Bibliographic record
Abstract
In this talk, I describe new results on three research topics within my research group. We first describe some of the unusual optical properties of materials, known as epsilon-near-zero (ENZ) materials, for which the dielectric permittivity is very small. We describe some of the unusual geometrical optical properties of such materials and present theoretical predictions of how fundamental radiative properties are modified under such conditions. We also describe how these materials can display extremely large nonlinear optical effects under ENZ conditions [1,2]. The second topic of study is the filamentation of intense laser beams as they propagate through nonlinear optical materials. Our recent work has demonstrated that the threshold for filamentation can be raised by properly controlling the polarization properties of the transverse structure of the beam [3]. The third topic is quantum radiometry. Historically, black body radiation has been taken to constitute a calibration standard for optical instruments. However, the zero-point fluctuations of the electromagnetic field constitute another fundamental calibration standard. We have devised a means for calibrating the response of a spectrophotometer by utilizing these vacuum fluctuations to seed the process of spontaneous parametric down conversion [4].
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".