Notice bibliographique
Résumé
This report provides the problems posed by the participants at the open problem session of the 26 Canadian Conference on Computational Geometry. This well-attended session was held Tuesday, August 12, 2014, as a scheduled session of the conference. Six participants presented a total of seven problems. All presenters kindly agreed to provide written versions of their problems, including references and attributions. The problems appear in the sections below. The references appear at the end. The text is essentially the same, modulo minor editing, as the text provided by the presenters. This material is not refereed. 1 Guarding Orthogonal Terrains presented by: Giovanni Viglietta Partition the plane into finitely many (possibly unbounded) orthogonal polygons, and extrude them in 3D, obtaining a set of “orthogonal skyscrapers” of different heights. Let n be the total number of vertices of the orthogonal polygons. We ask to find the minimum number (as a function of n) of vertex guards for the terrain induced by the skyscrapers. In other words, we seek to select a minimum number of “guards” among the vertices of the skyscrapers such that each point in 3-space lying “above” some skyscraper is visible to some guard, where lines of sight must not intersect a skyscraper’s top face or a side face. The best known lower bound is given by a row of k equal cuboidal skyscrapers, where n = 8k. In this case k + 1 vertex guards are needed, which yields a lower bound of (n/8) + 1 vertex guards. We conjecture n/8 + O(1) guards to be sufficient for all orthogonal terrains on n vertices (observe that an L-shaped skyscraper on 12 vertices needs three guards). To our knowledge, the problem is open even in the case of a single “tower” made of nested orthogonal prisms of increasing height, or a single “well”. For background, see [1]. ∗Professor, Department of Computer Science, U. of Victoria, Canada; email: sue@uvic.ca 1Postdoctoral Fellow, U. of Ottawa and School of Computer Science, Carleton U., Canada; email: viglietta@gmail.com 2 Flows on Terrains presented by: Jack Snoeyink What local actions can make a general difference for flow of water, nutrients, and pollutants in a terrain? This is more of an open application area for computational geometry techniques than an open problem. Consider a real-world terrain with patches having different soil types (e.g., different absorbency properties) together with a network of streams, house gutters, parking lot drains, and underground sewers. There are rain gauges reporting rainfall in cm/hr at some points and flow meters reporting liters/min profiles on some waterways. (These are increasingly common in the “internet of things.”) If we model a rainfall, do we see the measured flows? If not, can we suggest where our information about the flow network is incomplete or inaccurate? If we don’t like, say, the surge of flow in the sewers from a rainfall, can we suggest where rain gardens could most effectively delay the flow? At what scale should these questions be asked based on the sensors we have? There are many simulations that are used [2, 3], but the ideas of computational geometry (like continuous Dijkstra for paths in weighted regions [4], or partitioning terrain into catchments and capturing flow in equilibrium [5]) can be used to preprocess the terrain for more efficient exploration of modifications that would produce the observed or desired flow profiles. 2Professor, Dept. of Computer Science, U. North Carolina; email: snoeyink@cs.unc.edu 27 Canadian Conference on Computational Geometry, 2015
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,155 | 0,052 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».