The following three questions refer to the calculation: (1001 1110)2 – (0111 0010)2(d) State the value of the carry bit after this calculation. [2] A. 0 B. 1 C. X D. Z E. Undefined

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To subtract binary numbers, you start from the rightmost digit and subtract the corresponding digits. If the subtrahend digit is greater than the minuend digit, you need to borrow 1 from the next higher digit. In this case: ``` 1001 1110 - 0111 0010 ----------- 0010 1100 ``` There is a borrow operation during the subtraction of the second digit from the right, so the carry bit is 1. Therefore, the answer is B. 1.
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• PTM Responders must populate PTM ResponseD Messages as follows (refer to Figure 6-24 and the accompanying implementation note): ◦ The PTM Master Time field is a 64-bit value containing the value of PTM Master Time at the receipt of the PTM Request Message for the current PTM Dialog. In Figure 6-24 , for the 2nd PTM dialog, this is the PTM Master Time at time t2’. ◦ The Propagation Delay field is a 32-bit value containing the interval between the receipt of the PTM Request Message and the transmission of the PTM Response Message for the previous PTM dialog. In Figure 6-24 , for the 2nd PTM dialog, this is the time interval between t2 and t3 captured during the 1st PTM dialog. ◦ The unit of measurement for both fields is one ns. ◦ A PTM Responder with multiple Downstream Ports must populate all PTM ResponseD Messages with values from a single PTM Root across all its PTM Ports Downstream ports. 翻译

• PTM Responders 必须按照以下方式填充 PTM ResponseD Messages（参考图 6-24 和相应的实现说明）： ◦ PTM Master Time 字段是一个 64 位值，包含当前 PTM Dialog 的 PTM Master Time 值。在图 6-24 中，对于第二...

2023-06-09 09:46:11.022252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1900] Ignoring visible gpu device (device: 0, name: GeForce GT 610, pci bus id: 0000:01:00.0, compute capability: 2.1) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.5. 2023-06-09 09:46:11.022646: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_test_function.<locals>.test_function at 0x0000017BB39D0670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x0000017BB3AE83A0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.

2. TensorFlow 检测到了一些 CPU 功能，包括 AVX 和 AVX2 指令集。这些指令集可以在一些性能关键的操作中加速计算。为了在其他操作中启用它们，需要使用适当的编译器标志重新构建 TensorFlow。 3. TensorFlow 输出...

Define a class that meets the following specifications. Class name: WaterBody Class constructor parameter: 1. int/float Assign this number to the instance attribute volume The class has class attributes RHO = 997 and G = 9.81. Define a class method for the WaterBody class that meets the following specifications. Method name : get_hydrostatic_pressure Method parameter: 1. float Method type: Class method Return value: 1. float Using the input float, the depth. calculate and return the hydrostatic pressure. Hydrostatic pressure a given depth = RHO*G*depth If the depth is less than 0, the static method should raise an InvalidDepthError. Define a instance method for the WaterBody class that meets the following specifications. Method name: get_water_mass Method type: Instance method Return value: 1. Float This method should return the mass of the waterbody given that mass = RHO* volume

Here is the implementation of the WaterBody class: python class WaterBody: RHO = 997 G = 9.81 def __init__(self, volume): self.volume = volume @classmethod def get_hydrostatic_pressure...

Next, the first order statistics of the no. 36 kernel and the second order statistics of the no. 28 kernel were calculated; these refer to the designs of the no. 13,751 and no. 13,768 features. The characteristics of the filter response were reflected in the two parameters. As the second order statistics of the no. 28 kernel show, the no. 107 filter response of the wild-type tumor was more internally complicated and had more texture information. As the first order statistics of the no. 36 kernel show, the no. 107 filter response of the mutation-bearing tumors had lower intensity and was more gathered around the no. 36 kernel (the no. 36 kernel had a mean value of −0.1352 and a variance of 0.0055). Thus, the prediction based on CNN features clearly provided good results. Indeed, the two types of tumors have significantly different responses in deep filter banks. 解释

这段文字进一步解释了第107个深度滤波器的响应特征。通过计算第36个内核的一阶统计量和第28个内核的二阶统计量，得到了特征13,751和13,768的设计。这两个特征中反映了滤波器响应的特征。通过第28个内核的二阶统计量...

翻译"Numeric" is a term that can refer to either an integer (a whole number) or a floating-point number (a number with a fractional part). The exact definition of "numeric" can vary depending on the context and the programming language being used. As for length, the concept of length is not directly applicable to numeric data types. However, you can usually specify the number of digits or the range of values that a numeric value can take. For example, in some programming languages, you can specify the number of bytes used to store a numeric value, which determines the maximum and minimum values that the value can represent.

"数值型"是一个术语，可以指整数（整数）或浮点数（具有小数部分的数字）。 "数值型"的确切定义取决于上下文和使用的编程语言。 至于长度，长度的概念不能直接应用于数值数据类型。 但是，通常可以指定数值的位数或...

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Before Playstation, there was Pong, at one time the ultimate in video game entertainment. For those of you not familiar with this game please refer to the Wikipedia entry (http://en.wikipedia.org/wiki/Pong) and the many fine websites extolling the game and its virtues. Pong is not so very different in structure from the Billiard ball simulation that you developed earlier in the course. They both involve a ball moving and colliding with obstacles. The difference in this case is that two of the obstacles are under user control. The goal of this project is to develop your own version of Pong in MATLAB using the keyboard as input, for example, one player could move the left paddle up and down using the q and a keys while the right paddle is controlled with the p and l keys. You may check the code for the Lunarlander game which demonstrates some of the techniques you can use to capture user input. You will also probably find the plot, set, line and text commands useful in your program. You have used most of these before in the billiard simulation and you can use Matlabs online help to get more details on the many options these functions offer. Your program should allow you to play a game to 11 keeping track of score appropriately. The general structure of the code is outlined below in pseudo code While not done Update Ball State (position and velocity) taking into account collisions with walls and paddles Check for scoring and handle appropriately Update Display Note that in this case it is implicitly assumed that capturing the user input and moving the paddles is being handled with callback functions which removes that functionality from the main loop. For extra credit you could consider adding extra features like spin or gravity to the ball flight or providing a single player mode where the computer controls one of the paddles.

text(0.5, -0.5, 'Player 2: P/L', 'HorizontalAlignment', 'right', 'FontSize', 12); paddle1 = rectangle('Position', [-1+paddle_width/2 paddle1_y-paddle_height/2 paddle_width paddle_height], 'FaceColor',...

Write a program to simulate a process of translation from a logical address to physical address. Assumptions 1. Assume the file la.txt includes the sequence of generated addresses from CPU. 2. Use a part of memory as backing store that store data for a process. 3. The backing store size is 128 bytes 4. The size of process p is 128 bytes. 5. The contents of p is included in a file pdata.bin which is a binary file. 6. Use a part of memory as RAM. The size of physical memory is 256 bytes, from 0 to 255. All the physical memory is available, allocating starting from beginning in sequence. That is, allocate frame 0 first, then frame 1, then frame 2…. 7. The size of a frame is 32 bytes, i.e., 5 bits for the offset in a frame, total number of frames is 8. 8. At beginning, no page table is available for process p. Requirements Write a program to 1. Setup a simulating backing store in memory. Read the data from pdata.bin to this backing store. 2. Initialize a page table for process p, set the frame number to be -1 for each page, indicating that the page is not loaded into memory yet. 3. Read logical addresses one by one from la.txt. 4. For each logical address, a) if its page has been loaded into physical memory, simply find the frame number in the page table, then generate physical address, find and print out the physical address and data inside this address. b) if the page is used for the first time, i.e., in page table, its frame number is -1，then the page that contains this address should be loaded into a free frame in physical memory (RAM). Then update the page table by adding the frame number to the right index in the page table. Then repeat 4a). Refer to Figure 1 for the relationships and how physical memory, backing store, and CPU are simulated. Figure 1 How physical memory, backing store and CPU are simulated in this program assignment Hints: a) use a memory block pointed by a pointer or use an array as a simulation of backing store b) use functions fread or mmap for the binary file read. Search through the Internet for the usage of these functions. c) Use an array to simulate the memory. d) Use bit operators &, |, <<, and >> to get the bits in a logic address or form a physical address e) Use char for the type of data in the process, use unsigned char (8 bits) for the type of address. Coding & Submission 1. Using pure C to finish this program. 2. Put all the codes in one .c file named PA3_#####.c, replace “#####” as the last 5 digits of your student ID. 3. Put pdata.txt and la.txt in the same folder as PA3_#####.c, which the need .txt file can be open directly by filename instead of absolute path. 4. Submit only the .c file mentioned above.使用C语言完成

printf("Logical address: %d, Physical address: %d, Data: %c\n", logical_address, physical_address, physical_memory[physical_address]); } else { // find a free frame in physical memory int free_...

The interpretation of the quantities ma and ba is as follows. Given a Bayes-plausible measure η, the quantity ba denotes the probability that the receiver plays action a ∈ A under the optimal strategy a(·) when the sender uses the signaling scheme corresponding to η; in other words, ba � Pη(a(s¯) � a). Similarly, ma denotes the distribution of the state ω¯ , conditioned on the receiver choosing action a. By iterated expectation, we obtain ma(ω) � Eη[s¯(ω)|a(s¯) � a]. Thus, for any a ∈ A, the quantity ma denotes the mean of all posterior beliefs the receiver holds, conditioned on choosing action a. For this reason, we refer to ma as the mean posterior of the receiver corresponding to action a ∈ A. Note that ma may not correspond to any actual posterior that the receiver holds when choosing action a ∈ A; in fact, the mean posterior ma ∈ Conv(Pa) may not even lie in the set Pa. Figure 2 gives some geometric intuition for 翻译

这些量ma和ba的解释如下。给定一个Bayes-plausible度量η，量ba表示当发送者使用与η对应的信令方案时，接收者在最优策略a(·)下选择行动a∈A的概率；换句话说，ba表示Pη(a(s¯)∣a)。...图2给出了一些几何直觉。

README for navigating Sandia cell ARC Data • Name scheme = chemistry_state of charge_type of ARC • ARC plots in the manuscript are based on data recorded during the ‘exotherm’ mode of the ARC after it has completed ‘heat-wait-seek.’ This data is highlighted in the ‘Calculations’ tab for the EV-ARC files and the ‘Sheet 1’ tab for the ES-ARC files. • For the complete time course of the ARC experiment, including the ‘heat-wait-seek’ data, refer to the ‘Clean.DAT’ tab for the EV-ARC files and the ‘Data’ tab for the ES-ARC files. 含义

这段文本是关于导航三莱亚（Sandia）电池ARC数据的指南。其中介绍了命名方案，包括化学状态、电荷类型和ARC类型。在手稿中，ARC图是基于在完成“热门搜索”弧后的“排放室”模式中记录的数据。如果需要完整的时间...

Transfer learning25,26 is a method where previously learned knowledge is applied to another task and the task domains may be different. In our case, the domain is very differ- ent. ImageNet consists of natural camera images and does not include any type of lung nodule or cancer image. Our image set consists of only lung nodules in CT images. We experimented with three different pretrained CNN’s [vgg (visual geometry group)-m/vgg-f/vgg-s]27 in this study using a MA TLAB toolbox named MA TCONVNET.28 The f, m, and s after vgg- stand for fast, medium, and slow and refer to training time (so partly the number of weights). We obtained deep features from the outputs of the last fully connected layer after applying the activation function using a rectified linear unit (post-ReLU), which changes all values <0 to be 0. The LDCT images were grayscale (no color component and we changed the voxel intensities of LDCT images to 0-255 or 8 bit), but the pretrained network was trained on RGB images, so we normalized the images by the average red, green, and blue channel images, and exper- imented by using each channel separately. 解释

Transfer learning是一种方法，其中先前学习的知识被应用于另一个任务，任务域可能不同。在我们的情况下，领域非常不同。ImageNet由自然相机图像组成，不包括任何类型的肺结节或癌症图像。我们的图像集仅包含CT图像...

s8 BME280_SPI_bus_read(u8 dev_addr, u8 reg_addr, u8 *reg_data, u8 cnt) { s32 iError=BME280_INIT_VALUE; u8 array[SPI_BUFFER_LEN]={0,}; u8 stringpos; /* For the SPI mode only 7 bits of register addresses are used. The MSB of register address is declared the bit what functionality it is read/write (read as 1/write as BME280_INIT_VALUE)*/ array[BME280_INIT_VALUE] = reg_addr|SPI_READ;/*read routine is initiated register address is mask with 0x80*/ /* * Please take the below function as your reference for * read the data using SPI communication * " IERROR = SPI_READ_WRITE_STRING(ARRAY, ARRAY, CNT+1)" * add your SPI read function here * iError is an return value of SPI read function * Please select your valid return value * In the driver SUCCESS defined as 0 * and FAILURE defined as -1 * Note : * This is a full duplex operation, * The first read data is discarded, for that extra write operation * have to be initiated. For that cnt+1 operation done in the SPI read * and write string function * For more information please refer data sheet SPI communication: */ for (stringpos = BME280_INIT_VALUE; stringpos < cnt; stringpos++) { *(reg_data + stringpos) = array[stringpos+BME280_DATA_INDEX]; } return (s8)iError; }

s32 iError = BME280_INIT_VALUE; // 用于存储错误码的变量 u8 array[SPI_BUFFER_LEN] = {0}; // 用于存储读取数据的数组 u8 stringpos; // 在数组中存储读取操作的命令字节 array[BME280_INIT_VALUE] = reg_...

翻译：The final optimization Genus performs is incremental optimization. Optimizations performed during IOPT improve timing and area and fix DRC violations. Optimizations performed during this phase include multibit cell mapping, incremental clock gating, incremental retiming, tie cell insertion, and assign removal. For more information on multibit cell mapping, refer to Mapping to Multibit Cells in the Genus Synthesis Flows Guide. By default, timing has the highest priority and Genus will not fix DRC violations if doing so causes timing violations. This priority can be overridden by setting the drc_first attribute to true. In this case, all violations will be fixed as well as those paths with positive slack. IOPT also includes Critical Region Resynthesis (CRR) which iterates over a small window on the critical path to improve slack. You can control CRR through the effort level argument in the syn_opt command. It is asserted by specifying the high effort level. If for some reason you need to cancel the Genus session in the middle of IOPT, press the Ctrl-c key sequence. You will be given a warning message with a particular IOPT state and brought back to the command line. Next time you enter a Genus session (with the same commands, constraints, script, etc. that preceded the ctrl-c halt) you can specify the IOPT state at which you stopped with the stop_at_iopt_state attribute. Genus will continue with the netlist it had generated at the specified state.

有关多位单元映射的更多信息，请参阅Genus合成流程指南中的Mapping to Multibit Cells。默认情况下，时序具有最高优先级，并且如果修复DRC违规导致时序违规，Genus不会修复DRC违规。可以通过设置drc_first属性为true...

MissingDependency: The 'whoosh' backend requires the installation of 'Whoosh'. Please refer to the documentation.

这个错误提示意味着你需要安装 'Whoosh' 模块来使用 'whoosh' 后端。你可以通过运行以下命令来安装它： pip install whoosh 安装完成后，你需要在你的代码中导入 'whoosh' 模块，例如： ...

这是什么报错：FAILURE: Build failed with an exception. * What went wrong: Unable to start the daemon process. This problem might be caused by incorrect configuration of the daemon. For example, an unrecognized jvm option is used. Please refer to the User Manual chapter on the daemon at https://docs.gradle.org/7.4/userguide/gradle_daemon.html Process command line: C:\Program Files (x86)\Java\jdk1.8.0_341\bin\java.exe -Xmx2048m -Dfile.encoding=UTF-8 -Duser.country=CN -Duser.language=zh -Duser.variant -cp C:\Users\huadongzhou\.gradle\wrapper\dists\gradle-7.4-bin\c0gwcg53nkjbqw7r0h0umtfvt\gradle-7.4\lib\gradle-launcher-7.4.jar org.gradle.launcher.daemon.bootstrap.GradleDaemon 7.4 Please read the following process output to find out more: ----------------------- Error occurred during initialization of VM Could not reserve enough space for 2097152KB object heap * Try: > Run with --stacktrace option to get the stack trace. > Run with --info or --debug option to get more log output. > Run with --scan to get full insights.

这是 Gradle 构建工具的报错，提示无法启动守护进程。可能是由于守护进程的配置不正确，例如使用了无法识别的 JVM 选项。报错信息中还提到了一些可能的解决方法，例如运行带有 --stacktrace 选项的命令来获取堆栈...

unction load_boston is deprecated; load_boston is deprecated in 1.0 and will be removed in 1.2. The Boston housing prices dataset has an ethical problem. You can refer to the documentation of this function for further details. The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning. In this special case, you can fetch the dataset from the original source:: import pandas as pd import numpy as np data_url = "http://lib.stat.cmu.edu/datasets/boston" raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) target = raw_df.values[1::2, 2] Alternative datasets include the California housing dataset (i.e. :func:~sklearn.datasets.fetch_california_housing) and the Ames housing dataset. You can load the datasets as follows:: from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() for the California housing dataset and:: from sklearn.datasets import fetch_openml housing = fetch_openml(name="house_prices", as_frame=True) for the Ames housing dataset. warnings.warn(msg, category=FutureWarning) 线性回归模型的RMSE: 4.928602182665355 决策树回归模型的RMSE: 3.2273949915330395 随机森林回归模型的RMSE: 2.8109631609391226

这段提示信息的意思是，函数load_boston已经被弃用了，在1.0版本中已经弃用，在1.2版本中将被删除。波士顿房价数据集存在伦理问题，因此强烈不建议在非研究和教育伦理问题的情况下使用该数据集。...

Namespace not specified. Please specify a namespace in the module's build.gradle file like so: android { namespace 'com.example.namespace' } If the package attribute is specified in the source AndroidManifest.xml, it can be migrated automatically to the namespace value in the build.gradle file using the AGP Upgrade Assistant; please refer to https://developer.android.com/studio/build/agp-upgrade-assistant for more information.

这是一个错误信息，提示在 Android 应用程序的 build.gradle 文件中没有指定命名空间。命名空间是一个用于标识应用程序包名的前缀。为了解决这个问题，需要在 build.gradle 文件中添加如下代码： ...

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