2018 Seville Connect Workshop

January 29- February 2, 2018

The focus of 2018 Seville Connect Workshop will be on combinatorial and geometrical problems with applications in aerial robotics and flamenco music, Work Package 4: Graph-based algorithms for UAVs and for MIR.

ORGANIZER:

José Miguel Díaz Báñez, University of Seville, Spain.

PLACE:

Meeting room of Department of Applied Mathematics II.
Higher Technical School of Engineering. University of Seville.
C/ Camino de los descubrimientos, no number. E-41092 Seville.

PROGRAM:

	Monday, January 29	Tuesday, January 30	Wednesday, January 31	Thursday, February 1	Friday, February 2
9:30-14:00	Open problems session	Working session	Working session	Working session	Working session / Report session
14:00-16:00	Lunch*
16::00-	Working session	Working session	Visit to Seville	Working session
20:00-				Workshop Dinner*

* Lunches and Workshop Dinner sponsored by CONNECT-RISE project H2020-MSCA-RISE-2016-734922; common fund managed by the Universitat Politècnica de Catalunya.

PARTICIPANTS:

Oswin Aichholzer (Graz University of Technology)
Luis Evaristo Caraballo de la Cruz (University of Seville)
José Miguel Díaz Báñez (University of Seville)
Ruy Fabila Moroy (Cinvestav, Mexico)
Nadine Kroher (University of Seville)
Irene Parada (Graz University of Technology)
Ramón Piedra de la Cuadra (University of Seville)
Inmaculada Ventura Molina (University of Seville)
Birgit Vogtenhuber (Graz University of Technology)

WORK PACKAGE 4

Graph-based algorithms for UAVs and for MIR

Algorithmics for unmanned aerial vehicles.

One of the main challenges in the development of multi-UAVs systems is path planning. For instance, for surveillance missions in applications in oceanography, forest fire prevention or oil spill monitoring, teams of autonomous robots can monitor changing environments. This requires robots to consistently traverse the environment in trajectories designed to optimize certain performance criteria, such as quality or frequency of sensor measurements. Many problems related to cooperative UAVs rely on geometric networks when a discretization of the problem is considered. We illustrate this with two concrete examples:
(1) Search problems in continuous regions can be discretized by sampling the region of interest at locations with high target probability, reducing the problem to optimization in a finite network.
(2) The synchronization problem for information exchange consists in, given the planned trajectories of a set of UAVs, scheduling each vehicle’s movement along its trajectories so that every pair of neighboring vehicles is guaranteed to be close enough at some point in time, allowing to synchronize information with all other vehicles. It can be showed that the system can be synchronized if and only if the communication graph is bipartite. It follows that the use of geometric networks to represent different scenarios like the ones above implies that many domain-specific questions can be naturally translated into geometric graph problems, and vice-versa.

Algorithmics for musical information retrieval.

Music information retrieval (MIR) focuses on tools that extract intrinsic properties from musical audio, related to pitch, rhythm, timbre, harmony, etc. Graphs in MIR have been used for indexing music collections, representation of pitch class sequences and other relevant music features or acoustic-based music recommender systems. Most advances in audio-based feature extraction and classification have focused on Western classical and popular music. However, they do not perform well in ethnic music, as this music does not always correspond to the Western concepts. For instance, the computational analysis of flamenco music poses a variety of challenging mathematical and algorithmic problems. The following three research lines will be particularly important in this project:
1) Graph clustering for melody categorization. Related studies have evaluated approaches to automatic melody categorization based on their performance in a supervised k-NN-classification task. However, when using an unsupervised approach we have to compute a similarity graph from a similarity matrix, and sophisticated clustering algorithms tailored to the characteristics of melodic similarity are needed.
2) Phylogenetic graphs. Phylogenetic tree algorithms are useful to map pairwise similarities computed in a dataset to a twodimensional space. Consequently, phylogenetic analysis is a powerful tool to gain deeper understanding of musicological concepts, such as the evolution of styles. Computing “ancestral” nodes in phylogenetic graphs in a challenging task both in bioinformatics and music. 3) Music information visualization and retrieval. For enhancing the user experience in automatic music recommendation and contentbased retrieval, data visualization plays an important role: graphical user interfaces allow content-based browsing and exploring of large collections in an intuitive manner. Consequently, the development of algorithms mapping high-dimensional relations between data instances to two- and three-dimensional spaces is an important open challenge.