How long did right single support last? There were 60 pictures capture in each second Events and Phases LTO IHS LTO Left Lag RHS RTO RHS 10 20 30 GD 70 GG2 2s 0.33 s 1.5s 0.667s None of the Included answers is correct
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- Please do not give solution in image format thanku What server responds first? Group of answer choices The first reply comes from 216.93.145.253 in Frame 46 The first reply comes from 216.93.145.247 in Frame 50 The first reply comes from 172.19.3.22 in Frame 52 The first reply comes from 172.19.3.6 in Frame 48import numpy as np import json img_codes = np.load("data/image_codes.npy") captions = json.load(open('data/captions_tokenized.json')) for img_i in range(len(captions)): for caption_i inrange(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] network = CaptionNet(n_tokens).to(DEVICE) optimizer = torch.optim.Adam(network.parameters(), lr=1e-3) batch_size = 128 n_epochs = 100 n_batches_per_epoch = 50 n_validation_batches = 5 for epoch in range(n_epochs): train_loss = 0 network.train() for _ in tqdm(range(n_batches_per_epoch)): images, captions = generate_batch(train_img_codes, train_captions, batch_size) images = images.to(DEVICE) captions = captions.to(DEVICE) loss_t = compute_loss(network, images, captions) # clear old gradients; do a backward pass to get new gradients; then train with opt # YOUR CODE HERE train_loss += loss_t.detach().cpu().numpy() train_loss /=…Create client programmes that utilise your edge-weighted digraph to generate edge-weighted digraphs with a high percentage of negative weights but just a few negative cycles, for as wide a range of V and E values as feasible.
- You have an enrichment pipeline that uses Azure Cognitive Search for images. You need to persist the normalized images. Which three strings should you include in the knowledge store definition? Select all answers that apply. A. sourceContext B. storageConnectionString C. storageContainer D. source E.generatedKeyNameWe are building a CNN network for image classification. The image dmension is 64*64*3. Here is the network. CONV1: 64 filters of size 3*3*x1, stride 1, padding p1, (output image dimension is same as input); CONV2: 32 filters of size 5*5*x2, stride 1, padding p2, (output image dimension is same as input); POOL1: filter size 2*2, stride 2, padding 0 (output dimension is D) a) Find, D, x1, x2, p1, p2. (10) b) How many parameters this model needs to learn for this problem.(a). Without running the code in Python, describe the architecture for the followingdeep learning model, including number of layers, dimension of images in each layer,number of parameters in each layer. Show details of all you calculations.inputs = keras.Input(shape=(36, 36, 1))x = layers.Conv2D(filters=16, kernel_size=5, activation="relu")(inputs)x = layers.MaxPooling2D(pool_size=3)(x)x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(x)x = layers.MaxPooling2D(pool_size=2)(x)x = layers.Conv2D(filters=128, kernel_size=2, activation="relu")(x)x = layers.Flatten()(x)x = layers.Dense(128, activation="relu")(x)x = layers.Dropout(0.5)(x)outputs = layers.Dense(10, activation="softmax")(x)model = keras.Model(inputs=inputs, outputs=outputs)
- Answer Q1&Q2 please. Q)1 Apply the following filters on the given image below showing the final result image andelaborating on the resulting effect.- What will the result be when you use a 3x3 medianfilteron the image below?- What will the result be when you use a 3x3 averagefilteron the image below? The image is already zero-padded. 0 0 0 0 0 0 100 100 100 0 0 90 5 110 0 0 100 110 110 0 0 0 0 0 0 Q)2 You are requested to perform image compression using arithmetic coding. You have 3 possibleletters {a, b, c} and their probabilities are {0.2, 0.4, 0.4}. Decode the following float message {0.13}knowing that the original code was formed of three consecutive letters.Create client programmes that utilise your edge-weighted digraph to generate edge-weighted digraphs for a wide range of V and E values that have a significant portion of negative weights but only a few negative cycles.There are 5 page frames and 8 pages (numbered 0; 1; : : : ; 7). Assume that all frames are initially empty. The reference sequence is the following:7 1 2 3 1 4 2 0 5 6 2 5 0 3 1 2 1:(a) How many page faults will occur if the FIFO page replacement is used?(b) What if we use the "second chance" algorithm?Your answer should be justied (which references cause page faults and what is the reason).
- question in image. I cant tell why this code isnt compiling in Pep/9 BR MainLimit : . EQUATE 5number: . EQUATE O ; Local Variable# 1cMain : SUB SP1 , I ; push # numbernumber : . BLOCK 2LDBA intIN, d ; scant ("%d", & number)STDB number, dLDBA number, d ; while ( numbers < Limit )ADDA 1, ;STWA number ,dBR whileendwh : STOP. ENDQ1) 1-In image restoration process, the degraded image in frequency domain G(U,V) is defined as: G(U,V) = HU,V)F(U,V)+N(U,V) 1- Depict the degradation model 2-Ablur filter of 2x2 size has h(0,0) =8, h(1,0) =5, h(0,1) =7, h(1,1) =4 represents a motion blur of an object. For the location (0,0) of the blur filter, calculate the following: a- the discrete Fourier transform DFT at this location. b- calculate the magnitude, in frequency domain, for both the inverse filter and wiener filter at this location if §1 (0, 0)= 0.7 and Sx(0, 0)=0.01.Which of the following statements about the UML Class Diagram below are correct? (UMLClassDiagram.jpg (attached)) Check all that apply. A. a video must have at least one narrator B. all narrators are required to narrate videos C. the video on "Tactical Cat Breeding" has four narrators D. no video has more than two narrator every E. video is narrated F. some videos may not have any narrators