Anderson localization of ultrasonic waves in three dimensions.
Bibliographic record
Abstract
There is currently a resurgence of interest in Anderson localization, where multiply scattered waves become “trapped” through interference in a very strongly scattering medium. This interest is fueled by theoretical and experimental advances, especially for classical waves, where unambiguous experimental evidence for localization in three dimensions has remained elusive until recently. In this talk, progress in demonstrating the localization of ultrasound in a “mesoglass,” made by assembling aluminum beads into a three-dimensional (3-D) elastic network, is summarized. Measurements of the time-dependent transmission of the ultrasonic intensity, as well as the dynamic transverse confinement of the waves due to localization, are discussed. The data are well described by a new self-consistent theory of the dynamics of localization, providing an important validation of this theoretical approach and enabling the localization length to be measured. Finally, evidence for non-Gaussian statistics of the transmitted intensity is discussed, consistent with values of the Thouless conductance ɡ less than 1. This is the first time that these three different fundamental aspects of Anderson localization (time-dependent transmission, transverse confinement of the waves, and statistics) have been studied simultaneously, providing very convincing evidence for 3-D localization of ultrasound in these materials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".