uncertain | Fast and correct computations with uncertain values

I was reading a decent paper S-DCNet and I fell upon a section (page3,table1,classifier) where a convolution layer has been used on the feature map in order to produce a binary classification output as part of an internal process. Since I am a noob and when someone talks to me about classification I automatically make a synapse relating to FCs combined with softmax, I started wondering ... Is this a possible thing to do? Can indeed a convolutional layer be used to classify a binary outcome? The whole concept triggered my imagination so much that I insist on getting answers...

Honestly, how does this actually work? What is the difference between using a convolution filter instead of a fully connected layer for classification purposes?

Edit (Uncertain answer on how does it work): I asked a colleague and he told me that using a filter of the same shape as the length-width shape of the feature map at the current stage, may lead to a learnable binary output (considering that you also reduce the #channels of the feature map to a single channel). But I still don't understand the motivations behind such a technique ..

Now I have a nested list with integers and the amout of layers is uncertain based on user input. How can I go though each element and update its value?

For example, use char() for conversion.

I'd like to get an Azure Function working where the function gets triggered on every new item, modify of an existing item, or deletion of an item in the CosmosDB. The documentation i've been able to find so far is always about CosmosDB functioning as a documentDB.

However, I am using cosmosdb as a table storage for simple data. I've also found seperate documentation for Azure Table Storage which would trigger Azure Function, but i'm uncertain if that applies to CosmosDB also? To me it doesn't seem usable as it triggers on a queue message. https://docs.microsoft.com/en-us/azure/azure-functions/functions-bindings-storage-table-input?tabs=python

I currently have a function which puts data into the table using the python SDK. So there is not queue message to trigger off of.

Does anyone know how I create a function like this? A trigger on the control plane operation for the Table? So I can have the function look at the data in the table for that specific request.

For an Android app that uses Jeremy Feinstein's SlidingMenu and targets API level 29, a problem has recently been noted on 2 devices (Samsung Galaxy A01 and Samsung Galaxy Note20 Ultra, running Android 11) whereby the content at the bottom of the screen is covered by the navigation bar.

The only thing that has made a difference on the 2 affected devices is adding this in the sliding menu constructors:

I hope someone can help me out with the following:

I am trying to dynamically hide rows based on values on another worksheet. However, I am getting the error "Next without For" but I am also uncertain as to the functionality of my code. I would very much appreciate it if someone could help me out here.

Tl;dr: I would like a function that randomly returns a float (or optionally an ndarray of floats) in an interval following a probability distribution that resembles the sum of a "Gaussian" and a uniform distributions.

The function (or class) - let's say custom_distr() - should have as inputs (with default values already given):

• the lower and upper bounds of the interval: low=0.0, high=1.0
• the mean and standard deviation parameters of the "Gaussian": loc=0.5, scale=0.02
• the size of the output: size=None
• size can be an integer or a tuple of integers. If so, then loc and scale can either both simultaneously be scalars, or ndarrays whose shape corresponds to size.

The output is a scalar or an ndarray, depending on size.

The output has to be scaled to certify that the cumulative distribution is equal to 1 (I'm uncertain how to do this).

Note that I'm following numpy.random.Generator's naming convention from uniform and normal distributions as reference, but the nomenclature and the utilized packages does not really matter to me.

Since I couldn't find a way to "add" numpy.random.Generator's uniform and Gaussian distributions directly, I've tried using scipy.stats.rv_continuous subclassing, but I'm stuck at how to define the _rvs method, or the _ppf method to make it fast.

From what I've understood of rv_continuous class definition in Github, _rvs uses numpy's random.RandomState (which is out of date in comparison to random.Generator) to make the distributions. This seems to defeat the purpose of using scipy.stats.rv_continuous subclassing.

Another option would be to define _ppf, the percent-point function of my custom distribution, since according to rv_generic class definition in Github, the default function _rvs uses _ppf. But I'm having trouble defining this function by hand.

Following, there is a MWE, tested using low=0.0, high=1.0, loc=0.3 and scale=0.02. The names are different than the "The issue" section, because terminologies of terms are different between numpy and scipy.

I have a dashboard in Tableau with two worksheets added. Right now Worksheet #1 has Filter #1 added for Field #1. Now that I’m adding Worksheet #2 with Field #2 (from a new table) to that dashboard, I’m uncertain how to use the same Filter #1 to restrict the time range for Field #2 and everything else on the dashboard.

To be clearer on my problem, here is my linkage in my data source I'm struggling with

I need to change a DecimalField from having decimal_places=0 to decimal_places=7 while keeping max_digits=50. I think all numbers in this column are between 0 and 1,000,000,000. So the data migration could be unproblematic. However, I'm uncertain.

I have seen the AlterField documentation and I think I found the source code. However, I would need an introduction to this. Is it possible to see the generated SQL queries?

I could imagine several things going wrong:

• Data Dropping if Decimal(decimal_places=0) is different from Decimal(decimal_places=7) in a way that Django/Postgres does not take care of
• Data Change if the internal representation stays the same but the values are interpreted in a different way
• Overflow if values get out of bounds
• NaN / NULL if values get out of bounds

The last two cases should not happen in my specific case, but I'm still interested how Django migrations would deal with out of bounds cases.

I am trying to pass a property as a delegate into a blazor component. However, I get a Delegate 'Func' doest not take 1 arguement error. And uncertain what the correct syntax should be. Below is the component: