Wavelet analysis and its applications Associated People:
Bibliographic record
Abstract
remarkable progresses on applications of approximation theory and wavelet analysis to computational mathematics. By constructing wavelet bases for numerical solutions of biharmonic equations, R. Q. Jia and W. Zhao have shown that the numerical performance of their computational scheme is much better than any other known methods. R. Q. Jia and his students introduced the fastest algorithm so far for image denoising based on difference schemes. Their algorithms have been successfully applied to image processing by several research groups. Due to its ability to extract the multiscale structure in the data, wavelet transform has been proved to be a very successful tool widely used in many applications such as image compression, signal denoising and sampling, and computer graphics. To efficiently capture edge information in high-dimensional data, it is of fundamental importance to have directional systems to remedy the shortcoming of tensor product wavelets. University of Alberta mathematician Bin Han has successfully built high-dimensional directional framelets which not only inherit the usual advantages of wavelets such as multiscale representation and fast algorithm but also has the ability to capture directionality for dimensional data. Bin Han and his students are currently working on the applications of such directional framelets in image processing.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".