Fluid input devices and fluid dynamics-based human-machine interaction
Bibliographic record
Abstract
We propose a highly expressive input device having keys that each generate an acoustic sound or similar disturbance when struck, rubbed, or hit in various ways. A separate acoustic pickup is used for each key, and these pickups are connected to a computer having an array of analog inputs. Non-binary, continuous sensitivity allows a smooth ("fluid") range of control. We describe three embodiments of the input device, one that works in each of the 3 states-of-matter: solid, liquid, and gas. These use (respectively) geophones, hydrophones, and microphones as the pickup devices. When used with liquid or gas, the device becomes a fluid-dynamic user-interface, comprising an array of fluid flows that are sensitive to touch. Sounds are produced by a Karman vortex street generated across a separate shedder bar, shedder orifice, or other sound producing device for each finger hole. Data is entered by covering the holes in various ways. This gives highly intricate variations in each keystroke by using a concept we call "finger embouchure", akin to the embouchure expression imparted to a flute by the shape of a player's mouth. We also present the concept of an array of frequency shifters, which we refer to as a shifterbank. The shifterbank helps in providing meaningful audible feedback for the data input, as well as helping in one very specialized form of data entry, namely musical composition.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".