Unified formulas for arbitrary order symbolic derivatives and anti-derivatives of the power-inverse hyperbolic class 1
Bibliographic record
Abstract
We continue on tackling and giving a complete solution to the problem of finding the nth derivative and the nth anti-derivative, where n can be an integer, a fraction, a real, or a symbol, of elementary and special classes of functions. In general, the solutions are given through unified formulas in terms of the Fox H-function which in many cases can be simplified to less general functions. In this work, we consider two subclasses of the power-inverse hyperbolic class. Namely, the power-inverse hyperbolic sine class { f ( x ) : f ( x ) = Σ l j =1 Pj ( x α j )arcsinh(β j x γ j ), α j ∈ C, β j ∈ C\{0},γ j ∈ R\{0}, (1) and the power-inverse hyperbolic cosine class { f ( x ) : f ( x ) = Σ l j =1 Pj ( x α j )arccosh(β j x γ j ), α j ∈ C, β j ∈ C\{0},γ j ∈ R\{0}, (2) where pj's are polynomials of certain degrees. One of the key points in this work is that the approach does not depend on integration techniques The arbitrary order of differentiation is found according to the Riemann-Liouville definition, whereas the generalized Cauchy n-fold integral is adopted for arbitrary order of integration. The motivation of this work comes from the area of symbolic computation. The idea is that: Given a function f in a variable x , can CAS find a formula for the n th derivative, the n th anti-derivative, or both of f ? This enhances the power of integration and differentiation of CAS. In Maple, the formulas correspond to invoking the commands diff( f ( x ) for the n th derivative and int( f ( x ), x$n ) for the n th anti-derivative. A software exhibition will be given using Maple. Example: A unified formula for arcsinh(√ x ) in terms of the Meijer G-function (arcsinh(√ x )) (n) = x (1/2-- n over2√π G 1,2 over 1,2 (1/2,1/2over0, n --1/2│ x ) , │ x │ < 1. (3). The above G-function reduces to the original function if n = 0. It gives derivatives of any order if n > 0 and anti-derivatives of any order if n < 0.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".