Training Models of Shape from Sets of Examples

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湖南省长沙市第一中学2023-2024学年高三上学期月考卷(三)英语

长沙市一中2024届高三月考试卷(三)英语时量: 120 分钟满分: 150 分得分: 第一部分听力(共两节, 满分30 分)做题时, 先将答案标在试卷上。

录音内容结束后, 你将有两分钟的时间将试卷上的答案转涂到答题卡上。

第一节 (共5 小题;每小题1 . 5 分, 满分7 . 5 分)听下面5 段对话。

每段对话后有一个小题, 从题中所给的 A、B、C 三个选项中选出最佳选项。

听完每段对话后, 你都有10 秒钟的时间来回答有关小题和阅读下一小题。

每段对话仅读一遍。

例: How much is the shirt?A ·19 . 15 .B ·9 . 18 .C ·9 . 15 .答案是C。

1 . where is the woman probably from?A.per u.B.B r i t ai n . C . M exi c o .2 . what will the man do tonight?A. Attend a party.B. Reply to an invitation.C. play football ·3 . what does the woman think of her old roommate?A. selfish.B. Thoughtful.C. careful.4 . what should the city do according to the woman?A. create more jobs.B . Improve the air quality.C. close some businesses .5 . what are the speakers mainly talking about?A. Their daily routine .B. Their dormitory.C. The weather .第二节 (共15 小题;每小题1 . 5 分, 满分22 . 5 分)听下面5 段对话或独白。

高二英语复合句练习题40题

高二英语复合句练习题40题1<背景文章>Dr. Smith is a renowned scientist who has dedicated his life to the study of climate change. His research journey began when he was a young student fascinated by the mysteries of nature. After completing his undergraduate degree in environmental science, he decided to pursue a Ph.D. in climatology.During his doctoral research, Dr. Smith encountered many challenges. He had to analyze large amounts of data and use complex statistical models to understand the patterns of climate change. However, his determination and passion for science drove him to overcome these obstacles.One of the most significant contributions of Dr. Smith's research is his discovery of a new method to predict climate change. This method combines data from satellite imagery, ground-based sensors, and historical records to create a more accurate model of future climate trends. His work has been widely recognized by the scientific community and has led to several important policy decisions.Despite his many achievements, Dr. Smith remains humble and committed to furthering his research. He believes that only through continuous study and innovation can we hope to address the challenges ofclimate change and protect our planet for future generations.1. Dr. Smith began his research journey after he completed his undergraduate degree in environmental science, ___ he decided to pursue a Ph.D. in climatology.A. andB. butC. soD. or答案：A。

python实现线性回归之lasso回归

python实现线性回归之lasso回归Lasso回归于岭回归⾮常相似，它们的差别在于使⽤了不同的正则化项。

最终都实现了约束参数从⽽防⽌过拟合的效果。

但是Lasso之所以重要，还有另⼀个原因是：Lasso能够将⼀些作⽤⽐较⼩的特征的参数训练为0，从⽽获得稀疏解。

也就是说⽤这种⽅法，在训练模型的过程中实现了降维(特征筛选)的⽬的。

Lasso回归的代价函数为：上式中的w||w||1其中sign(θi)上述解释摘⾃：接下来是实现代码，代码来源：⾸先还是定义⼀个基类，各种线性回归都需要继承该基类：class Regression(object):""" Base regression model. Models the relationship between a scalar dependent variable y and the independentvariables X.Parameters:-----------n_iterations: floatThe number of training iterations the algorithm will tune the weights for.learning_rate: floatThe step length that will be used when updating the weights."""def__init__(self, n_iterations, learning_rate):self.n_iterations = n_iterationsself.learning_rate = learning_ratedef initialize_weights(self, n_features):""" Initialize weights randomly [-1/N, 1/N] """limit = 1 / math.sqrt(n_features)self.w = np.random.uniform(-limit, limit, (n_features, ))def fit(self, X, y):# Insert constant ones for bias weightsX = np.insert(X, 0, 1, axis=1)self.training_errors = []self.initialize_weights(n_features=X.shape[1])# Do gradient descent for n_iterationsfor i in range(self.n_iterations):y_pred = X.dot(self.w)# Calculate l2 lossmse = np.mean(0.5 * (y - y_pred)**2 + self.regularization(self.w))self.training_errors.append(mse)# Gradient of l2 loss w.r.t wgrad_w = -(y - y_pred).dot(X) + self.regularization.grad(self.w)# Update the weightsself.w -= self.learning_rate * grad_wdef predict(self, X):# Insert constant ones for bias weightsX = np.insert(X, 0, 1, axis=1)y_pred = X.dot(self.w)return y_pred需要注意的是这⾥的mse损失函数前⾯就只是0.5。

AI 艺术时代的原创会呈现怎样的形态中英互译

I want you to envision a single piece of artwork generated by artificial intelligence. When most of us think of AI art, I bet we’re imagining something like this. We’re all probably picturing something totally different.请大家想象一件由人工智能（AI）生成的艺术品。

大多数人想到AI 艺术时，我敢打赌我们想象的是这样的东西。

我们想象的东西可能完全不同。

Today, with machine learning models like DALL-E, Stable Diffusion and Midjourney, we’ve seen AI produce everything from strange life forms to imaginary influencers to entirely foreign, curious kinds of imagery. AI as a technology is fascinating to us because we’re inherently drawn to things we cannot understand.今天，有了像DALL-E、Stable Diffusion 和Midjourney 这样的机器学习模型，我们已经看到AI 可以产生各种各样的东西——从奇怪的生命形式到虚构的网红，再到完全陌生的、奇异的图像。

AI 作为一种技术对我们很有吸引力，因为我们天生就会被自己无法理解的东西所吸引。

And with neural networks processing data from thousands of other images made by people from every possible generation, every art movement, millions of images in one simple scan, they can produce visuals that are so familiar yet strikingly unfamiliar. More poetically, AI mirrors us.而且，通过神经网络处理来自每一代人、每一次艺术运动的成千上万张图像，以及一次简单扫描得到的百万张图像，AI 可以生成既熟悉又惊人陌生的视觉效果。

ai模型训练相关英文术语解释

ai模型训练相关英文术语解释1. Artificial Intelligence (AI): The theory and development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and problem-solving.2. Model Training: The process of teaching an AI model to learn patterns and make predictions or decisions by providing it with a large amount of training data and adjusting the model's internal parameters or structure.3. Training Data: The data used to train an AI model. It typically consists of input data and corresponding target output data that is used to guide the learning process.4. Labeling: The process of annotating or categorizing data for training an AI model. Labels provide ground truth information about the data and help the model learn to recognize patterns and make accurate predictions.5. Supervised Learning: A type of machine learning where the AI model is trained using labeled examples, meaning there is a known correct answer provided for each input data point.6. Unsupervised Learning: A type of machine learning where theAI model is trained using unlabeled data. The model is expected to find patterns or structures in the data without any explicit guidance.7. Reinforcement Learning: A type of machine learning where an AI model learns to make decisions or take actions in anenvironment to maximize a reward signal. The model learns through trial and error, receiving feedback on the quality of its actions.8. Neural Network: A type of model architecture inspired by the human brain. It consists of interconnected nodes (neurons) organized in layers, with each neuron performing a simple computation. Neural networks are commonly used in deep learning.9. Deep Learning: A subfield of machine learning that focuses on artificial neural networks with multiple layers. Deep learning allows for the learning of hierarchical representations of data, enabling the model to process complex patterns and relationships.10. Loss Function: A function that measures the discrepancy between the predicted outputs of an AI model and the true target outputs. During training, the model aims to minimize this discrepancy by adjusting its internal parameters.11. Gradient Descent: An optimization algorithm used to minimize the loss function in training an AI model. It calculates the gradient of the loss function with respect to the model parameters and updates them in the direction of steepest descent.12. Overfitting: A phenomenon that occurs when an AI model performs well on the training data but poorly on new, unseen data. It happens when the model becomes too specialized in capturing the noise or specific patterns of the training data, resulting in poor generalization.13. Hyperparameters: Parameters that define the configuration of an AI model and affect its learning process, but are not directly learned from the training data. They include parameters such as learning rate, number of layers, and activation functions.14. Validation Set: A portion of the training data that is set aside and not used for training the model. It is used to evaluate the performance of the model during the training process and tune the hyperparameters.15. Test Set: A separate dataset used to evaluate the final performance of the trained AI model. It consists of data that the model has never seen before and is used to assess the model's ability to generalize to new, unseen data.。

多模态模型中英文训练

多模态模型中英文训练In the realm of artificial intelligence, multimodalmodels have emerged as a critical tool for understanding and processing data from various sources. These models aretrained to interpret and integrate information from text, images, audio, and more, enhancing their ability to comprehend complex data sets.Training such models in English involves a nuanced understanding of the language's intricacies, including idiomatic expressions and contextual meanings. The process requires vast datasets and a deep learning architecture that can adeptly handle the linguistic subtleties of English.Conversely, training in Chinese presents its own set of challenges, such as the tonal nature of the language and the complexity of its characters. The model must learn to distinguish between homophones and understand the context in which characters are used to convey different meanings.The integration of both English and Chinese training is essential for models that aim to operate in multilingual environments. It demands a sophisticated approach to learning, where the model can switch between languages seamlessly, recognizing the unique features of each while maintaining a unified understanding.To achieve this, the training process must bemeticulously designed, with a focus on both the syntactic and semantic aspects of each language. This includes the use of parallel corpora, where equivalent sentences in both languages are used to align the models' understanding.Moreover, the model's performance is significantly influenced by the quality and diversity of the training data. It is crucial to include a wide range of examples that cover different domains and styles to ensure the model's robustness and adaptability.Finally, the success of multimodal models in a bilingual training context hinges on continuous evaluation and refinement. Regular assessments against real-world scenarios help identify areas for improvement and guide the model towards becoming more effective in handling multilingual and multimodal data.。

训练集测试集验证集英文

训练集测试集验证集英文The Importance of Training, Testing, and ValidationSets in Machine Learning.In the field of machine learning, the division of data into training, testing, and validation sets is crucial for ensuring the effective development and evaluation of models. Each set serves a distinct purpose in the machine learning workflow, and their integration is essential for achieving accurate and reliable results.Training Set.The training set is used to teach the machine learning model how to perform a specific task. It contains a subsetof labeled data, which the model uses to learn the underlying patterns and relationships between inputs and outputs. The model's parameters are adjusted based on the training data to minimize a predefined loss function, which measures the difference between the model's predictions andthe actual labels.During the training phase, the model's goal is to fit the training data as well as possible. However, it'scrucial to avoid overfitting, where the model performs poorly on new, unseen data because it has learned the noise or irrelevant details in the training set. To mitigate this issue, techniques such as regularization and dropout are often employed.Validation Set.The validation set serves as a middle ground between the training set and the testing set. It's used to evaluate the model's performance during the training process, allowing for adjustments to be made without corrupting the test set's integrity. The validation set helps in hyperparameter tuning, model selection, and early stopping to prevent overfitting.By monitoring the model's performance on the validation set, practitioners can assess how well it generalizes tounseen data. If the model's performance on the validation set stops improving, it's a signal to stop training to prevent overfitting. The validation set also allows for comparisons between different models or algorithms, enabling practitioners to choose the best-performing one.Testing Set.The testing set is used to assess the final performance of the trained model on unseen data. It's crucial to evaluate the model's performance on data it hasn't encountered during training or validation to ensure its generalization capabilities. The testing set should be completely separate from the training and validation sets and only used once at the end of the machine learning workflow.By comparing the model's predictions on the testing set to the actual labels, practitioners can calculate evaluation metrics such as accuracy, precision, recall, and F1 score. These metrics provide a quantitative measure of the model's performance and allow for comparisons withother models or benchmarks.Conclusion.The division of data into training, testing, and validation sets is fundamental to the success of machine learning projects. The training set teaches the model, the validation set helps in tuning and evaluating the model during training, and the testing set provides an unbiased assessment of the model's final performance. By leveraging these sets effectively, practitioners can develop accurate, robust, and reliable machine learning models that generalize well to new, unseen data.。

模拟ai英文面试题目及答案

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

小学五年级上册英语模拟卷(答案和解释)792

小学五年级上册英语模拟卷(答案和解释)(共50道题)下面有答案和解题分析一、综合题1.What is the opposite of "young"?A. OldB. NewC. TallD. Fast2.I ______ (watch) a movie with my family yesterday evening. We ______ (choose) a comedy film, and it ______ (make) us laugh a lot. After the movie, we ______ (talk) about the funny scenes.3.Jack woke up early this morning and got out of bed. He quickly went to the bathroom to wash his face and brush his teeth. After that, Jack went to the kitchen where his mom had already prepared breakfast. He sat down at the table and ate some __________, which was his favorite. His mom also gave him a glass of __________ to drink. After finishing his meal, Jack grabbed his __________ and put on his __________ before heading out the door to catch the school bus. Jack felt __________ because he was excited to see his friends at school.4.Yesterday, I __________ (1) to the supermarket with my mom. We __________ (2)a lot of things, like fruits, vegetables, and snacks. I __________ (3) to buy some chocolate, and my mom __________ (4) to buy some milk. After shopping, we__________ (5) home and __________ (6) lunch. It __________ (7) a very busy but productive day.5.She _______ (play) volleyball every weekend.6.Which of these is used for writing?A. BookB. PenC. ShoeD. Spoon7.They _______ (play / plays) soccer every weekend.8.I _______ (eat / eats / eating) dinner at 6 p.m.9.Which one is used to eat?A. KnifeB. PlateC. ForkD. Spoon10.Which one is a color?A. CircleB. BlueC. AppleD. Chair11.I _______ (have / has / had) a bike.12.Which of these is a color?A. BlueB. DogC. TableD. Chair13.Which one is a season of the year?A. MondayB. SummerC. JanuaryD. October14.I _______ (study) English every day after school. I _______ (read) a new book every week. My teacher _______ (give) us homework on Fridays, and we _______ (submit) it by Monday.15.Which of these is a healthy food?A. ChipsB. ChocolateC. AppleD. Soda16.What do we use to tell the time?A. PhoneB. ClockC. BookD. Chair17.Which of these is a month?A. MondayB. JanuaryC. SummerD. Winter18.She _______ (like / likes / liking) apples.19.We _______ (go) to the beach every summer. Last year, we _______ (stay) there for two weeks and _______ (enjoy) swimming and sunbathing.20.How many hours are in a day?A. TwentyfourB. TwelveC. TenD. Eight21.What do you use to cut food?A. ForkB. SpoonC. KnifeD. Plate22.I __________ (not/understand) this math problem. Can you __________ (help) me, please? I __________ (feel) confused. I __________ (need) some extra practice.23.What animal is the king of the jungle?A. LionB. TigerC. ElephantD. Monkey24.In the afternoon, I ______ (play) soccer with my friends. It ______ (be) sunny and warm. We ______ (enjoy) the game very much. After playing, we ______ (drink) water and ______ (rest) under a tree. I ______ (feel) happy.25.Which of these is a day of the week?A. SundayB. RedC. ChairD. Car26.He _______ (eat) breakfast at 7:00 AM every day.27.Which fruit is orange?A. AppleB. BananaC. OrangeD. Pear28.What do you drink when youre thirsty?A. FoodB. JuiceC. ShirtD. Pillow29.Which one is a color?A. RedB. AppleC. DeskD. Pencil30.Which of these is a body part?A. ChairB. HandC. TableD. Lamp31.Which of these is a color?A. TableB. BlueC. BookD. Chair32.Which fruit is yellow and has a curved shape?A. AppleB. BananaC. GrapeD. Orange33.In the summer, I _______ (like) to go swimming. Last summer, I _______ (swim) every day at the local pool. I also _______ (take) swimming lessons and _______ (learn) new techniques.34.What do we use to write on a whiteboard?A. PenB. ChalkC. MarkerD. Crayon35.They _______ (not understand) the lesson.36.What is the opposite of "day"?A. NightB. SunC. LightD. Moon37.He _______ (have) a lot of homework to do.38.Tom is my best friend. He is __________ (1) years old. He lives in a __________ (2) near the school. Every day, he __________ (3) to school on foot. Tom likes__________ (4) very much. After school, he goes to the __________ (5) to play football with his friends. He is very __________ (6) and likes to __________ (7) jokes. We always have a __________ (8) time together.39.I __________ (1) to play basketball after school. My friends __________ (2) with me, and we __________ (3) a lot of fun. We __________ (4) for one hour, and then we __________ (5) to a nearby store to buy drinks.40.My family ______ (live) in a small house near the beach. Every weekend, we______ (go) to the beach to swim and play volleyball. My brother ______ (like) to build sandcastles, and I ______ (enjoy) collecting seashells. We ______ (stay) there until the sun ______ (set).41.At school, we are learning about the ______ system. Our teacher told us that the Earth is the ______ planet from the Sun. We also learned that the ______ is the biggest planet, and that the ______ is closest to the Earth. After the lesson, we made models of the ______ system.42.Which one is a common pet?A. DogB. LionC. TigerD. Elephant43.Which one is a color?A. DogB. RedC. ChairD. Apple44.Which one is correct?A. She have a cat.B. She has a cat.C. She had a cat.D. She having a cat.45.Which one is a kind of tree?A. OakB. SpoonC. PlateD. Knife46.You _______ (be) my best friend.47.Which animal is known for its black and white stripes?A. GiraffeB. ZebraC. LionD. Elephant48.What shape is a ball?A. SquareB. CircleC. TriangleD. Rectangle49.Which of these is used to eat soup?A. SpoonB. KnifeC. PlateD. Fork50.We __________ (not, go) to the beach last summer because it __________ (rain) every day. Instead, we __________ (stay) at home and __________ (watch) movies. I __________ (hope) the weather __________ (be) better next year, so we __________ (plan) another trip to the beach.(答案及解释)。

模型超参数英文标准格式

模型超参数英文标准格式Hyperparameters are crucial parameters in machine learning models that are not learned through training but set by the user before training. These parameters control the behavior and performance of the model during training. Choosing the right hyperparameters is essential for achieving the best possible model performance. In this article, we will discuss some commonly used hyperparameters and their importance in machine learning models.1. Learning Rate: The learning rate controls how quickly the model learns from the training data. A low learning rate can result in slow convergence, while a high learning rate can cause the model to converge to suboptimal solutions or even diverge. It is important to choose an appropriate learning rate that balances convergence speed and accuracy.2. Number of Hidden Units/Layers: In neural networks, the number of hidden units and layers determines the model's complexity and capacity to learn complex patterns. Increasing the number of hidden units and layers can improve the model's ability to learn intricate relationships in the data but may also increase the risk of overfitting. The choice of the number of hidden units and layers depends on the complexity of the problem and the available computational resources.3. Regularization Strength: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. The regularization strength hyperparameter controls the amount of regularization applied. A higher regularization strength results in a simpler model with less overfitting but may also lead tounderfitting. Fine-tuning the regularization strength is necessary to find the right balance between bias and variance.4. Batch Size: During training, the data is divided into batches, and the model updates its parameters based on the loss calculated from each batch. The batch size hyperparameter determines the number of samples used in each update. A small batch size can result in noisy updates and slow convergence, while a large batch size may require more memory and computational resources. Choosing an appropriate batch size depends on the dataset size, available resources, and computational efficiency.5. Dropout Rate: Dropout is a regularization technique used in neural networks to prevent overfitting. It randomly sets a fraction of the input units to zero during each training step. The dropout rate hyperparameter controls the probability of dropping out each unit. A higher dropout rate increases regularization but may also decrease the model's capacity to learn. Experimenting with different dropout rates is necessary to determine the optimal value.6. Activation Function: The activation function determines the output of a neuron and introduces non-linearity into the model. Common activation functions include sigmoid, tanh, ReLU, and softmax. The choice of the activation function depends on the problem at hand and the characteristics of the data. For example, ReLU is often used in deep neural networks due to its ability to mitigate the vanishing gradient problem.7. Number of Iterations/EPOCHS: The number of iterations or epochs refers to the number of times the entire dataset is passedthrough the model during training. Increasing the number of iterations can potentially improve the model's performance, but it also requires more computational resources. Determining the optimal number of iterations can be done through techniques like early stopping, which stops training when the model's performance on a validation set starts to deteriorate.These are just a few examples of hyperparameters that can significantly impact the performance of machine learning models. Proper tuning of hyperparameters is essential for achieving optimal results. It usually involves manual experimentation or automated techniques like grid search or random search. Understanding the effect and importance of each hyperparameter is crucial for successful model training.。

计算机视觉读后感

Computer vision姓名：学号；专业：1 introductionObject segmentation in the presence of clutter and occlusions is a challenging task for computer vision and cross-media. Without utilizing any high-level prior information about expected objects, purely low-level information such as intensity, color and texture does not provide the desired segmentations. In numerous studies , prior knowledge about the shapes of the objects to be segmented can significantly improve the final reliability and accuracy of the segmentation result. However, given a training set of arbitrary prior shapes, there remains an open problem of how to define an appropriate prior shape model to guide object segmentation.Early work on this problem is the Active Shape Model (ASM), which was developed by T. Cootes et al.. The shape of an object is represented as a set of points. These points can represent the boundary or significant internal locations of the object. The evolutional shape is constrained by the point distribution model which is inferred from a training set of shapes. However, these methods suffer from a parameterized representation and the manual positioning of the landmarks. Later, level set based approaches have gained significant attention toward the integration of shape prior into variational segmentation . Almost all these works optimize a linear combination of a data-driven term and a shape constraint term. Data-driven term aims at driving the segmenting curve to the object boundaries, and shape constraint term restricts possible shapes embodied bythe contourThere are many ways to define the shape constraint term. Simple uniform distribution , Gaussian densities ,non-parametric estimator , manifold learning ,and sparse representation were considered to model shape variation within a training set. However, most methods are recognition-based segmentation. They are suitable for segmenting objects of a known class in the image according to their possible similar shapes. If the given training set of shapes is large and associated with multiple different object classes, the statistical shape models and manifold learning do not effectively represent the shape distributions due to large variability of shapes. In addition, global transformations like translation, rotation and scaling and local transformations like bending and stretching are expensive to shape model in image2 Deep Learning Shape PriorsRecently, deep learning models，are attractive for their well performance in modeling high-dimensional richly structured data. A deep learning model is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. The deep Boltzmann machine (DBM) has been an important developmentin the quest for powerful deep learning models . Applications that use deep learning models are numerous in computer vision and information retrieval, including classification , dimensionality reduction , visual recognition tasks , acoustic modeling , etc. Very recently, a strong probabilistic model called Shape Boltzmann Machine (SBM) is proposed for the task of modeling binary object shapes. This shape generative model has the appealing property that it can both generate realistic samples and generalize to generate samples that differ from shapes in the training set.A Restricted Boltzmann Machine (RBM) is a particular type of Markov Random Field (MRF) that has a two-layer architecture, in which the visible units are connected to hidden units. A Deep Boltzmann Machine (DBM) is an extension of the RBM that has multiple layers of hidden units arranged in layers . In general, the shape prior can be simply described as two levels of representation: low-level local features (like edges or corners) and high-level global features (like object parts or object). Low-level local features with good invariant properties can be re-used in different object samples. On the other hand, high-level global features describe the image content, and they are more appropriate to cope with occlusion, noise, and changes on the object pose. In order to learn a model that accurately captures the global and local properties of binary shapes. We use three-layered DBM to automatically extract the hierarchical structure of shape data.In DBM, the learned weights and biases implicitly define a probability distribution over all possible binary shapes via the energy . Moreover, this three-layered learning can effectively capture hierarchical structures of shape priors. Lower layers detect simple local features of shape and feed into higher layers, which in turn capture more complex global features of shape. Once binary states have been chosen for the hidden units, a shape generative model can be inferred by conditional probability. Since such generative shape is defined by probability, we adopt Cremers’s shape relaxed method [8] to replace the 2D visible vector _ with a shape _ of probabilistic representation, and define a shape constraint term in the following energetic form.In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used to model shape variations of global and local structures in an energetic form. Finally, it is applied to data-driven variational methods to perform object extraction of corrupted data based on shape probabilistic representation.3 Trust Region FrameworkTrust region is a general iterative optimization framework that in some sense is dual to the gradient descent, While gradient descent fixes the direction of the step and then chooses the step size, trust region fixes the step size and then computes the optimal descent direction, as described below.At each iteration, an approximate model of the energy is constructed near the current solution. The model is only “trusted” within some small ball around the current solution, a.k.a. “trust region”. The global minimum of the approximate model within the trust region gives a new solution. This procedure is called trust region sub-problem. The size of the trust region is adjusted for the next iterationbased on the quality of the current approximation. Variants of trust region approach differ in the kind of approximate model used, optimizer for the trust region subproblem, and a merit function to decide on the acceptance of the candidate solution and adjustment of the next trust region size. For a detailed review of trust region methods this， paper propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with nonlinear regional terms, which are known to be challenging for existing algorithms. These energies include, but are not limited to, KL divergence and Bhattacharyya distance between the observed and the target appearance distributions, volume constraint on segment size, and shape prior constraint in a form of distance from target shape moments. Our method is 1-2 orders of magnitude faster than the existing state-of-the-art methods while converging to comparable or better solutions.4 The Short Boundary Bias and Coupling EdgesWe first introduce the standard pairwise Markov random field (MRF) model for interactive segmentation and the extension Although a large number of neighbouring pixels in such images may take different labels, the majority of these pixels has a consistent appearance. most pixel pairs along the object boundary have a consistent (brown-white) transition. Therefore, proposed a priorthat penalizes not the length but the diversity of the object boundary, i.e., the number of types of transitions. This new potential does not suffer from the short-boundary bias.MAP Inference. Inferring the Maximum a Posteriori (MAP) solution of the models above corresponds to minimizing their respective energy functions. It is well known that the energy function (1) is submodular if all _(Ii; Ij) _0 and can then be minimized in polynomial time by solving an (s;t)-mincut problem [4]. In contrast, the higher order potential (5) makes MAP inference in general NP hard, and therefore [11] proposed an iterative bound minimization algorithm for approximate inference. We show next that higher order potentials of the form (5) can be converted into a pairwise model by the addition of some binary auxiliary variables.One of the key challenges posed by higher-order models is efficient MAP inference. Since inference in pair wise models is very well studied, one popular technique is to transform the higher-order energy function into that of a pair wise random field. In fact, any higher-order pseudo boolean function can be converted to a pair wise one, by introducing additional auxiliary random variables Unfortunately, the number of auxiliary variables grows exponentially with the arity of the function, and in practice this approach is only feasible for higher-order functions with few variables. If however the higher-order function contains inherent “structure”, then MAP inference can be practically feasible even with terms that act on thousands of variables [14, 12, 25, 29]. This is the case for the edge coupling potentials4，Co-segmentation.In this paper, we propose a novel correspondence-based object discovery and co-segmentation algorithm that performs well even in the presence of many noise images. Our algorithm automatically discovers the common object among the majority of images and computes a binary object/ background label mask for each image. Images that do not contain the common object are naturally handled by returning an empty labeling Our algorithm is designed based on the assumption that pixels (features) belonging to the common object should be: (a) salient, i.e. dissimilar to other pixels within their image, and (b) sparseCo-segmentation was first introduced by Rother et al. , who used histogram matching to simultaneously segment the same object in two different images. Co-segmentation was also explored in weakly supervised setups with multiple object categories . While image annotations may facilitate object discovery and segmentation, image tags are often noisy, and bounding boxes or class labels are usually unavailable. In this work we show that it is plausible to automatically discover visual objects from the Internet using image search alone. Let I = {I1, . . . , IN} be the image dataset consisting of N images. Our goal is to compute the binary masks B = {b1, . . . , bN}, where for each image Ii and pixel x = (x, y),bi(x) = 1 indicates foreground (the common object), and bi(x) = 0 indicates background (not the object) at location x.The saliency of a pixel or a region in an image can be defined in numerous ways and extensive research in computer and human vision has been devoted to this topic. In our experiments, we used an off the-shelf saliency measure—Cheng et al.’s Contrast-based Saliency —that produced sufficiently good saliency estimates for our purposes, but our formulation is not limited to a particular saliency measure and others can be usedthe saliency of a pixel based on its color contrast to other pixels in the image (how different it is from the other pixels). Since high contrast to surrounding regions is usually a stronger evidence for saliency of a region than high contrast to far away regions, they weigh the contrast by the spatial distances in the image.Formally, let wij denote the flow field from image Ii to image Ij . Given the binary masks bi, bj , the SIFT flow objective function becomesE (wij ; bi, bj) =_x∈Λibi(x)_bj(x + wij(x)) _Si(x) − Sj(x + wij(x))_1+(1 − bj(x + wij(x))C0 +_y∈Nixα _w(x) − w(y)_2_where Si are the dense SIFT descriptors of image Ii, x _→ _x_p is the Lp distance for p = 1 and 2, Λi is image Ii’s lattice, Nix is the neighborhood of x, α weighs the smoothness term, and C0 is a large constant. We then denote byWthe set of all pixel correspondences in the dataset: W= ∪Ni=1∪Ij∈Ni wij .The key insight to our algorithm is that common object patterns should be salient within each image, while being sparse with respect to smooth transformations across images. We propose to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. We performed extensive numerical evaluation on established co-segmentation datasets, as well as several new datasets generated using Internet search. Our approach is able to effectively segment out the common object for diverse object categories, while naturally identifying images where the common object is not present6 Technical ApproachThe overall approach to image segmentation embodied in this work is inspired by the work on gPb [3, 2] in that it starts with a set of edges derived from local pixel differences and then invokes a globalization procedure which strengthens or weakens those edges based on an analysis of the eigenvectors produced by a normalized cuts procedure.The procedure consists of three main processing stages：The first step in the procedure is an edge extraction stage which produces a set of edgels. This system employs a variant 哦f the method proposed by Meer and Georgescu which can be thought of as computing the normalized cross correlation between each pixel window and a set of oriented edge templates. In this work we modify this edge detection scheme by introducing an additional factor in the denominator which is based on the average response to the template. This modification serves to reintroduce some contrast information into the edge response so that larger steps have a greater response than smaller ones and slight variations in low contrast regions are not unduly amplified. second，In the original formulation of the Normalized Cuts segmentation procedure the principal goal is to minimize the Rayleigh quotient，Our aim then is to construct a matrix,L, whose column span captures the variation we expect in the eigenvectors of the orginal system. Once the L matrix has been constructed we can turn our attention to solving the reduced order eigensystem，We first note that this system is much smaller than the original system since m is on the order of 5000 where n was on the order of 115000 for the images in our test set. The approach advocated in this paper leverages the observation that in this image segmentation task the edge signal provides useful information about the final structure of the eigen problem. By constructing a basis tailored to the content of the image we are able to identify a subspace that captures the nuances of the edges and the details found in the full system7 summaryS egmentation, the problem of breaking a given image into salient regions, is one of the most fundamental issues in Computer Vision and a number of approaches have been advanced to accomplish this task. Among these schemes, three methods have proven to be quite popular in practice owing to their performance and/or their running time. The Mean Shift method of Comaniciu and Meer , the Normalized Cuts method developed by Shi and Malik [14] and the graph based method of Felzenswalb and Huttenlocher. More Recently Arbelaez, Maire, Fowlkes and Malik have proposed an impressive segmentation algorithm that achieves state of the art results on commonly available data sets. Their gPb method starts with a local edge extraction procedure which has been optimized using learning techniques. The results of this edge extraction step are then used as input to a spectral partitioning procedure which globalizes the results using Normalized Cuts. This globalization stage helps to focus attention on the most salient edges in the scene.Refers[A} Fei Chen， Hui min Yu， Roland Hu ,Xun xun Zeng ，Deep Learning Shape Priors for Object Segmentation, in CVPR, 2013.[B] Lena Gorelick, Frank R. Schmidt, Yuri Boykov ，Fast Trust Region for Segmentation，in CVPR, 2013.[C] Pushmeet Kohli，Anton Osokin，Stefanie Jegelka，A Principled Deep Random Field Model for Image Segmentation，in CVPR, 2013.[D] Michael Rubinstein，Armand Joulin，Johannes Kopf，Ce Liu ，Unsupervised Joint Object Discovery and Segmentation in Internet Images，in CVPR, 2013.[E] Camillo Jose Taylor ，GRASP Laboratory，Towards Fast and Accurate Segmentation，in CVPR, 2013.。

我想要当一名乐高设计师的英语作文

我想要当一名乐高设计师的英语作文全文共6篇示例，供读者参考篇1My Dream to Become a LEGO DesignerEver since I was a little kid, I've been obsessed with LEGO bricks. There's just something so magical about those colorful plastic pieces that lets my imagination run wild! When I play with LEGOs, I can build anything I can dream up – from towering castles and soaring spaceships to teeny-tiny houses for my LEGO people. No matter how crazy my ideas are, I can make them come to life with LEGOs.My LEGO obsession started when I was around 4 years old. My parents got me a big bucket of basic bricks for my birthday, and I was hooked from the first click of those iconic studs locking together. I spent hours upon hours just building random creations, not following any instructions at all. I loved how the possibilities were truly endless with LEGOs. One day I could make a car, and the next I could tear it all apart to build a pirate ship instead!As I got older, I started getting more advanced LEGO sets with instruction booklets to follow. Those were fun too, as I marveled at how the master LEGO designers were able to craft such cool models with just basic bricks and cleverly designed pieces. I'd study the instruction books intently, in awe of how each step slowly transformed a pile of bricks into an epic spaceship, detailed city scene, or realistic vehicle.But even when following the instructions, I could never resist putting my own creative spin on the models. I'd use extra pieces to add my own embellishments, incorporating fun elements like colorful laser blasts and quirky characters. Some of my favorite LEGO creations have been mashups – like the time I combined pieces from a space station set and a medieval castle set to build a crazy sci-fi fortress guarded by knights wielding laser swords!As my LEGO skills have progressed over the years, I've become absolutely determined to make my childhood dream a reality: I want to be an official LEGO designer when I grow up. Just imagining getting to dream up and design new LEGO sets for a living fills me with pure joy. What could possibly be a cooler job than that?!I know becoming a LEGO designer won't be easy though. For one thing, the LEGO company is pretty small, so there aren't thatmany designer jobs available. And I'm sure the competition is really fierce, with tons of other LEGO fanatics out there vying for those coveted positions. Not to mention I'll need to get really good at 3D modeling software, math, engineering principles, and all the technical skills involved in LEGO design.But I'm not letting any of that discourage me! My LEGO passion burns too brightly to be extinguished. I've already started teaching myself how to use 3D modeling programs on the computer, and I'm devouring every book and video I can find about LEGO design and the design process. Math and science might not be my favorite subjects in school, but you'd better believe I'm working twice as hard to master those skills, since I know they'll be crucial for my dream career.I'm also constantly coming up with new LEGO set ideas and building prototypes and concept models. Thatmedieval-meets-space fortress I built last year sparked an idea for an entire medieval sci-fi LEGO theme, with castles, starships, laser-wielding knights, and more. I've fleshed the idea out into an entire story world, dreaming up a bunch of awesome set concepts and making notes about unique LEGO pieces that could bring this world to life. Maybe I'm getting a bit ahead ofmyself, but hey – you've gotta dream big if you want to design LEGOs for a living, right?In the meantime, I'm doing everything I can to prepare for my future LEGO career. I enter LEGO design competitions whenever I can, using that as an opportunity to practice proposing set ideas and building prototypes. I've even started my own LEGO YouTube channel where I showcase my custom creations and model ideas. It's still a pretty small channel, but I'm having a blast making videos about my LEGO adventures.While becoming a professional LEGO designer is my main goal, I know there are lots of other potential careers out there that could allow me to work with LEGOs in some capacity. Maybe I could be a LEGO artist, making larger-than-life LEGO sculptures and displays. Or I might look into jobs at the LEGOLAND theme parks or LEGO retail stores. Heck, I could even become a LEGO teacher, helping inspire the next generation of builders and makers! As long as I get to pursue my passion for LEGOs in some way, I know I'll be happy.But for now, my number one dream is still to one day be an official LEGO designer. I can't wait to see what kinds of crazy, imaginative new sets and pieces I'll get to dream up. Will I design the next big LEGO theme that kids around the world go crazy for?Maybe I'll create a buildable set that actually walks and talks using LEGO robotics! Or who knows, maybe I'll one day help revolutionize the concept of LEGO bricks entirely, coming up with a whole new way to build and create that makes everyone's inner child feel that LEGO magic all over again.No matter what LEGO challenges and triumphs may lie ahead, I know this: When I put my mind to something, especially something I'm as passionate about as LEGOs, there's no limit to what I can build. The possibilities are infinite with my favorite little plastic bricks. So you can count on me working as hard as I can to turn my wildest LEGO dreams into reality!篇2Title: My Dream of Becoming a Lego DesignerEver since I was a tiny little kid, I've been completely obsessed with Legos. I can still remember the first time my parents gave me a Lego set – it was a small red bucket filled with colorful bricks of all different shapes and sizes. From the moment I opened that bucket and let the pieces spill out onto the floor, I was hooked. I spent hours upon hours building anything and everything my imagination could dream up.At first, my creations were pretty basic – a simple tower here, a little house there. But as I got older and my skills improved, my Lego models became more and more elaborate. I started following the instruction booklets that came with the bigger sets, carefully piecing together each section until the final product was complete. I built castles with working drawbridges, spaceships that could actually fly (well, glide across the room at least!), and entire cities with skyscrapers and parks and roads.My favorite part, though, was when I went totally freestyle and just built whatever crazy contraption popped into my head without any instructions at all. I'd mix and match pieces from a dozen different sets, coming up with weird and wonderful inventions that didn't make any sense but looked totally awesome. Like this one time I made a robot dinosaur that could shoot sparks out of its mouth. Or the floating underwater palace I designed for the mermaids and sharks to live in. My bedroom was always a gigantic mess, littered with thousands of loose Lego bricks in every color imaginable. But I didn't care – to me, it was the most beautiful kind of chaos.The more I built, the more I fell in love with the endless possibilities of those simple plastic bricks. I was amazed by how many different things could be created just by arranging thesame basic shapes in new and clever ways. Legos weren't just toys to me – they were an entire world of imagination and creativity just waiting to be explored.That's when I decided that I wanted to be a Lego designer when I grew up. I loved the idea of getting to dream up brand new Lego sets and bring them to life, brick by brick. Of coming up with awesome new characters and vehicles and buildings that kids everywhere could build and play with. It seemed like the absolute coolest job in the world to me.So I started doing everything I could to prepare myself for my future career. I read books and watched videos about how Legos were first invented and how the design process worked. I practiced sketching out my ideas for new sets, planning every tiny detail down to the last multi-colored brick. I even wrote letters to the Lego company itself, sharing my ideas and telling them why they should hire me as soon as I was old enough.Math – Understanding geometry, fractions, and measurements is crucial for designing 3D Lego models that actually work when you build them.Art – Having a good eye for colors, shapes, and overall visual design makes for way cooler and more appealing Lego sets.Problem-solving – When you're creating something new, you have to get really creative to figure out solutions to any roadblocks along the way.Communication – It's important to be able to clearly explain your ideas and visions, both in writing and by drawing diagrams.Teamwork – Most big Lego sets are designed by whole groups working together, so you've got to be a team player.Whenever I wasn't busy with schoolwork and other activities, I was busy practicing my Lego design skills. Any free time I had, you could find me hunched over my latest creation, brow furrowed in concentration as I carefully connected each new piece.All that practice really paid off too – I just keep getting better and better at dreaming up the coolest new Lego concepts. Like this one idea I have for a giant Lego robot that can actually walk and pick things up and do really basic tasks. Or my plan for a modular Lego home with rooms you can rebuild and rearrange however you want. Someday, if I'm lucky enough to become a real Lego designer, I'd love to bring ideas like those to life for kids all over the world to enjoy.Now, I know becoming a professional Lego designer is going to be really, really difficult. The company gets tons of applicants, and they can only hire the best of the best. But I'm not giving up on my dream that easily! I'm going to keep studying and practicing and working my hardest, because in my mind, there's no better job than getting paid to play with Legos all day. Maybe if I follow my passion and never lose my childlike sense of wonder and imagination, I'll somehow find a way to make it happen.So yeah, while other kids want to grow up to be doctors or astronauts or professional athletes, I just want to be a Lego designer. To me, that's a way bigger dream than any of those other careers. Because what could be more incredible than having the chance to actually build happiness, day after day? To create something that brings smiles to kids' faces and lets their imaginations run wild? That's the kind of job I'd never get tired of, no matter how old I get.I'm going to chase this dream with everything I've got. And who knows? Maybe someday in the future when you're browsing the Lego aisle, you'll spot a brand new set and think "Wow, that looks awesome! I wonder who the genius was who designedthat?" Well, if I have anything to say about it, that genius designer will be me.篇3I Want to Be a LEGO DesignerEver since I was a tiny tot, I've been completely mesmerized by the magic of LEGO bricks. Those colorful little plastic pieces have been the source of countless hours of fun and creativity for me. Who knew that simple bricks could unlock such an amazing world of imagination?When I was really little, maybe around four or five years old, I would sit on the floor of my room and spend what felt like entire days building and rebuilding with my LEGO sets. I remember my first set vividly – it was a little red car with a few extra bricks to let my creativity run wild. I built that car over and over again, following the instructions carefully at first, but then starting to experiment and put my own twist on the design.As I got older, my love for LEGO only grew stronger. Each year for my birthday and during the holidays, I would beg my parents for new LEGO sets. The bigger and more complex, the better! I loved the challenge of following those multi-step instruction booklets, carefully connecting each piece to bring themodel to life. There was something so satisfying about putting in the hard work and ending up with an awesome creation at the end.My favorite LEGO themes have always been the ones that let me build whole worlds and scenes. The City line with its police and fire stations, houses, cars, and parks has provided me with endless storytelling possibilities. I've spent rainy afternoons acting out dramatic rescues and daring capers with my LEGO City sets. And who could forget about the classic Space line? Building rockets, astronauts, and lunar bases has allowed my imagination to quite literally soar beyond our planet!As much as I love the pre-designed LEGO sets, there's something even more magical about free-building without instructions. Just a pile of random bricks and the limitless potential to create anything I can dream up. Some of my proudest LEGO accomplishments have been the unique models and scenes I've built from scratch. Once, I spent weeks slowly putting together a massive medieval castle, complete with a working drawbridge, towering turrets, and a tiny LEGO kingdom's worth of citizens. Another time, I painstakinglyre-created a life-sized model of our family dog using nothing but those brilliant bricks.LEGO has fueled my creativity and passion for building in a way that few other toys could. With LEGO, the possibilities are truly endless. I can build anything from the tales of fantasy and science fiction found in books and movies, or I can craft something completely new that has never existed before. There's no limit to what can be designed and constructed with those wonderful little bricks.That's why I've decided that I want to become a LEGO Designer when I grow up. To have the best job in the world –getting paid to play with LEGO all day and to dream up new sets and creations that will inspire other kids like me. I can't think of anything cooler than that!As a LEGO Designer, I would get to develop incredible new themes and decide which pieces and colors get included in upcoming sets. I could create the next awesome spaceships, pirate galleons, or dragon-guarded castles for other LEGO lovers to build. I would carefully craft the instruction booklets that provide the step-by-step guides for each model, making sure to include clever building techniques and fun play features. Maybe I'd even get to design some brand new LEGO pieces that have never existed before!I already have tons of ideas for new sets and themes that I can't wait to pitch if I become a LEGO Designer. For example, how awesome would it be to have an entire line of LEGO sets based on dinosaurs? They could range from friendly herbivores exploring lush prehistoric jungles to fearsome T-Rexes stalking their prey. Or what about LEGO models that bring creatures from myth and legend to life – like soaring griffins, powerful minotaurs, or wise sphinx guardians? I can picture it all so clearly in my mind's eye, just waiting to be transformed into plastic brick form.I'm sure I'll need to go to a special college to get trained in the skills I'll need, like 3D modeling, product design, and engineering. And I'll have to create a truly jaw-dropping portfolio that shows off my very best LEGO creations and design ideas. But I know that if I stay dedicated and never lose my passion for LEGO, anything is possible. My dream job of getting paid to play with LEGO bricks every single day is absolutely worth the effort.In the meantime, I'm not going to let my young age stop me from preparing to become a LEGO Designer. I've already started collecting random bricks in plenty of colors and experimenting with building my own original creations without instructions. I'vebeen sketching out ideas and concepts in a special notebook, adding to it whenever inspiration strikes. I even taught myself some coding so I could start to tinker with digital LEGO design software. You're never too young to start pursuing your dreams!LEGO bricks have been such a huge part of my life already, sparking my imagination and providing me with indescribable joy through the act of building. Becoming a LEGO Designer would allow me to be one of the special people who get to create that same magic and spread it to kids and adults around the world. Getting to put smiles on the faces of LEGO fans everywhere and enable fantastic new worlds of construction and creativity – that's my ultimate dream job! I'm going to work as hard as I can, never stop believing in myself, and one day soon I'll hopefully see my own LEGO set designs taking shape and coming to life. The playful possibilities would be endless!篇4当我长大后，我想要成为一名乐高设计师！你知道吗？乐高是我最喜欢的玩具之一。

管理信息系统理论与应用判断选择

Chapter 1True—False Questions1。

Developing a new product, fulfilling an order，or hiring a new employee are examples of business processes。

T2。

The dimensions of information systems are management, organizations，and information technology。

T3。

There are four major business functions：Sales and marketing; manufacturing and production; finance and accounting; and information technology。

F4。

In the behavioral approach to information systems, technology is ignored in favor of understanding the psychological，social，and economic impacts of systems.F5.A business model describes how a company produces, delivers，and sells a product or service to create wealth。

TMultiple-Choice Questions1.The six important business objectives of information technology are new products, services，and business models; customer and supplier intimacy；survival; competitive advantage；operational excellence；and：Ba。

地下水模拟软件GMS中文使用手册

2.1.1 纲要....................................................................................................................................... 17
2.2 开始.............................................................................................................................................. 18 2.3 属性对象...................................................................................................................................... 18
1.12.1 创建概念模型..................................................................................................................... 13 1.12.2 根据 GIS 数据作图............................................................................................................. 13
2.4 结论.............................................................................................................................................. 24 25 3 MODFLOW—概念模型法................................................................................................................ ................................................................................................................25 3.1 简介.............................................................................................................................................. 26

multitask learning code

multitask learning codeMultitask Learning: A Powerful Approach to Enhancing Machine Learning ModelsIntroductionIn the field of machine learning, multitask learning (MTL) has gained significant attention and recognition as a powerful approach to training models that can perform multiple related tasks simultaneously. Unlike traditional single-task learning, where models are trained to perform a specific task, MTL focuses on leveraging the shared information between tasks to improve overall performance and generalize better.What is Multitask Learning?Multitask learning can be defined as a machine learning paradigm that involves training a model to perform multiple tasks simultaneously. These tasks are often related or have some underlying connection, which enables the model to learn from the shared information between them. By jointly optimizing the parameters of multiple tasks, MTL aims to improve performance on each task individually.Advantages of Multitask Learning1. Improved Generalization: By leveraging shared information and features, MTL has been shown to improve the generalization capabilities of models. When tasks are related, the knowledge gained from one task can be transferred to others, leading to better overall performance.2. Data Efficiency: MTL allows for improved data efficiency since models can benefit from the additional information of related tasks. With limited labeled data, training a multitask model can be more effective than training separate models for each task.3. Reduced Overfitting: MTL can help in reducing overfitting, a common problem in single-task learning. By regularizing the shared parameters across tasks, MTL encourages the model to learn more robust representations that generalize better to unseen data.4. Implicit Feature Selection: Multitask learning can also implicitly perform feature selection by learning which features are useful for multiple tasks. If a feature is consistently helpful across different tasks, the model will assign it a higher importance, enhancing its predictive power.Implementing Multitask Learning with CodeTo implement multitask learning, we need to consider the following steps:1. Data Preparation: Gather and preprocess the data for each task. Ensure that the data for different tasks is appropriately labeled and split into training, validation, and test sets.2. Model Architecture: Design a neural network architecture that can accommodate multiple tasks. This architecture should have shared layers to capture the common features and task-specific layers to capture task-specific information.3. Loss Function: Define a loss function that combines the losses of individual tasks. The loss function should take into account the relative importance of each task. Common approaches include weighted sum, weighted average, or using separate loss terms for each task.4. Training: Train the multitask model using the prepared data and defined loss function. Use optimization algorithms such as stochastic gradient descent (SGD) or Adam to update the parameters iteratively. Monitor the performance on each task during training.5. Evaluation: Evaluate the trained model on the test set to assess its performance on each individual task. Compare the multitask model's performance with single-task models to validate the advantages of MTL.ConclusionMultitask learning is a valuable approach to enhance machine learning models by training them to perform multiple related tasks simultaneously. By leveraging shared information and features, MTL improves generalization, data efficiency, and reducesoverfitting. Implementing multitask learning requires careful data preparation, designing a suitable model architecture, defining an appropriate loss function, and training and evaluating the model accordingly. By adopting multitask learning techniques, researchers and practitioners can develop more robust and versatile machine learning models that excel in various domains and applications.。

深度学习模型中随机种子的影响说明书

torch.manual seed(3407)is all you needtorch.manual seed(3407)is all you need:On the inﬂuence of random seeds in deep learning architecture for computervisionDavid Picard******************** LIGM,´Ecole des Ponts,77455Marnes la vall´e e,FranceAbstractKeywords:Deep Learning,Computer Vision,Randomness1.IntroductionIn this report,I want to test the inﬂuence of the random generator seed on the results of ubiquitous deep learning models used in computer vision.I ask the following question:1.What is the distribution of scores with respect to the choice of seed?2.Are there black swans,i.e.,seeds that produce radically diﬀerent results?3.Does pretraining on larger datasets mitigate variability induced by the choice of seed?I feel this questions are important to ask and test because with the rise of large training sets and large deep learning models,it has become common practice in computer vision(and to other domains relying heavily on machine learning)to report only a single run of the experiment,without testing for its statistical validity.This trend started when computing power was limited,and it is perfectly understandable that the result of a single experiment is better than no result at all.To the best of my understanding,this seems to happen all the time in physics-ﬁrst results get quickly announced,and are later conﬁrmed or denied. The deep learning community has what may be a faulty approach in that it never conﬁrms results by running as many reproductions as required.Just as the noise in the measurement of a physical experiment,we know there are random factors in a deep learning experiment, like the split between training and testing data,the random initialization,the stochasticity of the optimization process,etc.It has been shown in Mehrer et al.(2020)that these aﬀect the obtained networks such a way that they diﬀer measurably.I strongly believe it is important to have a sense of the inﬂuence of these factors on the results in terms of evaluation metrics.This is essentially what was done by Bouthillier et al.(2021)on diﬀerent tasks,including computer vision,but limited to smaller datasets and only200runs.Instead of performing the tedious work of measuring the inﬂuence of each source of randomness at larger scales,I propose instead to ask the much simpler questions above that are based on the choice of a random seed.The variations I measure are thus the accumulation of all random factors relying on the random seed and if they are small enough,then the corresponding random factors have negligible inﬂuence.On the contrary,if they are largeD.Picardenough to not be negligible,then I believe deep learning publications should start to include a detailed analysis of the contribution of each random factor to the variation in observed performances.The remaining of this report is organized as follows:First,I detail the experimental setup used and motivate my choice.Then I discuss the limitations of this experiments and how they aﬀect the conclusions we can make from it.After that,I show theﬁndings with respect to each question with the chosen experimental setup.Finally,I draw some conclusions from thisﬁndings bounded by the limitations exposed earlier.2.Experimental setupBecause this is a simple experiment without the need to produce the best possible results, I allowed myself a budget of1000hours of V100GPU computing power.This budget is nowhere near what is currently used for most of major computer vision publications,but it is nonetheless much more than what people that do not have access to a scientiﬁc cluster can ever do.All the architectures and the training procedures have thus been optimize to ﬁt this budget constraint.This time was divided into half forﬁnding the architectures and training parameters that would allow a good trade-oﬀbetween accuracy and training time;and half to do the actual training and evaluation.The time taken to compute statistics and produceﬁgures was of course not taken into account.To ensure reproducible fondings,all codes and results are publicly available1.2.1CIF AR10For the experiments on CIFAR10,I wanted to test a large number of seeds so training had to be fast.I settled on an operating point of10000seeds,each taking30seconds to train and evaluate.The total training time is thus5000minutes or about83hours of V100 computing power.The architecture is a custom ResNet with9layers that was taken from the major results of the DAWN benchmark,see Coleman et al.(2019),with the following layout:C364−C3128−M2−R[C3128−C3128]−C3256−M2−C3256−M2−R[C3256−C3256]−G1−L10where C kd is a convolution of kernel size k and d output dimension followed by a batchnormalization and a ReLU activation,M2is a max pooling of stride2,R[·]denote a skip connection of what is inside the brackets,G1is a global max pooling and L10is a linear layer with10output dimensions.The training was performed using a simple SGD with momentum and weight decay. The loss was a combination of a cross-entropy loss with label-smoothing and the regular categorical cross-entropy.The learning rate scheduling was a short increasing linear ramp followed by a longer decreasing linear ramp.1.https:///davidpicard/deepseedtorch.manual seed(3407)is all you needTo make sure that the experiment on CIFAR10was close to convergence,I ran a longer training setup of1minute on only500seeds,for a total of just over8hours.The total training time for CIFAR was thus just over90hours of V100computing time.2.2ImageNetFor the large scale experiments on ImageNet,it was of course impossible to train commonly used neural networks from scratch in a reasonable amount of time to allow gathering statis-tics over several seeds.I settled on using pretrained networks where only the classiﬁcation layer is initialized from scratch.I use three diﬀerent architectures/initializations:•Supervised ResNet50:the standard pretrained model from pytorch;•SSL ResNet50:a ResNet50pretrained with self-supervision from the DINO repository, see Caron et al.(2021);•SSL ViT:a Visual transformer pretrained with self-supervision from the DINO repos-itory,see Caron et al.(2021).All training were done using a simple SGD with momentum and weight decay optimiza-tion,with a cosine annealing scheduling that was optimized to reach decent accuracy in the shortest amount of time compatible with the budget.The linear classiﬁer is trained for one epoch,and the remaining time is used forﬁne-tuning the entire network.All model were tested on50seeds.The supervised pretrained ResNet50had a training time of about2hours,with a total training time of about100hours.The SSL pretrained ResNet50had a training time of a little over3hours for a total of about155hours of V100 computation time.This longer time is explained by the fact that it required more steps to reach comparable accuracy when starting from the SLL initialization.The SSL pretrained ViT took about3hours and40minutes per run,for a total of about185hours of V100 computation time.This longer training time is also explained by the higher number of steps required,but also by the lower throughput of this architecture.The total time taken on ImageNet was about440hours of V100computing time.3.LimitationsThere are several limitations to this work that aﬀect the conclusion that can be drawn from it.These are acknowledged and tentatively addressed here.First,the accuracy obtained in these experiments are not at the level of the state of the art.This is because of the budget constraint that forbids training for longer times.One could argue that the variations observed in these experiments could very well disappear after longer training times and/or with the better setup required to reach higher accuracy. This is a clear limitation of my experiments-caused by necessity-and I can do very little to argue against it.Models are evaluated at convergence,as I show in the next section.On the accuracy on CIFAR10,the long training setup achieves an average accuracy of90.7% with the maximum accuracy being91.4%,which is very well below the current state of the art.However,recall that the original ResNet obtained only91.25%with a20layers architecture.Thus,my setup is roughly on par with the state of the art of2016.ThisD.Picardmeans papers from that era may have been subject to the sensitivity to the seed that I observe.Also,Note that the DAWN benchmark required to submit50runs,at least half of which had to perform over94%accuracy to validate the entry.In these test a good entry2 can achieve a minimum accuracy of93.7%,a maximum accuracy of94.5%while validating 41runs over94%accuracy.Thus,even with higher accuracy,there is still a fair amount of variation.Although it seems intuitive that the variation lessens with improved accuracy,it could also be that the variations are due to the very large number of seeds tested.For ImageNet,the situation is diﬀerent,because the baseline ResNet50obtains an aver-age accuracy of75.5%,which is not very far from the original model at76.1%.The results using SSL are also close to that of the original DINO paper,in which the authors already acknowledge that tweaking hyperparameters produces signiﬁcant variation.If anything,I would even argue that the experiments on ImageNet underestimate the variability because they all start from the same pretrained model,which means that the eﬀect of the seed is limited to the initialization of the classiﬁcation layer and the optimization process.Still,this work has serious limitations that could be overcome by spending ten times the computation budget on CIFAR to ensure all models are train to the best possible,and probably around50to100times the computation budget on ImageNet to allow training models from scratch.ﬁndings may thus be limited in that the observed variation is likely to be reduced when considering more accurate architectures and training setup,although I do not expect it to disappear entirely.4.FindingsTheﬁndings of my experiments are divided into three parts.First,I examine the long training setup on CIFAR10to evaluate the variability after convergence.This should answer question1.Then,I evaluate the short training setup on CIFAR10on10000seeds to answer question2.Finally,I investigate training on ImageNet to answer question3. 4.1Convergence instabilityTraining on CIFAR10for500diﬀerent seeds produces an evolution of the validation ac-curacy against time as shown on Figure1.In thisﬁgure,the dark red line is the average, the dark red corresponds to one standard deviation and the light red corresponds to the minimum and maximum values attained across seeds.As we can see,the accuracy does not increase past epoch25,which means that the optimization converged.However,there is no reduction in variation past convergence which indicates that training for longer is not likely to reduce the variation.If it were,we should observe a narrowing light red area,whereas it remains constant.Note that this narrowing behavior is observed before convergence.I next show the distribution of accuracy across seeds on Figure2.In thisﬁgure,I show the histogram(blue bars)with a kernel density estimation(black line)and each run (small stems at the bottom).Corresponding statistics are in Table1.As we can see,the distribution is mono-modal and fairly concentrated(dare I even say that it looks leptokurtic without going the trouble of computing higher order moments).Nonetheless,we can clearly see that results around90.5%accuracy are numerous and as common as results around 2.https:///apple/ml-cifar-10-faster/blob/master/run_log.txtof to aininD.PicardFigure3:Histogram and density plot of theﬁnal validation accuracy on CIFAR10 for the Resnet9architecture over104seeds. Each dash at the bottom corresponds to one run.Figure4:Histogram and density plot of theﬁnal validation accuracy on Imagenet for a pretrained ResNet50.Each dash at the bottom corresponds to one run.the community-to the point of being an argument for publication at very selective venues -whereas we know here that this is just the eﬀect ofﬁnd a lucky/cursed seed.The distribution can be seen on Figure3and is well concentrated between89.5%and90.5%.It seems unlikely that higher or lower extremes could be obtained without scanninga very large amount of seeds.That being said,the extremes obtained by scanning only the ﬁrst104seeds are highly non-representative of what the model would usually do.The results of this test allow me to answer positively to the second question:there are indeed seeds that produce scores suﬃciently good(respectively bad)to be considered as a signiﬁcant improvement(respectively downgrade)by the computer vision community.This is a worrying result as the community is currently very much score driven,and yet these can just be artifacts of randomness.4.3Large scale datasetsNext,I use the large scale setup with pretrained modelsﬁne-tuned and evaluated on Im-agenet to see if using a larger training set in conjunction with a pretrained model does mitigate the randomness of the scores induced by the choice of the seed.The accuracies are reported in Table2.Standard deviations are around0.1%and the gap between minimum and maximum values is only about0.5%.This is much smaller than for the CIFAR10tests,but is nonetheless surprisingly high given that:1)all runs share the same initial weights resulting from the pretraining stage except for the last layer;2)only the composition of batches varies due to the random seed.A0.5%diﬀerence in accuracy on ImageNet is widely considered signiﬁcant in the computer vision community-to the pointtorch.manual seed(3407)is all you needFigure5:Histogram and density plot of the ﬁnal validation accuracy on Imagenet for a self-supervised pretrained ResNet50.Each dash at the bottom corresponds to one run.Figure6:Histogram and density plot of the ﬁnal validation accuracy on Imagenet for a self-supervised pretrained Visual Trans-former.Each dash at the bottom corre-sponds to one run.of being an argument for publication at very selective venues-whereas we know here that it is entirely due to the change of seed.Training mode Accuracy mean±std Minimum accuracy Maximum accuracy ResNet5075.48±0.1075.2575.69ResNet50SSL75.15±0.0874.9875.35ViT SSL76.83±0.1176.6377.09Table2:Results on Imagenet for diﬀerent architecture and pretraining schemes.The corresponding distributions are shown on Figure4,Figure5and Figure6.These are not as well deﬁned as the ones obtained on CIFAR10,and I attribute it to only using 50seeds.In particular,looking at these distributions does not inspire me conﬁdence that the gap would not have grown to more than1%had I scanned more than just50seeds.The answer to the third question is thus mixed:In some sense,yes using pretrained models and larger training sets reduces the variation induced by the choice of seed.But that variation is still signiﬁcant with respect to what is considered an improvement by the computer vision community.This is a worrying result,especially since pretrained models are largely used.If even using the same pretrained modelﬁne-tuned on a large scale dataset leads to signiﬁcant variations by scanning only50seeds,then my conﬁdence in the robustness of recent results when varying the initialization(diﬀerent pretrained model)and scanning a large number of seeds is very much undermined.D.Picard5.DiscussionIﬁrst summarize theﬁndings of this small experiment with respect to the three opening questions:What is the distribution of scores with respect to the choice of seed?The distribution of accuracy when varying seeds is relatively pointy,which means that results are fairly concentrated around the mean.Once the model converged,this distribution is relatively stable which means that some seed are intrinsically better than others.Are there black swans,i.e.,seeds that produce radically diﬀerent results?Yes. On a scanning of104seeds,we obtained a diﬀerence between the maximum and minimum accuracy close to2%which is above the threshold commonly used by the computer vision community of what is considered signiﬁcant.Does pretraining on larger datasets mitigate variability induced by the choice of seed?It certainly reduces the variations due to using diﬀerent seeds,but it does not mitigate it.On Imagenet,we found a diﬀerence between the maximum and the minimum accuracy of around0.5%,which is commonly accepted as signiﬁcant by the community for this dataset.Of course,there are many shortcomings to this study as already discussed in section3. Yet,I would argue that this is a realistic setup for modeling a large set of recent work in computer vision.We almost never see results aggregated over several runs in publications, and given the time pressure that theﬁeld has been experiencing lately,I highly doubt that the majority of them took the time to ensure that their reported results where not due to a lucky setup.As a matter of comparison,there are more than104submissions to major computer vision conferences each year.Of course,the submissions are not submitting the very same model,but they nonetheless account for an exploration of the seed landscape comparable in scale of the present study,of which the best ones are more likely to be selected for publi-cation because of the impression it has on the reviewers.For each of these submissions,the researchers are likely to have modiﬁed many times hyper-parameters or even the computa-tional graph through trial and error as is common practice in deep learning.Even if these changes where insigniﬁcant in terms of accuracy,they would have contributed to an implicit scan of seeds.Authors may inadvertently be searching for a lucky seed just by tweaking their peting teams on a similar subject with similar methods may unknowingly aggregate the search for lucky seeds.I am deﬁnitely not saying that all recent publications in computer vision are the result of lucky seed optimization.This is clearly not the case,these methods work.However,in the light of this short study,I am inclined to believe that many results are overstated due to implicit seed selection-be it from common experimental practice of trial and error or of the“evolutionary pressure”that peer review exerts on them.It is safe to say that this doubt could easily be lifted in two ways.First,by having a more robust study performed.I am very much interested in having a study scanning104to 105seeds in a large scale setup,with big state-of-the-art models trained from scratch.That would give us some empirical values of what should be considered signiﬁcant and what is the eﬀect of randomness.Second,the doubt disappears simply by having the communitytorch.manual seed(3407)is all you needbe more rigorous in its experimental setup.I strongly suggest aspiring authors to perform a randomness study by varying seeds-and if possible dataset splits-and reporting average, standard deviation,minimum and maximum scores.AcknowledgmentsThis work was granted access to the HPC resources of IDRIS under the allocation2020-AD011011308made by GENCI.ReferencesXavier Bouthillier,Pierre Delaunay,Mirko Bronzi,Assya Troﬁmov,Brennan Nichyporuk, Justin Szeto,Nazanin Mohammadi Sepahvand,Edward Raﬀ,Kanika Madan,Vikram Voleti,Samira Ebrahimi Kahou,Vincent Michalski,Tal Arbel,Chris Pal,Gael Varo-quaux,and Pascal Vincent.Accounting for variance in machine learning benchmarks.In Proceedings of Machine Learning and Systems,volume3,pages747–769,2021.Mathilde Caron,Hugo Touvron,Ishan Misra,Herv´e J´e gou,Julien Mairal,Piotr Bojanowski, and Armand Joulin.Emerging properties in self-supervised vision transformers.arXiv preprint arXiv:2104.14294,2021.Cody Coleman,Daniel Kang,Deepak Narayanan,Luigi Nardi,Tian Zhao,Jian Zhang, Peter Bailis,Kunle Olukotun,Chris R´e,and Matei Zaharia.Analysis of dawnbench,a time-to-accuracy machine learning performance benchmark.ACM SIGOPS Operating Systems Review,53(1):14–25,2019.Johannes Mehrer,Courtney J Spoerer,Nikolaus Kriegeskorte,and Tim C Kietzmann.In-dividual diﬀerences among deep neural network models.Nature communications,11(1): 1–12,2020.。

小学上册第10次英语第一单元真题试卷

小学上册英语第一单元真题试卷英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.I can ______ (jump) high on the trampoline.2.The cat is ______ on the couch. (sitting)3.What do we call a person who studies the human mind?A. PsychologistB. SociologistC. AnthropologistD. Biologist4.We will go _____ the fair next week. (to)5.He is a great ___. (singer)6.I love building with my ________ (乐高) sets every weekend.7.Helium is produced in the cores of ______.8.The _______ (The Great Society) aimed to reduce poverty and promote social welfare.9.Many ________ (文化) use plants for rituals.10.I can ________ my toys.11.What do you use to cut paper?A. GlueB. ScissorsC. TapeD. RulerB12.I love going to the ______ (博物馆) to learn about ______ (历史).13.The chemical symbol for silver is ______.14.What do you call a solid that has no fixed shape?A. LiquidB. GasC. IceD. Rock15.The tree is ___ (green/brown).16.I have a _____ (球) that bounces high. I love to play with it outside. 我有一个弹得很高的球。

我、我自己和人工智能：你需要了解的内容

1TransformationMe, Myself and AI: What you need to know15 March 2023Key takeaways•We are at a defining moment – like the internet of the 90s – where Artificial Intelligence (AI) is moving toward mass adoption.According to Statista, at over 8 billion, there are now more AI digital voice assistants than people on the planet. To navigate this crossroad, we consider 10 key questions about AI and beyond.•But let ’s back up – what is AI? AI leverages large data sets and uses algorithms to find underlying relationships, which can be used to drive new or better business outcomes. Generative AI (think ChatGPT) is a type of AI technology that can produce various types of content, including text, imagery, audio, and synthetic data.•And this could be big – AI could potentially contribute up to $15.7 trillion to the global economy by 2030 (source: PwC), while open data (i.e., data that anyone can access, use and share) has the potential to unlock $3.2-5.4 trillion in economic value annually by, for example, reducing emissions, increasing productivity, and improving healthcare (source: McKinsey).Cheat sheet: Artificial Intelligence1. What is artificial intelligence and what are the key technologies?Artificial intelligence (AI) leverages large data sets and uses algorithms to find underlying relationships, which can be used to drive new or better business outcomes. Generative AI is a type of AI technology that can produce various types of content, including text, imagery, audio, and synthetic data. Some of the key AI technologies include machine learning, deep learning, predictive analytics, natural language processing and machine vision.According to BofA Global Research, the leap in this technology is due to a combination of four powerful factors: 1) democratization of data; 2) unprecedented mass adoption; 3) warp-speed technological development; and 4) abundance of commercial use cases.2. Why do we need AI?More data is created per hour today than in an entire year just two decades ago, and global data is expected to double every 2 years (source: BofA Global Research). The data we are creating is increasing exponentially and we need AI to analyze and interpret it, especially when new applications that require more data are being developed constantly. Holograms, metaverse, brain computer interfaces and electric vertical take-off and landing (eVTOL) aircraft are just some of the technologies that will be very data-heavy and are yet to be launched … not to mention quantum computing, which could leapfrog the total data creation once commercially available. In short, data will grow much faster than initially expected in the coming years.Currently, we are storing and transmitting only 1% of global data (source: International Data Corporation (IDC)). Therefore,according to BofA Global Research, if we take into consideration: 1) the exponential growth of data creation; 2) that the amount of global data stored and analyzed could swell given that 37% of it could be useful if analyzed (vs 1% actually analyzed today); 3) that more people will be online globally, especially following the COVID crisis; and 4) the higher penetration of data-heavy and yet-to-be launched applications, then we are likely to see significant uptake of AI to analyze such data and train AI-based algorithms within the next 10 years.I N S T I T U T EAccessible version215 March 2023I N S T I T U T E Source: Domo3. Algorithms, machine learning, and deep learning – what’s the difference?Algorithms are a set of instructions that can be used for machine and deep learning. An algorithm is a finite set of instructions that is used to solve a well-defined computational problem. Algorithms can be used to carry out machine and deep learning. Linear regressions and logistic regressions are some examples of machine learning algorithms.Machine learning is a subdivision of AI that uses computerized mathematical algorithms, which can learn from the data and teaches itself to progress as the data keeps fluctuating. Put differently, rather than having humans write programs, computers themselves determine program functions from the data. Machine learning algorithms automatically apply mathematicalcalculations to Big Data (i.e., data sets that are too large or complex to be dealt with by traditional data-processing application software) in order to learn from past data and in turn produce repeatable and reliable decisions and results – e.g., improved videosuggestions based on past video viewing activity. In general, with machine learning, if the data is of the same type, then increasing the amount of data will result in an improvement in the accuracy of output.Deep learning is the subset of machine learning and includes algorithms inspired by the function and structure of the brain called artificial neural networks. Deep learning's strength stems from the system's ability to ascertain additional data relationships that are difficult to identify. After sufficient training, the network of algorithms constantly improves predictions or interpretations – e.g., improved product rankings based on relationships of data that humans cannot easily identify.4. What is the economic potential of AI?According to Accenture, AI could double the annual global economic growth rates by 2035 and is likely to drive this in three different ways. First, AI will lead to a strong increase in labor productivity (by up to 40%) due to automation. Second, AI will be capable of solving problems and self-learning. And third, the economy will benefit from the diffusion of innovation.A study by PwC estimates that AI could also contribute up to $15.7 trillion to the global economy by 2030, while open data (i.e., data that anyone can access, use and share) has the potential to unlock $3.2-5.4 trillion in economic value annually by, for example, reducing emissions, increasing productivity, and improving healthcare (source: McKinsey).According to IDC, global revenues for the AI market, including software, hardware, and service sales, will grow at a compound annual growth rate (CAGR) (~2022-26) of 19% to reach over $900 billion by 2026 (Exhibit 2). Big data and AI could double the gross value-added growth rates of developed markets by ~2035 and add 0.8-1.4 percentage points to global productivity growth in the long run. The Big Data ecosystem will have tremendous positive impacts on society and over half the benefits could be captured as consumer surplus and public benefits rather than corporate profits.A lot! Investor funding for generative AI increased 71% year-over-year (YoY) in 2022 from $1.5 billion to $2.6 billion. Even global private investment in AI increased 103% YoY in 2021 to $93.5 billion, more than double the total private investment in 2020 (Exhibit 3). Governments around the world are also increasingly seeking to promote and provide funding for AI development and innovation. In 2020, the US passed the IOGAN ACT (Identifying Outputs of Generative Adversarial Networks Act), which directed the National Science Foundation to support research on generative adversarial networks and other relevant technology.And according to BofA Global Research, as the global population ages and younger generations like Gen Z and Gen C (or “the Covid generation”) make up a larger portion of the population, AI adoption should increase too because these two generations, particularly Gen C, will be unable to live without tech in most aspects of their lives. By the end of 2021, Gen C numbered 700 million, or ~9% of the world’s population. They are estimated to reach up to 2 billion by 2025 or ~20% but will be smaller in size than Gen Z due to declining birth rates (source: Kinetics).15 March 2023 3Large language models are models that use deep learning in natural language processing (NLP) uses. An LLM is a transformer-based neural network which predicts the text that is likely to come next. The performance of the model can be judged on how many parameters it has (i.e., the number of factors it considers when generating output).Natural language processing (NLP) is the AI technology that enables machines to understand human language including slang, contractions, and pronunciations, and consecutively produce human-like dialogue and text. NLP entails applying different algorithms to identify and extract natural language rules to convert the unstructured language data into a form that computers can interpret. A real-world example is improved results for voice search queries.Since 2020, natural language systems have become more advanced at processing human language, particularly in terms of sentiment and intent. They can generate human-like text, and express understanding about an image through language (visual understanding).on Foundation Models (CRFM), Stanford University Institute for Human-Centered Artificial Intelligence4 15 March 20237. What is ChatGPT and why is the technology behind it so ground-breaking?ChatGPT is a chatbot, developed by OpenAI, that can generate coherent human-like text. It is the first application of its kind that is openly available to a wide audience. Until now AI could read and write but could not understand content. Generative AI models like ChatGPT changed that, enabling machines to understand human language, and consecutively produce human-like dialogue and content.Since ChatGPT can generate human-like text, it can be used for content generation (e.g., writing essays, news articles, social media posts, marketing content, stories, music, emails etc.), data extraction, summarizing text, optimizing web browsers, language translation and computer programming. Programmers are already using this technology for program generation or to explain code or concepts.Since its launch in November 2022, ChatGPT has gained significant traction, amassing one million users after just five days. For context, it took Netflix 3.5 years and Instagram 3 months to reach this milestone.predict the next word in a sentence based on previous entries. Hence, ChatGPT is classified as a form of generative AI. ChatGPT is a variation of GPT-3, a large language model (LLM) developed by OpenAI. ChatGPT has been trained based on a dataset of 20 billion parameters (i.e., it generates a response based on an analysis of 20 billion different variables), which is a significant development in the space of LLMs. GPT-3 is based on 175 billion parameters, whereas its predecessors, GPT-1 and GPT-2, are based on 117 million and 1.5 billion parameters, respectively. At more than 6 billion parameters, the models can learn without re-training or updating their parameters.OpenAI pioneered this technology due to its ability to train a model at scale. It was the first to use a 10,000 GPU (graphics processing unit) supercomputer to train a single model. To put this in perspective, a GPU with a processing speed of 1.5 gigahertz (GHz) can execute 1.5 billion instructions per second. On this assumption, a supercomputer with 10,000 GPUs could calculate 15 trillion instructions per second.The second key differentiator is that this model is trained using reinforcement learning from human feedback (RLHF). This is where the model generates outputs that are labelled and calculated for some reward objective, such as to represent human preferences for how a task should be done or things to avoid (e.g., harmfulness).8. How is this technology going to evolve?According to BofA Global Research, this technology is likely to evolve beyond single applications (e.g., text and images) towards multimodality – for instance, using text, images, and/or voice recordings as prompts to generate a response from the AI system. More proficient language model deployment could proliferate conversational tools into, for example, word processors, virtual video meetings and email systems to enable their onboarding for more users to interact via speech. Another application is that this technology could be used to generate entire programming applications rather than just being able to suggest or explain code.There is ongoing debate around the competencies of AI chatbot systems; whether they could be applied to any industry vertical in their current capabilities and parameters, or whether they need to be verticalized to become more commercially useful.15 March 2023 5According to BofA Global Research, in the long term, LLMs may be general enough so that verticalization is no longer needed. However, in the short-term, LLMs may need to be domain-specific to achieve an increase in performance in the industries that intend to use them.Some companies are already developing verticals that integrate ChatGPT’s functionality. For example, Microsoft has announced that its search engine, Bing, will be powered by the same AI technology as ChatGPT, which should improve the user experience of its search engine. Similarly, Velocity, an Indian fintech company, has launched a ChatGPT-integrated chatbot called Lexi to help e-commerce companies. India’s Ministry of Electronics and IT is also planning to integrate ChatGPT with WhatsApp to help farmers learn about several government schemes.9. What are the barriers to entry?Sectors that can combine computing power, data, and talent to enable AI could capitalize on the commercial opportunities. However, operating costs (e.g., semiconductors, staff) could present a large barrier to entry and as the parameter size increases, costs increase, too. The hardware alone required to train a 530 billion-parameter model (the reported size of OpenAI’s GPT-4 model under development) would be a $100 million experiment. A single search query in a GPT-like system can cost two to three US cents. This could be challenging to absorb when such models perform billions of queries a day. For this technology to be more viable, we would likely need a 10-20x improvement in efficiency, otherwise it would be too costly for entrants to deploy them commercially.Additionally, reinforcement learning from human feedback (RLHF) involves difficult engineering, as companies need to build their own. This problem is compounded by scarce talent: a small number of people know how to do this and work for a small number of companies.10. What are some of the risks of this technology?Since ChatGPT can generate human-like content, it is possible to introduce automation in sectors that are based on idea generation e.g., advertising, art and design, entertainment, music, media and legal. This could help drive the fifth wave of industrial revolution – the coexistence of humans and machines.But if we look at risks, ChatGPT can “hallucinate” (i.e., generate an incorrect answer with confidence). Furthermore, it is not able to make decisions or deal with too much memory/generation and can also respond to harmful instructions, therefore lowering the barrier to entry for threat actors because it opens the door for more malware, phishing, and identity-based ransomware attacks. Additionally, the model could accidently reveal sensitive information and the output can be misused e.g., tracking individuals. If data is misused, then it could be the case that the model violates privacy laws e.g., the EU’s general Data Protection Regulation (GDPR). In general, this technology could have broad-based implications for cybersecurity, particularly for email security, identity security, and threat detection.Lastly, intellectual property is often overlooked before public release and is difficult to incorporate. It is a grey area, as there is currently nothing stopping companies from using AI-generated content beyond compliance. While some large technology companies like have developed responsible AI principles to avoid unfair bias in AI models, incorporating fairness and preventing bias becomes difficult when training models with billions of parameters.BofA Global Research, KnowHow6 15 March 2023ContributorsVanessa CookContent Strategist, Bank of America InstituteTaylor BowleyEconomist, Bank of America InstituteSourcesHaim IsraelGlobal Head of Thematic Research, BofA Global ResearchMartyn BriggsSenior Thematic Research Analyst, BofA Global ResearchFelix TranStrategist, BofA Global ResearchKate PavlovichAnalyst, BofA Global Research15 March 2023 7DisclosuresThese materials have been prepared by Bank of America Institute and are provided to you for general information purposes only. To the extent these materials reference Bank of America data, such materials are not intended to be reflective or indicative of, and should not be relied upon as, the results of operations, financial conditions or performance of Bank of America. Bank of America Institute is a think tank dedicated to uncovering powerful insights that move business and society forward. Drawing on data and resources from across the bank and the world, the Institute delivers important, original perspectives on the economy, Environmental, Social and Governance (ESG) and global transformation. Unless otherwise specifically stated, any views or opinions expressed herein are solely those of Bank of America Institute and any individual authors listed, and are not the product of the BofA Global Research department or any other department of Bank of America Corporation or its affiliates and/or subsidiaries (collectively Bank of America). The views in these materials may differ from the views and opinions expressed by the BofA Global Research department or other departments or divisions of Bank of America. Information has been obtained from sources believed to be reliable, but Bank of America does not warrant its completeness or accuracy. Views and estimates constitute our judgment as of the date of these materials and are subject to change without notice. The views expressed herein should not be construed as individual investment advice for any particular client and are not intended as recommendations of particular securities, financial instruments, strategies or banking services for a particular client. This material does not constitute an offer or an invitation by or on behalf of Bank of America to any person to buy or sell any security or financial instrument or engage in any banking service. Nothing in these materials constitutes investment, legal, accounting or tax advice.Copyright 2023 Bank of America Corporation. All rights reserved.8 15 March 2023。

小学上册第七次英语第3单元全练全测

小学上册英语第3单元全练全测英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1.I like to _______ my favorite stories.2.The sun sets in the ___ (west/east).3.What is the name of the famous wall in China?A. Great WallB. Berlin WallC. Hadrian's WallD. China's BarrierA4.What is the term for the study of the Earth's physical features?A. BiologyB. GeologyC. GeographyD. MeteorologyB5.My aunt has a beautiful __________ (花园).6. A chemical change results in the formation of ______ substances.7.I have a toy _______ that dances and sings catchy tunes.8.Parrots can ______ human speech.9._____ (园艺) can be a relaxing hobby.10. A _____ (植物笔记) can help track growth and changes.11.The mantis is a type of _______ (昆虫).12.I like to ride my ______ (自行车) in the park. It is very ______ (有趣).13.The chemical formula for bismuth trioxide is _____.14.What is the primary color of a banana?A. GreenB. YellowC. RedD. BrownB15.What do you use to brush your teeth?A. ToothbrushB. CombC. ToothpasteD. SoapA16.The ________ is often seen in gardens.17.Iceland is known for its beautiful _____ (冰雪景观).18.The _____ (老虎) is a powerful animal found in the jungle. 老虎是在丛林中发现的强大动物。

人工智能的未来发展趋势及原因英语作文

人工智能的未来发展趋势及原因In recent years, the field of artificial intelligence has experienced rapid growth and is poised to revolutionize many aspects of our lives in the future. There are several key trends and factors driving the development of AI technology and shaping its future trajectory.One major trend in the development of artificial intelligence is the increasing focus on deep learning algorithms. These algorithms, inspired by the structure and function of the human brain, have shown remarkable success in tasks such as image recognition, natural language processing, and autonomous driving. As researchers continue to refine and optimize these algorithms, AI systems are becoming more capable and sophisticated.Another important trend is the growing availability of large-scale data sets for training AI models. The rise of big data has provided AI researchers with the raw material they need to train complex machine learning models. With access to vast amounts of data, AI systems can learn to perform tasks with a level of accuracy and precision that was previously unattainable.Advancements in hardware technology are also driving the development of artificial intelligence. The increasing power and efficiency of computer processors, along with the rise of specialized hardware such as graphics processing units (GPUs), are enabling researchers to train larger and more complex AI models. This hardware evolution is crucial for the continued progress of AI research and the deployment of AI systems in real-world applications.Furthermore, the collaboration between academia, industry, and government is playing a significant role in advancing artificial intelligence. Universities and research institutions are conducting cutting-edge research in AI, while companies are investing heavily in AI research and development to gain a competitive edge. Government agencies are also supporting AI initiatives through funding, regulation, and policy frameworks that promote innovation and responsible AI use.In addition to these trends, societal factors such as the increasing demand for automation and efficiency in various industries are driving the adoption of artificial intelligence. Businesses are turning to AI technologies to streamline operations, reduce costs, and improve customer experiences. As AI systems prove their value in areas such as healthcare, finance, and transportation, the demand for AI-powered solutions is expected to grow.In conclusion, the future of artificial intelligence is promising and full of potential. With advancements in deep learning algorithms, the availability of large data sets, improvements in hardware technology, and collaboration across different sectors, AI technology is poised to transform the way we live and work. As wecontinue to push the boundaries of AI research and innovation, the possibilities for what artificial intelligence can achieve are endless.。

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In order to model a shape, we represent it by a set of points. For the resistors we have chosen to place points around the boundary, as shown in Figure 3. This must be done for each shape in the training set. The labelling of the points is important, each la-
10 lar class of objects. Bookstein [9] has studied the statistics of shape deformation by representing objects as sets of 'landmark points', but has not applied this to the problem of shape modelling. Mardia, Kent and Walder [10] represent the boundary of a shape as a sequence of points with distributions related by a covariance matrix. To fit a model to an image they cycle through the points to find the most likely position given the image and the current shape. The examples given seem to be local models, in that deforming one part of the boundary does not affect the rest of it until the change has been propagated round the boundary by the updating method. In this paper we describe a new method of shape modelling based on the statistics of labelled points placed on a set of training examples. The sets of points are automatically aligned so that their mean positions and main modes of variation can be calculated. Aligning the shapes allows the positions of equivalent points in different examples to be compared simply by examining their co-ordinates. A model consists of the mean positions of the points and a number of vectors describing the modes of variation.
1 Introduction
We have previously described a method for modelling two dimensional shape, based on the statistics of chord lengths over a set of examples [12]. Although this provided a means of automatically parameterising shape variability, the method was difficult to use, requiring an iterative procedure to reconstruct a shape given a set of parameters. The method has computational complexity Ofa2] where n is the number of points used to describe the shape. In this paper we present a new method which produces a more compact representation, allows direct reconstruction of a shape from a set of parameters and offers O[n] computational complexity. Image interpretation using rigid models is well established [1,2]. However, in many practical situations objects of the same class are not identical and rigid models are inappropriate. This is particularly true in medical applications, but also many industrial applications involve assemblies with moving parts, or components whose appearance can vary. In such cases flexible models, or deformable templates, can be used to allow for some degree of variability in the shape of the imaged object. Yuille, Cohen and Hallinan [3] and Iipson et al [4] use deformable templates for image interpretation. Unfortunately their templates are hand-crafted with modes of variation which have to be individually tailored for each application. Kass, Witkin and Terzopoulos [5] described 'Active Contour Models', flexible snakes which can stretch and deform to image features. These have been extended to apply constraints to their deformation by adjusting the elasticity and stiffness of the model [6,7]. Pentland and Sclaroff [8] model objects as lumps of elastic clay, generating different shapes using combinations of the modes of vibration of the clay. However this does not always lead to a very compact description of the variability within a of printed circuit board showing examples of resistors. 2.1 Labelling The Training Set
Figure 2 : Examples of resistor shapes from a training set.
Training Models of Shape from Sets of Examples
TECootes, CJ.Taylor, D.H.Cooper and J.Graham
Department of Medical Biophysics University of Manchester Oxford Road Manchester M13 9PT email: bim@
2 Point Distribution Models
Suppose we wish to derive a model to represent the shape of resistors as they appear on a printed circuit board, such as those shown in Figure 1. Different examples of resistor have sufficiently different shapes that a rigid model would not be appropriate. Figure 2 shows some examples of resistor boundaries which were obtained from backlit images of individual resistors. Our aim is to build a model which describes both typical shape and allowed variability, using the examples in Figure 2 as a training set.
11 belled point represents a particular part of the object or its boundary. For instance, in the resistor model, points 0 and 31 always represent the ends of a wire, points 3, 4 and 5 represent one end of the body of the resistor and so on. The method works by modelling how different labelled points tend to move together as the shape varies. If the labelling is incorrect, with a particular point placed at different sites on each training shape, the method will fail to capture shape variability. 5 10