Decomp Computer

[Clara Zellinger]

More generally, functional decomposition in computer science is a technique for mastering the complexity of the function of a model. A functional model of a system is thereby replaced by a series of functional models of subsystems.[3]

A decomposition paradigm in computer programming is a strategy for organizing a program as a number of parts, and usually implies a specific way to organize a program text. Typically the aim of using a decomposition paradigm is to optimize some metric related to program complexity, for example a program's modularity or its maintainability.

Most decomposition paradigms suggest breaking down a program into parts to minimize the static dependencies between those parts, and to maximize each part's cohesiveness. Popular decomposition paradigms include the procedural, modules, abstract data type, and object oriented paradigms.

Though the concept of decomposition paradigm is entirely distinct from that of model of computation, they are often confused. For example, the functional model of computation is often confused with procedural decomposition, and the actor model of computation is often confused with object oriented decomposition.

A decomposition diagram shows a complex, process, organization, data subject area, or other type of object broken down into lower level, more detailed components. For example, decomposition diagrams may represent organizational structure or functional decomposition into processes. Decomposition diagrams provide a logical hierarchical decomposition of a system.

I wanted to build NNs that can predict certain properties about matrices which are NP-hard to compute but might require eigenvalue decomposition. However, I am not sure if hardcode-calculating eigenvalues at any specific layer would be a good idea, because I feel the need to find eigenvalues shouldn't be pre-determined at a certain layer and maybe yield issues in backpropagating errors.

This smells like an XY problem. A neural network is not a good tool to use for computing the eigenvalue decomposition. Machine learning is best for problems where you have examples of input-output pairs but you don't know of a classical algorithm to solve the problem. That doesn't apply here.

If you think that the eigenvalue decomposition of a matrix might help a neural network to learn the desired output, then compute the eigenvalue decomposition via a standard algorithm and feed it in as an additional input (additional features).

That said, I feel wary about the entire direction you are sketching. If you are trying to solve a NP-hard problem, I suspect there's a good likelihood that neural nets are probably not a helpful direction. Neural nets are not magic pixie dust that can solve every problem. See, e.g., Evolving artificial neural networks for solving NP problems and and

The concept of computer generated reaction modelling was broadened through the development of a general planar graph algorithm for determination of isomorphism. The previous capability was limited by its inability to determine the uniqueness of ring-containing species unambiguously, restricting the application of automatic network generation to non-cyclic species or cyclic species where the ring was not involved in the chemical transformation. In this work, the systematic identification of both noncyclic and cyclic species was carried out by constructing the structurally explicit decomposition tree, an assembly of the biconnected components of the graph, from which a graph invariant unique string code was obtained by iteratively encoding and ordering the subtrees of the decomposition tree. A lexicographical comparison of the unique string code of the candidate species with the string codes of all previously generated species with the same empirical formula allowed unambiguous determination of species uniqueness.

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I'm assuming that the folks at Empty Clip have a basic decomp going. We could theoretically have one based on all the info we have scattered about here. Wboy's 30-team provides some interesting data points to look into and with a number of the things modders have figured out here, we should compile the info and start mapping out the structure. I know smoz had mentioned it a few times in the past, but it's a big-ass thankless job. LOL

The only decompile projects I've seen are the ones you've mentioned, and none of them have been Sega games. I would think that if anyone had seriously tried they would have done so first on Sonic 1, as a proof of concept principally but to spearhead the movement on Genesis games.

We have a couple decomps down to the assembly code somewhere on here (and another on the discord? I should copy that onto the forum if it's not here...). I opened up the ROM in IDA Pro (reverse engineering tool) many years ago, too.

It comes down to having people with the expertise and the time, but we don't really have that compared to those bigger and more traditional-gamer games. I don't think we have any games programmers here who know how to rewrite the graphics stuff, controller inputs, etc..so it'd be a lot of work for someone to learn.

I think there is a fair amount of decomp in Sonic games, but they choose to just keep it on emulators instead. There's a whole site that lists each hex command so you have an idea of what's happening. It's not a proper decomp but it's been broken down to understand things better.

Abdul @bcrt2000 has been working on a homebrew port of NHL95PC for modern systems, which is similar to NHL 94, just with a season mode and more bells and whistles.

I went down this same path a few months ago. Some folks on the discord pointed me to the partial dasm project by MarkyJester, which actually is able to compile a bit perfect version of NHL94. There still needs to be a lot done to separate the sound effects, music, tiles, player/teams db from game code, and then catalog the game code. Not to be discouraging, I felt like this effort would probably take me 5000-10000 hours to do, whereas doing a from scratch engine remake of the game in Unity would probably closer to 1000-2000 hours of effort for me.

That being said, I do think as a community it makes a lot of sense to do a dasm project of the original ROM, and if the community is successful, that could become the basis of any future rom edits, and add a lot more flexibility to what features could be added to the game (fighting, season mode)

I haven't fully settled on what it is yet-- at worst its going to be a best of both worlds hybrid of NHL 94 Genesis, and NHL 95 PC (+ fighting from 93). At best its going to be customizable and you can choose what you want from each version (and for the game to work in widescreen, I may need to do a new rink & a different rink-to-player ratio).

But I think the menus are just more intuitive from the console versions, so currently the working version's menu is based of NHL 94, and I think the Genesis version feels better to play, so while I do want the difficulty of scoring to be harder (maybe via a difficulty slider), I do want the game to play like the Genesis version. But I'm just settling on calling it an engine remake of NHL 94 to keep it simple for now

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An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

In this answer to Decomposition groups for the Galois module $\mu_8$KConrad computes the decomposition groups over a number field in a special case using an ad hoc method. See also this answer to Biquadratic extension of global function fields with cyclic decomposition groupsin which Will Sawin computes the decomposition groups over $\Bbb F_q(t)$ using an ad hoc method.

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