Water Jets as Pixels: Water Fountains as Both Sensors and Displays
Bibliographic record
Abstract
We propose a hydraulic user interface consisting of an array of spray jets and the appropriate fluid sensing and fluid flow control systems for each jet, so that the device functions as a fluid-based tactile user interface. Our array of fluid streams work like the keys on a keyboard, but where each fluid stream can also provide tactile feedback by dynamically modulating the pressure of the fluid spray, so that the keyboard is actually bi-directional (i.e. is both an input and an output device). A 104-jet version can be used as a fun and tactile "QWERTY..." style keyboard. More importantly, however, the device can also be used for applications, such as musical instruments, where its more expressive multi-dimensional input capabilities can be put to full use. One such instrument, the hydraulophone (hydrauliphone, hydraulaphone), is a hollow tubular object with a row of holes in it. It is played much like one would play a tin flute or recorder, by covering up the holes to restrict fluid flow. This gives rise to a fun new way of playing music by successively blocking water jets in a fountain, or while frolicking in a pool, or splash pad. Additionally, the hydraulophone can be used as a teaching tool to help children learn music by playing in the water. We demonstrate this teaching capability by way of an implementation of the arcade game "Touch Me" using a hydraulic user interface. We describe some of our present and upcoming installations of these devices in public parks, pools, and aquatic play areas
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".