“Brain-Machine-Brain Wireless Interfaces for Intracortical Biosensing and Subsequent Treatments” in Talk by DL Mohamad Sawan at SSCS-New York and Santa Clara in May and June [People]
Bibliographic record
Abstract
FALL 20 11 IEEE SOLID-STATE CIRCUITS MAGAZINE IIn two lectures at SSCS-New York in May and SSCS-Santa Clara in June, Prof. Mohamad Sawan of Polytechnique Montreal focused on circuits and systems techniques for the design, implementation, and integration of biosensing and treatment microsystems. He described fully implantable devices that interact with the neural system and interconnect with intracortical neural tissues to bidirectionally exchange data with an external base station, with wireless links used for powering up. antenna structure, its radiation effi ciency, and simulation methodology. All who were polled agreed that Prof. Niknejad’s technical vision and stunning presentation set a new bar for SSCS-Vancouver. —Shahriar Mirabbasi Chair, SSCS-Vancouver
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".