Multicolor Tunable Photonic Reservoir Computing
Bibliographic record
Abstract
Reservoir computing (RC) is a bioinspired computational paradigm that employs nonlinear dynamical systems (i.e. reservoir) to increase the dimensionality of sequential data [1] . The reservoir is fixed and only the readout is trained with a simple method such as linear regression to map the states of the reservoir to a targeted pattern. A delay line together with a nonlinear node, constitutes the basic topology of a delay-feedback reservoir. The complex dynamics of such systems is engaged with time multiplexing to create a number of virtual time nodes over the delay time associated with the feedback loop. The delay time is usually harmonized to the time sequence of the input data. To create a sufficiently strong nonlinear mapping, the independent internal states of the reservoir must be increased by time multiplexing at a rate much faster than the delay time. The number of virtual nodes created by time multiplexing is thus limited by the speed at which the input data is masked (e.g. by modulating the sampled-and-hold input data). In most applications, hundreds of nodes are typically required. This necessitates multiplexing with the speeds hundreds of times faster than the input data stream. To overcome the inherent trade-off between the number of neurons and the processing time, parallelization schemes might be envisioned [2] , [3] .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".