bobcat | templates handling child processes streams/sockets | Runtime Evironment library

I want to select nodes from an XML that have consecutive child nodes with values matching with the respective words from my search term.

Here is a sample XML:

I need to get rows that contains exact column value cat and lion and a column value containing saurus

For exact match I know this

I have a Google Cloud Storage bucket that looks like:

What is the best way to fix the issue where the form fields values cant be seen when selected when the table fits the screen. I want the table to scroll at least and have the form fields be actually visible to the eye. Specifically you can see this in the innings pitched.

I have a list of forbidden words. I have to check if one of those forbidden words is inside a given string. My current code working fine partially.

A match should be true only and only if:

1. any of the words in the string is an exact match with any of the forbidden words, e.g.: the pool is cold.
2. any of the words in the string starts with any of the forbidden words, e.g.: the poolside is yellow.

A match should be false otherwise, and that includes both of these cases which are not currently working fine:

1. if any of the words in the string ends with any of the forbidden words, e.g.: the carpool lane is closed.
2. if any of the words in the string contains any of the forbidden words, e.g.: the print spooler is not working.

Current code:

I am trying to create a big regex from many options in a file, to be used in gawk. The goal is to find matches in lines.txt which match ANY of the options in regex.txt

File of lines to be searched

I am getting an OOM error when trying to read a large result set using MyBatis. MyBatis version 3.5 I want to iterate over the cursor instead of loading the entire query result. Does anyone have a working example of returning a cursor from mybatis select instead of entire result.

I have a query regarding the extraction of VGG16/VGG19 features for my experiments.

The pre-trained VGG16 and VGG19 models have been trained on ImageNet dataset having 1000 classes (say c1,c2, ... c1000) and normally we extract the features from first and second fully connected layers designated ('FC1' and 'FC2'); these 4096 dimensional feature vectors are then used for computer vision tasks.

My question is that can we use these networks to extract features of an image that does not belong to any of the above 1000 classes ? In other words, can we use these networks to extract features of an image with label c1001 ? Remember that c1001 does not belong to the Imagenet classes on which these networks were initially trained on.

In the article available on https://www.pyimagesearch.com/2019/05/20/transfer-learning-with-keras-and-deep-learning/, I am quoting the following -

When performing feature extraction, we treat the pre-trained network as an arbitrary feature extractor, allowing the input image to propagate forward, stopping at pre-specified layer, and taking the outputs of that layer as our features

From the above text, there is no restriction to whether the image must necessarily belong to one of the Imagenet classes.

Kindly spare some time to uncover this mystery.

In the research papers, the authors simply state that they have used features extracted from VGG16/VGG19 network pre-trained on Imagenet dataset without giving any further details.

I am giving a case study for reference:

Animal with Attribute dataset (see https://cvml.ist.ac.at/AwA2/) is a very popular dataset with 50 animal classes for image recognition task. The authors have extracted ILSVRC-pretrained ResNet101 features for the above dataset images. This ResNet 101 network has been pre-trained on 1000 imagenet classes (different imagenet classes are available at https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a#file-imagenet1000_clsidx_to_labels-txt).

Also, the AWA classes are put as follows:

I am working (in an Angular project) with a form where the user selects something from a HTML Select. from the example below, the "Cat" and "Dog" are optgroup(s) and the other things listed are option(s) attributes. Im wondering if it is possible to have the user select from the drop down, say the "Lion" option, and the text that populates the collapsed version of the drop down to read something like.. Cat: Lion.

Cat

• Lion
• Bobcat Dog
• Wolf
• Clifford

Is it possible to do this with just Angular (not jquery)

thanks!

EDIT:

thanks to the help of shashank sharma's comment i was able to figure it out. i used the example he gave and was able to get it to work by making the label a variable in the typescript and then setting that variable in the onCategorySelection function

thing.component.html

{{animalKingdom}}

thing.component.ts