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Global Inference and Learning Algorithms for MultiLingual Dependency Parsing
, 2008
"... This paper gives an overview of the work of McDonald et al. (McDonald et al. 2005a, 2005b; McDonald and Pereira 2006; McDonald et al. 2006) on global inference and learning algorithms for datadriven dependency parsing. Further details can be found in the thesis of McDonald (McDonald 2006). This pap ..."
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This paper gives an overview of the work of McDonald et al. (McDonald et al. 2005a, 2005b; McDonald and Pereira 2006; McDonald et al. 2006) on global inference and learning algorithms for datadriven dependency parsing. Further details can be found in the thesis of McDonald (McDonald 2006). This paper is primarily intended for the audience of the ESSLLI 2007 course on datadriven dependency parsing.
Finding Precursor Compounds in Secondary Metabolism
 Genome Informatics
, 1999
"... A precursor is a compound which is transformed to a class of functional molecules within short steps. It is an important process in the production of natural drugs to decide whether a given compound is a precursor or not. We present two strategies to select precursor compounds in the secondary me ..."
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A precursor is a compound which is transformed to a class of functional molecules within short steps. It is an important process in the production of natural drugs to decide whether a given compound is a precursor or not. We present two strategies to select precursor compounds in the secondary metabolism of terpenoids: one is to find the packing of basic molecules in the given cyclic structure, and the other is to find the synthetic map of the given set of compounds. Both strategies play important roles in reproducing tracer experiments on a computer. 1 Introduction Finding the biosynthetic pathway of a hormone or a natural drug is the key issue for its industrial production. Even a limited increase in its production rate may lead to a drastic change in the synthetic scheme at a commercial level, because of its very low yield from raw materials. For example, the average yield of paclitaxel (a recently approved anticancer drug) from the yew bark is in the range of 0.0140.017%. ...
Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence Bargaining for Revenue Shares on Tree Trading Networks
"... We study trade networks with a tree structure, where a seller with a single indivisible good is connected to buyers, each with some value for the good, via a unique path of intermediaries. Agents in the tree make multiplicative revenue share offers to their parent nodes, who choose the best offer an ..."
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We study trade networks with a tree structure, where a seller with a single indivisible good is connected to buyers, each with some value for the good, via a unique path of intermediaries. Agents in the tree make multiplicative revenue share offers to their parent nodes, who choose the best offer and offer part of it to their parent, and so on; the winning path is determined by who finally makes the highest offer to the seller. In this paper, we investigate how these revenue shares might be set via a natural bargaining process between agents on the tree, specifically, egalitarian bargaining between endpoints of each edge in the tree. We investigate the fixed point of this system of bargaining equations and prove various desirable for this solution concept, including (i) existence, (ii) uniqueness, (iii) efficiency, (iv) membership in the core, (v) strict monotonicity, (vi) polynomialtime computability to any given accuracy. Finally, we present numerical evidence that asynchronous dynamics with randomly ordered updates always converges to the fixed point, indicating that the fixed point shares might arise from decentralized bargaining amongst agents on the trade network. 1
Several geometric data structures for objects in the plane can be constructed using persistent binary search trees. (I know, I promised I wouldn't do much geometry, but this was sort of irresistible.)
"... ach insertion or deletion crates a new version of T y ; we attach each version to the corresponding leaf in T x . The overall preprocessing time is O(n log n), and the overall space is O(n). To answer a query for point (a; b), we search for a in T x to nd the correct version of T y , and then sear ..."
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ach insertion or deletion crates a new version of T y ; we attach each version to the corresponding leaf in T x . The overall preprocessing time is O(n log n), and the overall space is O(n). To answer a query for point (a; b), we search for a in T x to nd the correct version of T y , and then search that version of T y for the successor of b. The query time in each tree is O(log n), so the overall query time is also O(log n). (b) Modify the previous data structure to store a set of n disjoint nonhorizontal segments, with the same preprocessing, space, and query bounds. [Hint: Use a persistent kinetic binary search tree.] Solution: A fulledged kinetic data structure is overkill here. The only necessary change from part (a) is that we store the line equations of segments in T y . Whenever we search T y , either to locate a point (a; b) or to insert a new segment with left endpoint (a; b), we replace every direct comparison with b with a test whether (a; b) is above or below a lin
Graph Constraints in Constraint Programming: Weighted Spanning Trees
"... Abstract. The recently introduced CP(Graph) framework consists of a constraint solver that allows the programmer to declare variables whose values are graphs and to specify properties that these graphs should have in the form of constraints. The usefulness of graph variables depends on the existence ..."
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Abstract. The recently introduced CP(Graph) framework consists of a constraint solver that allows the programmer to declare variables whose values are graphs and to specify properties that these graphs should have in the form of constraints. The usefulness of graph variables depends on the existence of efficient algorithms to handle the constraints imposed on them. The most important task is filtering, i.e., shrinking the variable domains in order to reduce the search space. In this paper we describe a filtering algorithm for the weightbounded spanningtree constraint WBST (G, T, I, W), which specifies that T is a spanning tree of G whose total weight is at most I, where W is a vector of the edge weights. More generally, we wish to introduce the task of filtering for graph constraints to the algorithms community, and to demonstrate the type of algorithmic challenges that it entails. In a nutshell, the algorithm needs to work with sets of graphs rather than individual graphs and instead of searching for a single solution to the problem at hand, it needs to compute the union and the intersection of all such solutions. 1
Speeding Up Enumeration Algorithms with Amortized Analysis
"... In this paper, we characterize a class of fast enumeration algorithms and propose a method for constructing ..."
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In this paper, we characterize a class of fast enumeration algorithms and propose a method for constructing
SOME COMPUTATIONAL IMPROVEMENTS ON FINDING THE K SHORTEST SPANNING TREES
, 2008
"... A key computational step in the K shortest spanning tree problem is the procedure to obtain the second best. In this paper we describe and implement several methods for the determination of the second shortest tree on a network. The methods use different strategies for reaching the best swap of ed ..."
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A key computational step in the K shortest spanning tree problem is the procedure to obtain the second best. In this paper we describe and implement several methods for the determination of the second shortest tree on a network. The methods use different strategies for reaching the best swap of edges that leads to the second best spanning tree. Several fathoming conditions are considered in order to prevent useless calculations. Computational experiments are presented and results are analysed for randomly generated networks.
Spanning Trees
, 2013
"... Given a MST, an immediate problem is how to get a new MST if a new vertex or certain new edges are added into the graph, or how to get a new MST if an existing vertex or certain existing edges are deleted from ..."
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Given a MST, an immediate problem is how to get a new MST if a new vertex or certain new edges are added into the graph, or how to get a new MST if an existing vertex or certain existing edges are deleted from