Multidimensional vector radix FHT algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, efficient multidimensional (M-D) vector radix (VR) decimation-in-frequency and decimation-in-time fast Hartley transform (FHT) algorithms are derived for computing the discrete Hartley transform (DHT) of any dimension using an appropriate index mapping and the Kronecker product. The proposed algorithms are more effective and highly suitable for hardware and software implementations compared to all existing M-D FHT algorithms that are derived for the computation of the DHT of any dimension. The butterflies of the proposed algorithms are based on simple closed-form expressions that allow easy implementations of these algorithms for any dimension. In addition, the proposed algorithms possess properties such as high regularity, simplicity and in-place computation that are highly desirable for software and hardware implementations, especially for the M-D applications. A close relationship between the M-D VR complex-valued fast Fourier transform algorithms and the proposed M-D VR FHT algorithms is established. This type of relationship is of great significance for software and hardware implementations of the algorithms, since it is shown that because of this relationship and the fact that the DHT is an alternative to the discrete Fourier transform (DFT) for real data, a single module with a little or no modification can be used to carry out the forward and inverse M-D DFTs for real- or complex-valued data and M-D DHTs. Thus, the same module (with a little or no modification) can be used to cover all domains of applications that involve the DFTs or DHTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it