provably good and practically useful

The lettering of maps is a classical problem of cartography that consists of placing names, symbols, or other data near to specified sites on a map. Certain design rules have to be obeyed. A practically interesting special case, the Map Labeling Problem, consists of placing axis parallel rectangular labels of common size so that one of its corners is the site, no two labels overlap, and the labels are of maximum size in order to have legible inscriptions. The problem is NP-hard; it is even NP-hard to approximate the solution with quality guaranty better than 50 percent. There is an approximation algorithm A with a quality guaranty of 50 percent and running time O (n log n). So A is the best possible algorithm from a theoretical point of view. This is even true for the running time, since there is a lower bound on the running time of any such approximation algorithm of (n log n). Unfortunately A is useless in practice as it typically produces results that are intolerably far off the maximum size. The main contribution of this paper is the presentation of a heuristical approach that has A's advantages while avoiding its disadvantages: 1\. It uses A's result in order to guaranty the same optimal running time efficiency; a method which is new as far as we know. 2\. Its practical results are close to the optimum. The practical quality is analysed by comparing our results to the exact optimum, where this is known; and to lower and upper bounds on the optimum otherwise. The sample data consists of three different classes of random problems and a selection of problems arising in the production of groundwater quality maps by the authorities of the City of München

Genetic algorithms for map labeling

Map labeling is the cartographic problem of placing the names of features (for example cities or rivers) on the map. A good labeling has no intersections between labels. Even basic versions of the problem are NP-hard. In addition, realistic map-labeling problems deal with many cartographic constraints, which pose more demands on how the labels should be placed in relation to their surroundings. For example, a label is preferably placed above and to the right of a city. These two aspects (combinatorially hard and the need of considering cartographic rules) make the problem challenging. Genetic algorithms (GAs) are heuristic solvers for optimization problems. Based on the theory of Darwinian evolution, they are able to "evolve" solutions using a process similar to adaptation in biology. In this thesis we apply GAs to solve map-labeling problems. Problems dealing with point features (like cities) and line features (like rivers) are discussed. It is also shown how additional cartographic rules can be incorporated in the algorithm. Experiments done on randomly-generated maps and real-world data show that the GAs are successful in finding good solutions. The GAs were designed with theoretical insights regarding linkage and mixing in mind. The map-labeling problem is interesting in that its linkage is geometrically determined and therefore reasonably clear. This property was exploited in the design of the GAs. The GAs were also used to verify the predictions of theoretical models from literature (a convergence model and a population-sizing model). The GA was able to match the assumptions of the models thanks to a novel operator, the so-called geometrically local optimizer. Experimental results indeed matched the predictions of the models. As a result, the number of fitness evaluations scales linearly with the input size (the size of the map)

Automatic tagging and geotagging in video collections and communities

Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

An Algorithmic Framework for Labeling Road Maps

Given an unlabeled road map, we consider, from an algorithmic perspective, the cartographic problem to place non-overlapping road labels embedded in their roads. We first decompose the road network into logically coherent road sections, e.g., parts of roads between two junctions. Based on this decomposition, we present and implement a new and versatile framework for placing labels in road maps such that the number of labeled road sections is maximized. In an experimental evaluation with road maps of 11 major cities we show that our proposed labeling algorithm is both fast in practice and that it reaches near-optimal solution quality, where optimal solutions are obtained by mixed-integer linear programming. In comparison to the standard OpenStreetMap renderer Mapnik, our algorithm labels 31% more road sections in average.Comment: extended version of a paper to appear at GIScience 201

Bigraphs with sharing

Bigraphical Reactive Systems (BRS) were designed by Milner as a universal formalism for modelling systems that evolve in time, locality, co-locality and connectivity. But the underlying model of location (the place graph) is a forest, which means there is no straightforward representation of locations that can overlap or intersect. This occurs in many domains, for example in wireless signalling, social interactions and audio communications. Here, we define bigraphs with sharing, which solves this problem by an extension of the basic formalism: we define the place graph as a directed acyclic graph, thus allowing a natural representation of overlapping or intersecting locations. We give a complete presentation of the theory of bigraphs with sharing, including a categorical semantics, algebraic properties, and several essential procedures for computation: bigraph with sharing matching, a SAT encoding of matching, and checking a fragment of the logic BiLog. We show that matching is an instance of the NP-complete sub-graph isomorphism problem and our approach based on a SAT encoding is also efficient for standard bigraphs. We give an overview of BigraphER (Bigraph Evaluator & Rewriting), an efficient implementation of bigraphs with sharing that provides manipulation, simulation and visualisation. The matching engine is based on the SAT encoding of the matching algorithm. Examples from the 802.11 CSMA/CA RTS/CTS protocol and a network management support system illustrate the applicability of the new theory

Mobile Video Object Detection with Temporally-Aware Feature Maps

This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices. Our approach combines fast single-image object detection with convolutional long short term memory (LSTM) layers to create an interweaved recurrent-convolutional architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that significantly reduces computational cost compared to regular LSTMs. Our network achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate feature maps across frames. This approach is substantially faster than existing detection methods in video, outperforming the fastest single-frame models in model size and computational cost while attaining accuracy comparable to much more expensive single-frame models on the Imagenet VID 2015 dataset. Our model reaches a real-time inference speed of up to 15 FPS on a mobile CPU.Comment: In CVPR 201

Clone Detection and Elimination for Haskell

Duplicated code is a well known problem in software maintenance and refactoring. Code clones tend to increase program size and several studies have shown that duplicated code makes maintenance and code understanding more complex and time consuming. This paper presents a new technique for the detection and removal of duplicated Haskell code. The system is implemented within the refactoring framework of the Haskell Refactorer (HaRe), and uses an Abstract Syntax Tree (AST) based approach. Detection of duplicate code is automatic, while elimination is semi-automatic, with the user managing the clone removal. After presenting the system, an example is given to show how it works in practice