sgn | Shikata ga nai encoder

In general you should not think of cosine-similarities as an absolute measure that'd be comparable between models. That is, you should not think of "0.7" cosine-similarity as anything like "70%" similar, and choose some arbitrary "70%" threshold to be used across models.

Instead, it's only a measure within a single model's induced space - with its effective 'scale' affected by all the parameters & the training data.

One small exercise that may help illustrate this: with the exact same data, train a 100d model, then a 200d model. Then look at some word pairs, or words alongside their nearest-neighbors ranked by cosine-similarity.

With enough training/data, generally the same highly-related words will be nearest-neighbors of each other. But the effective ranges of cosine-similarity values will be very different. If you chose a specific threshold in one model as meaning, "close enough to feed some other analysis", the same threshold would not be sufficient in the other. Every model is its own world, induced by the training data & parameters, as well as some sources of explicit or implicit randomness during training. (Several parts of the word2vec algorithm use random sampling, but also any efficient multi-threaded training will encounter arbitray differences in training-order via host OS thread-scheduling vagaries.)

If your parameters are identical, & the corpora very-alike in every measurable internal proportion, these effects might be minimized, but never eliminated.

For example, even if people's intended word meanings were perfectly identical, one year's training data might include more discussion of 'war' or 'politics' or some medical-topic, than another. In that case, the iterative, interleaved tug-of-war in training updates will mean words from that overrepresented domain have far more push-pull influence on the final model word positions – essentially warping subregions of the final space for finer distinctions some places, and thus *coarser distinctions in the less-updated zones.

That is, you shouldn't expect any global-per-model scaling factor (as you've implied might apply) to correct for any model-to-model differences. The influences of different data & training runs are far more subtle, and might affect different 'neighborhoods' of words differently.

Instead, when comparing different models, a more stable grounds for comparison is relative rankings or relative-proportions of words with respect to their closeness-to-others. Did words move into, or out of, each others' top-N neighbors? Did A move more closely to B than C did to D? etc.

Even there, you might want to be careful about differences in the full vocabulary: if A & B were each others' closest neighbors year 1, but 5 other words squeeze between them in year 2, did any word's meaning really change? Or might it simply be because those other words weren't even suitably represented in year 1 to receive any position, or previously had somewhat 'noisier' positions nearby? (As words get rarer their positions from run to run will be more idiosyncratic, based on their few usage examples, and the influences of those other sources of run-to-run 'noise'.)

Limiting all such analyses to very-well-represented words will minimize misinterpreting noise-in-the-models as something meaningful. Re-running models more than once, either with same parameters or slightly-different ones, or slightly-different training data subsets, and seeing which comparisons hold up across such changes, may also help determine which observed changes are robust, versus methodological artifacts such as jitter from run-to-run, or other sampling effects.

A few previous answers on similar questions about comparing word-vectors across different source corpora may have other useful ideas or caveats for you:

how calculate distance between 2 node2vec model

Word embeddings for the same word from two different texts

How to compare cosine similarities across three pretrained models?

If you can use std::bit_cast, you can manipulate floating point types cast to integer types. The portability is limited to the representation of double, but if you can assume the IEEE 754 double-precision binary floating-point format, cast to uint64_t and using sign bit should work.

got it: the answer is to use matplotlib.Patches.Polygon

This is quite hard to optimize this loop. The main issue is that each iteration of the loop is dependent of the previous one and even instructions in the loops are dependent. This creates a long nearly sequential chain of instruction to be executed. As a result the processor cannot execute this efficiently. In addition, some instructions in this chain have a quite high latency: tzcnt has a 3-cycle latency on Intel processors and L1 load/store have a 3 cycle latency.

One solution is work directly with registers instead of an array with indirect accesses so to reduce the length of the chain and especially instruction with the highest latency. This can be done by unrolling the loop twice and splitting the problem in two different ones:

This answer essentially is a more detailed version of the comments, essentially finding that what you want to do is not possible.

You ask for a way to

Certify Document when Approval Signature exist

According to the current PDF specification:

Thus, you cannot validly add a certification signature to a PDF which already has approval signatures.

(This does not automatically mean that all validators will detect the issue if you add a certification signature after approval signatures, let alone correctly display the cause. Many validators only do a subset of the checks that strictly speaking are necessary...)

More question if the certification signature come first then next is approval like 3 signature approval, can the last the certification setMDPPermission with 1 ? so at the end the document can't add more approval signature

setMDPPermission adds a DocMDP transform to the signature, and such a transform makes the signature a certification signature. Thus, using this method when signing a document that already has an approval signature, will create an invalid PDF or fail entirely.

You can lock a PDF document with an approval signature, though, if you add a Lock dictionary to the signature field with a P entry of 1. Beware, though, this is a ISO 32000-2 feature originally introduced as a feature of an Adobe Extension to ISO 32000-1. Not all PDF viewers support ISO 32000-2 yet, so some viewers may not respect this entry.

Add width attribute to task-contianer and add your fotter class attribute on your CSS file.

I think the problem is in the object you call in html2canvas. I made a codesandbox where I use an accordion and capture the open element to render it on a canvas.

The steps are:

1. Get the panel that is open
2. Get the panel dimensions to resize the canvas and clean the canvas
3. Paint on the canvas
4. Download image

I leave below the most important function

I would start by declaring a common pattern for construction. Something like this:

Must be Neovim-0.6, I guess. For good or bad, it has few :h default-mappings builtin. Personally, I just kill'em all ASAP: