The power of a GIS comes from the ability to relate different information in a spatial context and to reach a conclusion about this relationship. Most of the information we have about our world contains a location reference, placing that information at some point on the globe. When rainfall information is collected, it is important to know where the rainfall is located. This is done by using a location reference system, such as longitude and latitude, and perhaps elevation. Comparing the rainfall information with other information, such as the location of marshes across the landscape, may show that certain marshes receive little rainfall. This fact may indicate that these marshes are likely to dry up, and this inference can help us make the most appropriate decisions about how humans should interact with the marsh. A GIS, therefore, can reveal important new information that leads to better decisionmaking. Geographic Information Systems (GIS) Poster http://egsc.usgs.gov/isb/pubs/gis_poster/

ZusammenfassungAndere als die standardmäßig in Pascal vorgesehenen Datentypen mit Namen integer, real, boolean und char können durch Angabe von Typdefinitionen (type definition part) im Deklarationsteil von Programmen vereinbart werden.

Im vorangegangenen Kapitel ist deutlich geworden, daß zur vollständigen Formalisierung von Algorithmen, etwa in der Programmiersprache Pascal, eine genaue Definition der Regeln der Sprache (Syntax), also der zulässigen, korrekten Pascal-Programme, erforderlich ist. Die rein verbale Erklärung solcher Regeln ist äußerst umständlich und häufig nicht hinreichend präzise. Wir werden daher in möglichst vielen Fällen neben der verbalen eine formale Beschreibung der Syntax von Pascal angeben. Als Darstellungsform hierfür wählen wir die sogenannten Syntaxdiagramme, die wir im folgenden vorstellen und erläutern werden.

We propose a novel approach for optimization under uncertainty. Our approach does not assume any particular noise model behind the measurements, and only requires two typical instances. We first propose a measure of similarity of instances (with respect to a given objective). Based on this measure, we then choose a solution randomly among all solutions that are near-optimum for both instances. The exact notion of near-optimum is intertwined with the proposed similarity measure. Our similarity measure also allows us to derive formal statements about the expected quality of the computed solution. Furthermore, we apply our approach to various optimization problems.

We introduce sequence hypergraphs by extending the concept of a directed edge (from simple directed graphs) to hypergraphs. Specifically, every hyperedge of a sequence hypergraph is defined as a sequence of vertices (not unlike a directed path). Sequence hypergraphs are motivated by problems in public transportation networks, as they conveniently represent transportation lines. We study the complexity of several fundamental algorithmic problems, arising (not only) in transportation, in the setting of sequence hypergraphs. In particular, we consider the problem of finding a shortest st-hyperpath: a minimum set of hyperedges that "connects" (allows to travel to) t from s; finding a minimum st-hypercut: a minimum set of hyperedges whose removal "disconnects" t from s; or finding a maximum st-hyperflow: a maximum number of hyperedge-disjoint st-hyperpaths. We show that many of these problems are APX-hard, even in acyclic sequence hypergraphs or with hyperedges of constant length. However, if all the hyperedges are of length at most 2, we show that these problems become polynomially solvable. We also study the special setting in which for every hyperedge there also is a hyperedge with the same sequence, but in reverse order. Finally, we briefly discuss other algorithmic problems such as finding a minimum spanning tree, or connected components.

We introduce sequence hypergraphs by extending the concept of a directed edge (from simple directed graphs) to hypergraphs. Specifically, every hyperedge of a sequence hypergraph is defined as a sequence of vertices (imagine it as a directed path). Note that this differs substantially from the standard definition of directed hypergraphs. Sequence hypergraphs are motivated by problems in public transportation networks, as they conveniently represent transportation lines. We study the complexity of some classic algorithmic problems, arising (not only) in transportation, in the setting of sequence hypergraphs. In particular, we consider the problem of finding a shortest st-hyperpath: a minimum set of hyperedges that "connects" (allows to travel to) t from s; finding a minimum st-hypercut: a minimum set of hyperedges whose removal "disconnects" t from s; or finding a maximum st-hyperflow: a maximum number of hyperedge-disjoint st-hyperpaths.We show that many of these problems are APX-hard, even in acyclic sequence hypergraphs or with hyperedges of constant length. However, if all the hyperedges are of length at most 2, we show, these problems become polynomially solvable. We also study the special setting in which for every hyperedge there also is a hyperedge with the same sequence, but in the reverse order. Finally, we briefly discuss other algorithmic problems (e.g., finding a minimum spanning tree, or connected components).

Schummer [27] introduced the concept of bribeproof mechanism which, in a context where monetary transfer between agents is possible, requires that manipulations through bribes are ruled out. Unfortunately, in many domains, the only bribeproof mechanisms are the trivial ones which return a fixed outcome. This work presents one of the few constructions of non-trivial bribeproof mechanisms for this setting. Though the suggested construction applies to rather restricted domains, the results obtained are tight: for several natural problems, the method yields the only possible bribeproof mechanism and no such mechanism is possible on more general domains.

Given a peptide as a string of amino acids, the masses of all its prefixes and suffixes can be found by a trivial linear scan through the amino acid masses. The inverse problem is the ideal de novo peptide sequencing problem: Given all prefix and suffix masses, determine the string of amino acids. In biological reality, the given masses are measured in a lab experiment, and measurements by necessity are noisy. The (real, noisy) de novo peptide sequencing problem therefore has a noisy input: a few of the prefix and suffix masses of the peptide are missing and a few others are given in addition. For this setting we ask for an amino acid string that explains the given masses as accurately as possible. Past approaches interpreted accuracy by searching for a string that explains as many masses as possible. We feel, however, that it is not only bad to not explain a mass that appears, but also to explain a mass that does not appear. That is, we propose to minimize the symmetric difference between the set of given masses and the set of masses that the string explains. For this new optimization problem, we propose an efficient algorithm that computes both the best and the k best solutions. Experiments on measurements of 342 synthesized peptides show that our approach leads to better results compared to finding a string that explains as many given masses as possible.

We consider the exploration of a simple polygon P by a robot that moves from vertex to vertex along edges of the visibility graph of P . The visibility graph has a vertex for every vertex of P and an edge between two vertices if they see each other—that is, if the line segment connecting them lies inside P entirely. While located at a vertex, the robot is capable of ordering the vertices it sees in counterclockwise order as they appear on the boundary, and for every two such vertices, it can distinguish whether the angle between them is convex (⩽ π) or reflex ( > π). Other than that, distant vertices are indistinguishable to the robot. We assume that an upper bound on the number of vertices is known. We obtain the general result that a robot exploring any locally oriented, arc-labeled graph G can always determine the base graph of G . Roughly speaking, this is the smallest graph that cannot be distinguished by a robot from G by its observations alone, no matter how it moves. Combining this result with various other techniques allows the ability to show that a robot exploring a polygon P with the preceding capabilities is always capable of reconstructing the visibility graph of P . We also show that multiple identical, indistinguishable, and deterministic robots of this kind can always solve the weak rendezvous problem in which they need to position themselves such that they mutually see each other—for instance, such that they form a clique in the visibility graph.

We study variants of the vertex disjoint paths problem in plane graphs where paths have to be selected from given sets of paths. We investigate the problem as a decision, maximization, and routing-in-rounds problem. Although all considered variants are NP-hard in planar graphs, restrictions on the locations of the terminals on the outer face of the given planar embedding of the graph lead to polynomially solvable cases for the decision and maximization versions of the problem. For the routing-in-rounds problem, we obtain a p -approximation algorithm, where p is the maximum number of alternative paths for a terminal pair, when restricting the locations of the terminals to the outer face such that they appear in a counterclockwise traversal of the boundary as a sequence for some permutation . © 2015 Wiley Periodicals, Inc.NETWORKS, Vol. 66(2), 136–144 2015

Given an urban public transportation network and historic delay information, we consider the problem of computing reliable journeys. We propose new algorithms based on our recently presented solution concept (Bohmova et al., ATMOS 2013), and perform an experimental evaluation using real-world delay data from Zurich, Switzerland. We compare these methods to natural approaches as well as to our recently proposed method which can also be used to measure typicality of past observations. Moreover, we demonstrate how this measure relates to the predictive quality of the individual methods. In particular, if the past observations are typical, then the learning- based methods are able to produce solutions that perform well on typical days, even in the presence of large delays.

In a faulty environment, comparisons between two elements with respect to an underlying linear order can come out right or go wrong. A wrong comparison is a recurring comparison fault if comparing the same two elements yields the very same result each time we compare the elements. We examine the impact of such faults on the elementary problems of sorting a set of distinct elements and finding a minimum element in such a set. The more faults occur, the worse the approaches to solve these problems can become and we parametrize our analysis by an upper bound k on the number of faults. We first explain that reconstructing the sorted order of the elements is impossible in the presence of even one fault. Then, we focus on the maximum information content we get by performing all possible comparisons. We consider two natural approaches for sorting the elements that involve knowledge of the outcomes of all comparisons: the first approach finds a permutation (compatible solution) that contradicts at most k times the outcomes of comparisons, and the second approach sorts the elements by the number of times an element is returned to be larger in the outcomes of its comparisons with all other elements (score solution). In such permutations the elements can be dislocated from their positions in the linear order. We measure the quality of such permutations by three measures: the maximum dislocation of an element, the sum of dislocations of all elements, and the Kemeny distance compared to the linear order. We show for compatible solutions that the Kemeny distance is at most 2k, the sum of dislocations at most 4k, and the maximum dislocation at most 2k. In score solutions the Kemeny distance is smaller than 4k, the sum of dislocations smaller than 8k, and the maximum dislocation at most k+1 . Our upper bounds are tight for compatible solutions, but possibly not tight for score solutions. It turns out that none of the two approaches is better than the other in all measures. For the problem of finding a minimum element, we first observe that there is no deterministic algorithm that guarantees to return one of the smallest k+ 1 elements. This implies that computing the first element of a score solution is optimum and we derive an algorithm that guarantees to find one of the k+ 2 smallest elements in time O(√(k)n) making O(√(k)n) comparisons, where n is the number of elements, and we generalize this algorithm to find all elements of score at most a given target t.

When I was younger and spent most of my time playing in the field of (more) theoretical computer science, I used to think of data mining as an uninteresting kind of game: I thought that area was a wild jungle of ad hoc techniques with no flesh to seek my teeth into. The truth is, I immediately become kind-of skeptical when I see a lot of money flying around: my communist nature pops out and I start seeing flaws everywhere. I was an idealist, back then, which is good. But in that specific case, I was simply wrong. You may say that I am trying to convince myself just because my soul has been sold already (and they didn’t even give me the thirty pieces of silver they promised, btw). Nonetheless, I will try to offer you evidences that there are some gems, out there in the data miner’s cave, that you yourself may appreciate. Who knows? Maybe you will decide to sell your soul to the devil too, after all. 1 Welcome to the Dungeon Data mining is the activity of drawing out patterns and trends from data; this evocative expression started being used in the 1990s, but the idea itself is much older and does not necessarily involve computers. As suggested by many, one early example of successful data mining is related to the 1854 outbreak of cholera in London. At that time it was widely (and wrongly) believed that cholera was a “miasmal disease” that was transmitted by some sort of lethal vapor; the actual cause of the disease, a bacterium usually found in poisoned waters, would have been discovered later by Filippo Pacini and Robert Koch1. John Snow was a private physician working in London who was deeply convinced that the killing agent entered the body via ingestion, due to contaminated food or water. In late August 1854, when the outbreak started in Soho, one of the poorest neighborhoods of the city, Snow began his investigation to obtain evidences of what was the real cause behind the disease. Through an accurate and deep investigation that put together ideas from different disciplines, and by means of an extensive analysis of the factual data 1 Filippo Pacini in fact published his results right in 1854, but his discoveries were largely ignored until thirty years later, when Robert Koch independently published his works on the Vibrio cholerae (now officially called Vibrio cholerae Pacini 1854 ). A. Ferro, F. Luccio, and P. Widmayer (Eds.): FUN 2014, LNCS 8496, pp. 1–15, 2014. c © Springer International Publishing Switzerland 2014