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I am building a React + Vercel application that needs to work with the official Canadian immigration form IMM5710. The form is an XFA-based dynamic PDF that: shows “Please wait...” in browsers opens correctly only in Adobe Acrobat Reader is an official IRCC government form Current setup: React + Vite frontend Vercel serverless backend CommonJS backend Existing upload route: /api/upload Current goal: upload IMM5710 PDF extract its data (XFA/XML) normalize into JSON auto-fill existing frontend form Future goal: reuse same structure later for generating/filling the official IMM5710 PDF. Questions: What is the best approach/library in Node.js to extract XFA data from IMM5710?

Amazon Seller Central requires valid business and account information to create a seller account, but I do not currently have a real Amazon seller business/account. Is there a way for developers to create a developer-only Amazon Seller account? What is the recommended approach for developers in this situation?

THE CONTEXT I am adding shadow mapping to my DirectX 12 renderer. So i need to access lights both individually and the whole array. I decided to use a StructuredBuffer to store the lights. The main constraint i had is that DX12 forces me to use a struct that is 256 bytes aligned to be able to feed it to SetGraphicsRootConstantBufferView. THE TEST SCENE Simple scene. One plane two spheres and two lights THE ISSUE When i render the scene with my new changes i get black render SOLUTION Two possibilities. Either the shadow mapping doesnt work properly(1) either the light buffer is broken(2) // Here what the DX12 light struct look like. HLSL struct matches this. struct DX12Light { DirectX::XMFLOAT4 position; DirectX::XM

I am extracting streaming subscriber data from text using an LLM, and I get results like this: { "raw_extractions": [ { "platform_mention": "Netflix", "year_mention": "2012", "subscriber_mention": "roughly 30 million subscribers worldwide" }, { "platform_mention": "Netflix", "year_mention": "2020", "subscriber_mention": "just under 200 million" }, { "platform_mention": "Netflix", "year_mention": "2022", "subscriber_mention": "hovered around 220 million subscribers" } ] } I need to convert this into clean time-series data for analysis: | year | platform | subscribers_min | subscribers_max | confidence | |------|----------|----------------|-----------------|------------|

I'm wanting to use Python to create an additional 5 sets of data per distance PER material I have. I have my CSV's Set up into two columns as such with 5 pieces of data per distance: Distance(mm) Counts 100 112 100 105 100 119 100 122 100 117 150 89 150 84 and so on up to 300mm increasing in 50mm increments. I have multiple CSV's set up like this for each material. Now what I want to do is increase each distances data from 5 to 10, 5 real world data and 5 predicted synthetic data to increase the strength of my lab reports results and to look better to the lecturers. Now what I have for code is the following for one of the CSV's:

The error notification like that: <timestamp>: ERROR [TNBHSUR_7331]: Error parsing python interpreter I tried to re-install but missing requirements. Maybe need a custom loader?

I am currently a second year college student. I am studying audio engineering/music with a minor in CS. As a summer project, I've decided to make a prototype of a rhythm game; this would combine both of my interest fields. However, I don't feel so confident in my programming skills yet. I am being taught Java at school and want to work with haxe/haxeflixel since I am used to OOP. I want advice on how to go into my journey without overthinking or falling into rabbit holes. I also don't want to rely on AI for this. I feel more or less comfortable with general programming concepts and oop like methods, classes/objects, loops, flow control amongst others. Before this last semester of college I did not have any experience with coding. Could you help guiding me?

I am experimenting with using kmeans clusters to examine whether the clusters fall into expected categories or not (specifically examining land cover between areas around species occurrence records and random points). I have created my clusters using kmeans and visualized them using fviz_cluster, but to do this I had to strip my factor variable (occurrence or random) from the numeric dataset (percentage of each land cover type). I would like to reattach this factor variable so that I can plot the labels of each point (occurrence or random) on the fviz_cluster, but I'm new to these functions and am not sure where/how to attach them for plotting. Example code: library(factoextra) type<-c(rep("occurrence",3),rep("random",3)) water<-c(18.8,rep(17.2,4),16.6) barren<-c(3.34,rep(3.20,4),3.21) evergreen<-c(75.5,rep(72.2,4),77.8) mixed<-c(0.461,rep(0.457,4),0.459) test_frame<-data.frame(type,water,barren,evergreen,mixed)

Not sure if SQL database project build can show an error, when a sproc with insert has incompatible column mappings Ex: Table1.col1 is VARCHAR(MAX) Table2.col1 is VARCHAR(20) CREATE PROC dbo.[usertest] AS INSERT Table2 (col1) SELECT col1 FROM Table1; When I build my project I see no build error. Can't figure if there is any setting to help, which will raise an error or warning. As col1 in table2 is smaller as table1 col1 is larger.