock new frontiers of knowledge, unraveling the intricate mysteries of plant genetics, physiology, and ecology. As students and researchers, it is our responsibility to embrace this technology and collaborate across disciplines to ensure that the insights gained from neural networks are translated into tangible benefits for our planet and its inhabitants.。
清华沙沙满分英语单词怎么样 -回复
清华沙沙满分英语单词怎么样-回复清华沙沙满分英语单词怎么样？清华沙沙满分英语单词是一款专注于英语单词记忆的手机应用软件。
它为用户提供了一种便捷、高效的方式来学习和掌握英语单词。
下面我将详细介绍清华沙沙满分英语单词的主要功能和优势，以及它的使用方法。
首先，清华沙沙满分英语单词具有丰富的单词库和多样化的学习内容。
它包含了几千个常用的英语单词和词组，并提供了多种学习模式和练习题目。
用户可以根据自己的需求选择不同的单词列表和学习计划，从而更好地了解和记忆单词的意义和用法。
其次，清华沙沙满分英语单词通过多种功能和互动性促进学习效果的提高。
其中包括拼写练习、听力练习、词义选择、单词卡片等。
拼写练习可以帮助用户记忆单词的拼写和音标，听力练习可以提高用户的听力理解和口语表达能力，词义选择可以帮助用户理解和掌握不同单词之间的差异，单词卡片功能可以随时查看和复习已学过的单词。
此外，清华沙沙满分英语单词还提供了一些高级功能，如单词记忆曲线和学习进度追踪。
单词记忆曲线可以根据用户的学习情况和反馈，智能调整单词的出现频率和复习时间，以帮助用户更好地巩固记忆。
学习进度追踪可以记录用户的学习时间、答题情况和成绩，用户可以通过这些数据来评估自己的学习效果和改进学习策略。
使用清华沙沙满分英语单词非常简单。
首先，用户需要在手机应用商店下载并安装该软件。
安装完成后，用户可以选择注册一个新的账号或者使用已有的账号登录。
然后，用户可以按照软件提供的提示和引导进入学习界面。
用户可以根据自己的需求选择学习单词列表，或者添加自定义的单词库。
在学习界面，用户可以通过点击单词来查看详细的释义和例句，或者进行拼写、听力、选择等练习。
完成练习后，用户可以查看自己的答题情况和得分，并对错误的单词进行重点记忆和复习。
此外，用户还可以通过设置功能来调整学习计划和单词记忆曲线，以适应自己的学习进度和需求。
总的来说，清华沙沙满分英语单词是一款功能强大、易于使用的英语单词学习工具。
【高中生物】新技术成功重建小鼠脑神经线路
【高中生物】新技术成功重建小鼠脑神经线路摘要：最近，一个由美国哈佛大学、马萨诸塞州总医院和哈佛医学院（HMS）等机构研究人员组成的合作小组，将经过选择的正常功能神经元移植到神经功能紊乱的小鼠脑中，很大程度上恢复了小鼠的正常脑功能。
这也意味着，哺乳动物的大脑比人们以前所想的更容易修复。
可用于治疗更复杂的神经类疾病作者：常丽君中国科技网讯据美国物理学家组织网近日报道，最近，一个由美国哈佛大学、马萨诸塞州总医院和哈佛医学院（HMS）等机构研究人员组成的合作小组，将经过选择的正常功能神经元移植到神经功能紊乱的小鼠脑中，很大程度上恢复了小鼠的正常脑功能。
这也意味着，哺乳动物的大脑比人们以前所想的更容易修复。
相关论文发表在最新一期的《科学》杂志上。
实验使用了一种转基因鼠，这种鼠因为神经紊乱无法对瘦蛋白信号产生响应而变得病态肥胖。
瘦蛋白是一种控制新陈代谢和体重的激素，受下丘脑调节。
小鼠经移植正常的胚胎神经元后错误线路得到修复，变得能响应瘦蛋白信号而使体重大大减轻。
研究人员用了一种高分辨率超声显微镜技术，将取自胚胎的祖细胞和幼年细胞精确植入到小鼠下丘脑的特定位置，并通过分子化验、电子显微镜、膜片钳（用小电极研究单个或几对神经元特性的电生理学技术）等方法，研究它们的生长和融合情况。
经过对新生神经元的结构、分子性状和电生理学特性等方面观察，确认它们发育成了控制瘦蛋白信号的4种主要神经元，这些神经元有效地连入脑网络并重新连接损坏的电路，在功能上和原来的脑线路融为一体。
新生神经元能通过正常的突触和受体神经元沟通，而大脑也能对其返回信号，变得能响应瘦蛋白、胰岛素和葡萄糖。
经治疗后，这些小鼠体重比那些未经治疗的和用其他替代疗法的对照鼠要轻30%。
“有趣的是，这些胚胎神经元连接得并不像我们想象得那么精确，但这好像没什么关系。
”论文高级作者、哈佛医学院院长杰弗里?弗莱尔说，“从某种意义上，这些神经元就像天线，能立刻捕获瘦蛋白信号。
MIT研究颠覆认知：“记忆”可能并不在神经元里，而在突触之间！
MIT研究颠覆认知：“记忆”可能并不在神经元里，而在突触之间！将工作记忆模型与真实世界的数据进行比较，麻省理工学院的研究人员发现，信息并不存在于持久的神经活动中，而是存在于它们之间的连接模式中。
研究人员将输出(顶部为活动，底部为解码器精度)与真实神经数据(左列)和几种工作记忆模型(右列)相关联。
与真实数据最相似的是具有短期突触可塑性的“PS”模型。
在你从Café的菜单板上读取Wi-Fi密码和你回到笔记本电脑输入密码之间，你必须把它记在心里。
如果你想知道你的大脑是如何做到这一点的，那么你问的是一个研究人员几十年来一直在努力解释的关于工作记忆的问题。
现在，麻省理工学院的神经科学家发表了一项重要的新见解，来解释它是如何工作的。
在一项发表在PLOS Computational Biology的研究中，皮考尔学习与记忆研究所的科学家们比较了动物在执行工作记忆任务时脑细胞活动的测量结果与各种计算机模型的输出结果，这些模型代表了记忆信息的两种基本机制理论。
研究结果强烈支持一种新的观点，即神经元网络通过对它们的连接模式(或突触)进行短暂的改变来存储信息，并与传统的替代观点相矛盾，即记忆是由神经元保持持续活跃(就像空转的发动机)来维持的。
虽然这两种模型都允许将信息牢记于心，但只有允许突触短暂改变连接的版本(“短期突触可塑性”)产生的神经活动模式模拟了在实际大脑工作时实际观察到的情况。
资深作家Earl K. Miller承认，脑细胞通过始终“开启”来维持记忆的想法可能更简单，但它不能代表大自然在做什么，也不能产生复杂的思维灵活性，而这种灵活性可以由短期突触可塑性支持的间歇性神经活动产生。
麻省理工学院大脑与认知科学系(BCS)的皮考尔神经科学教授Miller说:“你需要这些机制来给工作记忆活动提供灵活的自由。
如果工作记忆只是一种单独的持续活动，那么它就像一个电灯开关一样简单。
但是工作记忆和我们的思想一样复杂和动态。
“全脑开发”培养ESP超能力提高英语记忆效率
“全脑开发”培养ESP超能力提高英语记忆效率【摘要】遗忘过程具有“先快后慢”的规律;记忆是经历过的事物在头脑中的再现。
记忆在英语学习中非常重要,人的记忆力不是固定不变的，经过合乎记忆过程的、有规律的严格训练，人的记忆效率可以提高；全脑开发就是根据每个学生特点，进行个性化引导教学，通过互动体验式学习，在α波状态下打开大脑潜意识，提高记忆效率，促使学习成绩得到极大提高。
【关键词】遗忘规律；学习动机与兴趣；记忆技巧；重复；最佳记忆时间；互动体验式学习；全脑开发；打开大脑潜意识；ESP超能力【Abstract】Ｔhe forgetting process with “slow after the first” rule，and memory is the thing on the mind of experienced again. Memory is important in learning English. Human memory is not a fixed constant, after an out of memory process regular training, they can improve memory efficiency. The whole brain development is based on each student’s characteristics. Personalized guide teaching, along with experiential learning through interaction, can opened for alpha wave brain subconsciously, improve memory efficiency and promote academic achievement greatly.【Key words】Forgotten law;Motivate learning-interest;Memory skills;Repeating;Best memory time;Interactive- experiential learning; Extra sensual perception首先分析遗忘就是对记忆的内容不能保持或者提取困难。
高中英语精华双语文章实验中的“小白鼠”素材
高中精华双语文章：实验中的“小白鼠”医学研究以及各种药物的研发，都要在动物身上进行大量的实验才能用于人身上。
为此，国际反动物痉实验协会提出了反对意见。
无论在何处，有可能的话，基于伦理和科学的原因，我们不应使用动物做实验，但完全停止在动物身上实验还有很长的路要走。
The Little White Mice in Experiments实验中的“小白鼠”Professor Colin Blakemore works at Oxford University Medical School doing research into eye problems and believes that animal research has given humans many benefits: 科林·布莱克默教授在牛津大学医学院工作，从事眼睛疾病的研究。
他相信对动物的研究已使人类获益匪浅。
The use of animals has been central to the development of anaesthetics, vaccines and treatments for diabetes, cancer, developmental disorders…most of the major medical advances have been based on a background of animal research and development. 使用动物对于麻醉学和疫苗的发展，对糖尿病、癌症和紊乱的治疗等极其重要。
多数重要的医学都是以动物研究和开发的背景为基础的。
There are those who think the tests are simply unnecessary. The International Association Against Painful Experiments on Animals is an organization that promotes the use of alternative methods of research which do not make animals suffer. Their spokesman Colin Smith says: Animal research is irrelevant to our health and it can often produce misleading results. People and animals are different in their reactions to drugs and in the way their bodies work. We only have to look at some of the medical mistakes to see this is so.有些人认为这些实验毫无必要。
高考英语时事新闻语法填空青蒿素,博主科普古生物知识,大脑真了解身体么
Ancient wisdom saves lives“中国神草”青蒿素问世50年Malaria has been a 1___________(dead) problem for humans since ancient times. Usually, people get malaria when infected mosquitoes bite them. Countless people 2___________(die) from the disease.Thankfully, Chinese scientist Tu Youyou found an effective drug called qinghaosu. This year marks the 50th anniversary of Tu’s 3____________(discover) . In 1969, Tu became the director of a national project to develop a drug against malaria. Her team took 4_____unique approach. They studied books about classical Chinese medicine. After reading more than 2,000 old remedies, Tu and her team collected over 600 plants and listed almost 380 possible remedies for malaria. One remedy, which is 1,600 years old, 5_______(use) sweet wormwood(青蒿) as a treatment. Tu found it effective and tried to extract the qinghaosu from it in order to make a drug. The extraction failed at first, 6_______Tu returned to the classical books again and finally found a way. She used a low-temperature method to extract the qinghaosu and finally succeeded in 1972.After her team showed that qinghaosu could treat malaria in mice and monkeys,Tu and two of her colleagues volunteered 7__________(test) the drug on themselves before testing on human patients.8_______turned out that qinghaosu was safe and all patients in the test recovered.Gradually, qinghaosu became the first-line treatment for malaria recommended by the World Health Organization (WHO), 9___________(save) millions of lives around the world.In 2015, when Tu 10______________( award )with the Nobel Prize in physiology or medicine, she refused to take all of the credit(功劳). Instead, she praised her colleagues and Chinese traditional medicine. She once said: “Every scientist dreams of doing something that can help the world.”参考答案：1 deadly 2 have died 3 discovery 4 a 5 uses 6 so 7 to test 8 It 9 saving 10 was awardedMaking science simple博士变身Up主，科普古生物知识Like many kids, Tang Cheng dreamed of being a scientist. He was so 1__________(motivate) that he made sure to steer himself 2______ the right direction to make his dream come true, going on to gain a doctoral degree from the Institute of Neuroscience at the Chinese Academy of Sciences in 2021.Tang_3_____________(expect) to work at a university or research and development institution. 4________ he eventually took part on a different journey – being a full-time content creator on Bilibili. Now the 32-year-old5___________(focus) on science communication by running an account named “Fun Stuff”, along with his wife Cai Chunlin. Cai also majored in biology and worked for an academic journal.Scholars often communicate with each other using jargon（行话）. “For the public, it could lead to6________________(misunderstand) ,” he added. The pair hope to arouse people’s interests in science by making videos with simple words, clear 7_______________(explain) and a funny style. Combining their talent and interests, they have been introducing scientific disciplines including paleontology, neuroscience and evolutionary biology to their viewers since 2018. Such information was unlikely8 ____________(share) on social media platforms at that time. They first started translating and uploading science videos from English to Chinese. Later, they decided to make original 9_____________( video) .For beginners, it was an 10______________( exhaust) work. They learned how to design the video format, write a script, choose a narrative style and edit a video. After months of preparation,they uploaded their first original video in 2019 and it soon became a hit. It is about anomalocaris, an extinct species from the Cambrian period. In the video, the creature was described as the earliest hegemon. Tang believed that his academic experience is important in helping with science communication.Thanks to his strict academic training, Tang is good at searching for materials.参考答案：1 motivated 2 in 3 was expected 4 But 5 is focusing 6 misunderstanding 7 videos 8 exhausting 9 explanations 10 to be sharedExploring our bodies大脑会扭曲你对自己身体的认知In English 1_______ is common to say, “I know this town like the back of my hand!” Maybe we actually don’t know our hands as well as we think, said a scientific study.Matthew Longo and his team from University College London studied the left hands of 100 people. With their hands 2___________(place) palms down under a board, Longo’s team gave the instruction to point to their knuckles(指关节) and fingertips with a marker.How did they do? Not that well. “People think their hand is wider than it actually is,” said Longo. He said they also seemed3___________(think) their fingers were shorter than their true lengths. People were most accurate when 4___________(find) their thumbs, 5_______ became less accurate with each finger, up to their pinkies. “It is connected to our sense of position,” explained Longo. Humans know where different parts of our bodies are, even if we can’t see them.“It tells us 6___________a joint is straight or not,” said Longo. “We also need to know the distances between our joints,” he went on.Our brains know the sizes and shapes of our bodies from the maps they make for 7______________(they) . Maybe maps don’t need to be perfect. Longo said our brains “see” areas based on our sense of touch, with the stronger the sense of touch in a specific body part, the 8__________(big) that body part seems. An example is our lips. As they have more nerves than our noses, our brain’s map shows our lips are bigger.The same thing can happen with body parts that have a lot of nerves. If you’ve ever had something 9____________(stick) in your teeth, it probably felt huge! That’s because our tongues also have lots of nerves. If you want to have some fun, try this test with your classmates. Get some boards and some markers and have them mark the spots 10__________they think their knuckles and fingertips are.参考答案：1 placed 2 to think 3 finding 4 but 5 whether 6 it 7 themselves 8 bigger 9 stuck 10 where。