Bibliographic record
Abstract
Several different distributed computational universes have been considered and studied within the interdisciplinary field called Programmable Matter.In this field, the matter is envisioned as a very large number of micro and nano-sized computational entities with limited capabilities programmed to collectively perform a task without the need for any central or external intervention.Within distributed computing, several theoretical active and hybrid models for programmable matter have been proposed.Within these models, a central concern has been the formation of geometric shapes; among them, the line is especially important.An important requirement, common to most research, is the connectivity of the operating elements at all times.In the extensive literature on the problem of shape formation in programmable matter systems, it is almost generally assumed that the system elements never fail.Hence the problem of reconfiguring the shape following the failure of some elements has been neglected.In this thesis we studied the problem of handling failures when the shape is the line.We considered first of all the Connected Line Recovery problem requiring the nonfaulty elements to restore the line shape following the failures of some of the elements.We examined the instance of this problem in the programmable matter systems defined by the Metamorphic Robots and Amoebot models.We then studied the more complex Dynamic Line Maintenance problem when the faults are fully dynamic (i.e., can occur at any time).We examined the instance of this problem in the systems defined by the Amoebot and the Hybrid Programmable Matter models.For both problems and the systems considered, we provided a near complete feasibility characterization of problems, identifying the conditions necessary for their solvability, and constructively proving the sufficiency of those conditions.In particular, we presented solution protocols that operate correctly, maintain connectivity of the non-faulty entities, without constraints on the number of entities that will become faulty, nor on the location, nor (in the dynamic case) on the time of the occurrence of each fault.Our impossibility results hold even under the weak fully-synchronous scheduler, while the possibility results hold under the more difficult semi-synchronous one.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".