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The Ligamentum Venosum is ____________ to the Caudate Lobe.

Part A: Trait Analysis Table You are given four organisms: Brook trout Desert spadefoot toad Spruce Tree Red-tailed hawk Black bear Using the list of traits below, create your own summary table that indicates whether each trait is present or absent in each organism. Your table should clearly indicate which organisms share each trait. Traits to consider: Vertebrae Legs Shelled egg Feathers Warm-blooded (endothermy) Instructions: For each trait, show whether it is present or absent in each organism by selecting from the dropdown in each cell. Trait Brook Trout Spadefoot Toad Spruce Tree Red-tailed Hawk Black Bear Vertebrae Legs Shelled Egg Feathers Warm-blooded  

You will use a probe with a frequency of 10 MHz to scan the Liver.

The image is ____________ plane.

Part B: Trait Organization Below is a simplified phylogenetic tree showing the relationships among five organisms. The branching points are labeled Trait 1–4, but the evolutionary traits that define each branch are missing.            ┌── Red-tailed Hawk (Present)         │     Trait 4         │         └── Black Bear     Trait 3         │         └── Spadefoot Toad         │     Trait 2         │         └── Brook Trout     Trait 1         │         └── Spruce Tree (Toward Common Ancestor)   Instructions: Below is a list of five traits, but only four of them correctly describe the evolutionary branching points shown in the tree. Match each branching point (Trait 1–4) with the most appropriate trait from the list. Use each selected trait only once. One trait will not be used. Trait Options: (One trait does not fit in the tree – choose carefully.) A. Legs B. Warm-blooded C. Feathers D. Vertebrae E. Shelled egg  Branching Point Trait Name Point 1 Point 2 Point 3 Point 4  

Multi-layer perceptron. Consider the following neural network defined in PyTorch.  class NeuralNetwork(nn.Module):    def __init__(self):        super().__init__()        self.linear_relu_stack = nn.Sequential(            nn.Linear(20, 100),            nn.ReLU(),            nn.Linear(100, 100),            nn.ReLU(),            nn.Linear(100, 3),        )    def forward(self, x):        logits = self.linear_relu_stack(x)        return logits (a) (2 pts) How many learnable layers does the neural network have? Count only layers that contain trainable parameters. (b) (2 pts) How many parameters does the neural network have? You may disregard bias/offset terms.