Pseudo-differential operators : complex analysis and partial differential equations : international workshop, York University, Canada, August 4-8, 2008
Bibliographic record
Abstract
Boundary Value Problems with the Transmission Property.- Spectral Invariance of SG Pseudo-Differential Operators on L p ? n .- Edge-Degenerate Families of Pseudo-Differential Operators on an Infinite Cylinder.- Global Regularity and Stability in S-Spaces for Classes of Degenerate Shubin Operators.- Weyl's Lemma and Converse Mean Value for Dunkl Operators.- Dirichlet Problems for Inhomogeneous Complex Mixed-Partial Differential Equations of Higher order in the Unit Disc: New View.- Dirichlet Problems for the Generalized n-Poisson Equation.- Schwarz, Riemann, Riemann-Hilbert Problems and Their Connections in Polydomains.- L p -Boundedness of Multilinear Pseudo-Differential Operators.- A Trace Formula for Nuclear Operators on L p .- Products of Two-Wavelet Multipliers and Their Traces.- Pseudo-Differential Operators on ?.- Pseudo-Differential Operators with Symbols in Modulation Spaces.- Phase-Space Differential Equations for Modes.- Two-Window Spectrograms and Their Integrals.- Time-Time Distributions for Discrete Wavelet Transforms.- The Stockwell Transform in Studying the Dynamics of Brain Functions.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".