paperwork | Lightweight JSON validation for node.js REST APIs | REST library

QUICK & DIRECT but limited (update)

Include following function in Manifest under the driver column (assuming driver names are Sammy, Jose, Ian, Tame, etc.):

Answer: justify-content: center;

(You had a typo)

For making it responsive to mobile use media queries: https://www.w3schools.com/css/css_rwd_mediaqueries.asp

Also, hover on mobile acts like a touch input so I don't think you have a problem with that

When paper introduce they method they said:

The attention modules aim to exploit the relationship between disease labels and (1) diagnosis-specific feature channels, (2) diagnosis-specific locations on images (i.e. the regions of thoracic abnormalities), and (3) diagnosis-specific scales of the feature maps.

(1), (2), (3) corresponding to channel-wise attention, element-wise attention, scale-wise attention

We can tell that element-wise attention is for deal with disease location & weight info, i.e: at each location on image, how likely there is a disease, as it been mention again when paper introduce the element-wise attention：

The element-wise attention learning aims to enhance the sensitivity of feature representations to thoracic abnormal regions, while suppressing the activations when there is no abnormality.

OK, we could easily get location & weight info for one disease, but we have multiple disease:

Since there are multiple thoracic diseases, we choose to estimate an element-wise attention map for each category in this work.

We could store the multiple disease location & weight info by using a tensor A with shape (height, width, number of disease):

The all-category attention map is denoted by A ∈ RH×W×C, where each element aijc is expected to represent the relative importance at location (i, j) for identifying the c-th category of thoracic abnormalities.

And we have linear classifiers for produce a tensor S with same shape as A, this can be interpret as:

At each location on feature maps X(CA), how confident those linear classifiers think there is certain disease at that location

Now we element-wise multiply S and A to get M, i.e we are:

prevent the attention maps from paying unnecessary attention to those location with non-existent labels

So after all those, we get tensor M which tells us:

location & weight info about certain disease that linear classifiers are confident about it

Then if we do global average pooling over M, we get prediction of weight for each disease, add another softmax (or sigmoid) we could get prediction of probability for each disease

Now since we have label and prediction, so, naturally we could minimizing loss function to optimize the model.

Following code is tested on colab and will show you how to implement channel-wise attention and element-wise attention, and build and training a simple model base on your code with DenseNet121 and without scale-wise attention:

Ax is an array of subplots because you created more than one. So in order to set the titles of the subplots, you need to iterate through them as well. You could fix this fairly easily like so:

The ftplib has no issue with spaces. The problem are the quotes you add to the RETR command. There should be no quotes:

There are three problems:

1. Though newItems is an array, the v-for iterates it using 3 arguments, which is a syntax only for iterating objects. So tid is undefined everywhere in the loop. You can find it on item.tid instead.

2. You've tried to access item.index in one of the s but the items don't have an index property, they have only tid and tname:

Here's a jq solution that assumes the CSV input is very simple (e.g., no field has embedded commas), followed by a brief explanation.

To handle arbitrary CSV, you could use a CSV-to-TSV conversion tool in conjunction with the jq program given below with trivial modifications.

The following jq program assumes jq is invoked with the -R option. (The -n option should not be used as the header row is read without using input.)