On Computing Longest Paths in Small Graph Classes The longest path problem is to find a longest path in a given graph. While the graph classes in which the Hamiltonian path problem can be solved efficiently are widely investigated, few graph classes are known to be solved efficiently for the longest path problem. For a tree, a simple linear time algorithm for the longest path problem is known. We first generalize the algorithm, and show that the longest path problem can be solved efficiently for weighted trees, block graphs, and cacti. We next show that the longest path problem can be solved efficiently on some graph classes that have natural interval representations. The Hamiltonian path problem is one of the most well known N P-hard problem, and there are numerous applications of the problems [17] . For such an intractable problem, there are two major approaches; approximation algorithms [20, 2, 35] and algorithms with parameterized complexity analyses [15] . In both approaches, we have to change the decision problem to the optimization problem. Therefore the longest path problem is one of the basic problems from the viewpoint of combinatorial optimization. From the practical point of view, it is also very natural approach to try to find a longest path in a given graph, even if it does not have a Hamiltonian path. However, finding a longest path seems to be more difficult than determining whether the given graph has a Hamiltonian path or not. Even if a graph has a Hamiltonian path, it is impossible to find paths of length n − n for any > 0 unless P = N P [21] . In general, the longest path problem does not belong to APX unless P = N P [21] , and the best known performance ratio of an approximation algorithm is O(n(log log n/ log n). 2 ) [7] (see also [27, 1, 32, 36] for related results).. Recently, many new graph classes are proposed, and the complexity of hard problems on those graph classes are investigated [9, 18] . The classification of the graph classes by the difficulty in solving the Hamiltonian path problem gives us an insight for the longest path problem. If the Hamiltonian path problem is N P-hard, the longest path problem is also intractable since the Hamiltonian path problem is the special case of the longest path problem. Such "hard" graph classes include chordal bipartite graphs, strongly chordal split graphs (and hence chordal graphs and split graphs) [28] , undirected path graphs, double interval graphs, rectangle graphs [6] , and circle graphs [13] . On the other hand, proper interval graphs are a "trivial" graph class in the sense that all connected proper interval graphs have a Hamiltonian path [5] . Thus we can find a longest path of length equal to one less than the number of vertices for any given connected proper interval graph. Between them, there are "non-trivial" graph classes; the Hamiltonian path problem is polynomial time solvable for circular arc graphs (and hence interval graphs) [14] , and bipartite permutation graphs [33] . Moreover, such "non-trivial" graph classes include infinite number of graphs that have no Hamiltonian paths (e.g., trees). In this paper, we focus on the longest path problem for the "non-trivial" graph classes, and propose efficient algorithms for several graph classes.. There are few polynomial time algorithms for finding a longest path in a graph; as far as the authors know, trees are the only graph class which is natural and non-trivial such that the longest path problem can be solved in polynomial time. The algorithm for trees was invented by W. Dijkstra around 1960. It runs in linear time, and the formal proof is given by R.W. Bulterman et al [11] .. We first generalize the Dijkstra's algorithm and its correctness proof in [11] , and show that the longest path problem can be solved efficiently for (vertex/edge) weighted trees, block graphs, and cacti.. Next, we focus on the graph classes which have natural interval representations. We consider some graph classes between "non-trivial" and "trivial" graph classes and show that the longest path problem can be solved efficiently on these classes.. The class of interval bigraphs has a natural bipartite analogy with the class of interval graphs. In the class, there are some subclasses; chain graphs ⊂ bipartite permutation graphs ⊂ biconvex graphs ⊂ convex graphs ⊂ interval bigraphs. We will show some relationship among these graph classes (though some of them seem to be folklore), and show a linear time algorithm for finding a longest path in a bipartite permutation graph. (For these graph classes, see, e.g., [37, 23] for chain graphs, [33, 10, 25] for bipartite permutation graphs, [9] for (bi)convex graphs, and [29, 19] for interval bigraphs.). We then turn to the interval graphs and their subclasses. Although all longest paths of a connected interval graph have non-empty intersection [3, Corollary 2.2], there are no efficient algorithms for finding a specific longest path in an interval graph. Between the class of interval graphs and the class of proper interval graphs, we introduce a new graph class named "interval biconvex graphs." A interval biconvex graph G is an interval graph that corresponds to a biconvex graph G such that G and G have the same interval representations. That is, the class of interval biconvex graphs is the class that the class of biconvex graphs is a natural bipartite analogy. We also show the inclusion among graph classes (proper interval graphs ∪ threshold graphs) ⊂ interval biconvex graphs ⊂ interval graphs. That is, the class of interval biconvex graphs is a generalization of both of proper interval graphs and threshold graphs. Finally, we give an algorithm that finds a longest path for any interval biconvex graph in O(n 3 (m + n log n)) time. The main open problems are the complexity of the longest path problem for interval graphs, convex graphs, and biconvex graphs.. The longest path problem for interval graphs can be reduced to the longest path problem for convex graphs in polynomial time by using a simple reduction by Damaschke in [28, Section 2] (see Appendix A). Thus the problem for convex graphs seems to be more difficult than interval graphs. Intuitively, however, a convex graph has similar structure to the interval graph that has the MPQ-tree with only one Q-node in the manner of [24] . Therefore, the complexity of the longest path problem for interval graphs and convex graphs seems to be essentially the same. The longest path problems for biconvex graphs seems to be easier than the other two graph classes.. It is also worth investigating for the complexity of the longest cycle problem instead of the longest path problem. It is known that a longest cycle can be found in threshold graphs in polynomial time [26] . The efficient algorithms for the Hamiltonian cycle problem for proper interval graphs [5, 30] and interval graphs [22] have been given. There are a few results for the Hamiltonian cycle problem on split graphs [12, 31] . Dynamic programming technique seems to be effective for some graph classes that have tree decomposition (e.g., partial k-trees).

O O O O O O B I O B I I O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O B I I O O O O O O O O O O B I I O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O B I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O B I I O O O O O O O O O B I I O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I I O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O B I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O B I I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O


Exploiting sequential dependencies for expert finding We propose an expert finding method based on assumption of sequential dependence between a candidate expert and the query terms in the scope of a document. We assume that the strength of relation of a candidate to the document's content depends on its position in this document with respect to the positions of the query terms. The experiments on the official Enterprise TREC data demonstrate the advantage of our method over the method based on independence of query terms and persons in a document. The search for people with relevant expertise rather than for relevant documents is becoming more and more of a necessary everyday activity in organizations. Recently, the expert finding task attracted the close attention of IR research community and stays a part of the Enterprise track of the Text REtrieval Conference (TREC) [2] already since 2005. The majority of approaches proposed so far is based on inferring personal expertness by analyzing the degree of cooccurrence of personal identifiers with the query terms in top retrieved organizational documents. The state-of-theart methods relying on this principle in fact consider that the candidate's expertness can be measured by the sum of retrieval scores of related documents [1, 5] . In this paper we propose to take not only the fact of co-occurrence into account, but also the important property of this co-occurrence: the sequential order of a candidate's identifier and the query terms mentioned in a document. We presented an expert finding method taking the sequential dependence of a person and query terms occurring in a document into account. We claimed that it is useful to differentiate the orders of their occurrence in a document to estimate the strength of the relation of a candidate expert to the document's content. Experiments on the dataset from the Enterprise TREC 2007 justified our assumptions.. In the future we plan to develop a unified model combining all features describing co-occurrence into account: the proximity of a candidate to query terms, the order of their occurrence, the frequency of a candidate's occurrence etc. It is also promising to make the document-candidate weights dependent not only on the positions of candidate experts with respect to query terms positions in a document, but also on their absolute positions in the document or on the type of the document (at least, on its easily determined properties like extension or length). We experiment with the CSIRO collection used by the Enterprise TREC community in 2007. It represents a crawl from Australia's national science agency's (CSIRO) web site. 50 queries with judgments made by retired CSIRO employees and 3500 candidates found in the collection were used for the evaluation. At the collection preparation stage, we extracted associations between candidate experts and documents by searching for the candidates email addresses and full names in the text of documents. It was enough to retrieve 50 documents containing at least one candidate's mention for the best performance of the baseline method.. Two expert finding methods are evaluated: the baseline method based on the assumption of independence between query terms and the candidate's mention (see Formula 1) and our method using the assumption of their sequential dependence (see Formula 3). For the baseline method the association score between the document and any candidate mentioned is always equal to 1.0. Since our method has only 3 parameters, we calculated their optimal setting with a simple coordinate-level hill climbing search method. The best performance of our method was reached with the following values for association scores: Table 1 : The performance of expert finding methods using two assumptions: independence and sequential dependence of a candidate and query terms This result suggests that the most important persons in the most relevant documents are always mentioned before topical words (for instance, it often happens in resumes that are very important evidence of expertness in CSIRO). Of course, this may vary between organizations and should actually depend on what kinds of documents prevail in the organization.. Both methods are compared in terms of common Information Retrieval performance measures officially used in TREC evaluations: Mean Average Precision (MAP), precision at top 5 ranked candidate experts (P@5) and Mean Reciprocal Rank (MRR). Table 1 demonstrates the advantage of our method based on the assumption of sequential dependence over the baseline method assuming sequential independence.

O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Test set embedding for deterministic BIST using a reconfigurable interconnection network Abstract-We present a new approach for deterministic built-in self-test (BIST) in which a reconfigurable interconnection network (RIN) is placed between the outputs of a pseudorandom pattern generator and the scan inputs of the circuit under test (CUT). The RIN, which consists only of multiplexer switches, replaces the phase shifter that is typically used in pseudorandom BIST to reduce correlation between the test data bits that are fed into the scan chains. The connections between the linear-feedback shift-register (LFSR) and the scan chains can be dynamically changed (reconfigured) during a test session. In this way, the RIN is used to match the LFSR outputs to the test cubes in a deterministic test set. The control data bits used for reconfiguration ensure that all the deterministic test cubes are embedded in the test patterns applied to the CUT. The proposed approach requires very little hardware overhead, only a modest amount of CPU time, and fewer control bits compared to the storage required for reseeding techniques or for hybrid BIST. Moreover, as a nonintrusive BIST solution, it does not require any circuit redesign and has minimal impact on circuit performance.

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Power-aware SoC test planning for effective utilization of port-scalable testers Many system-on-chip (SoC) integrated circuits contain embedded cores with different scan frequencies. To better meet the test requirements for such heterogeneous SoCs, leading tester companies have recently introduced port-scalable testers, which can simultaneously drive groups of channels at different data rates. However, the number of tester channels available for scan testing is limited; therefore, a higher shift frequency can increase the test time for a core if the resulting test access architecture reduces the bit-width used to access it. We present a scalable test planning technique that exploits port scalability of testers to reduce SoC test time. We compare the proposed heuristic optimization method to two baseline methods based on prior works that use a single scan data rate for all embedded cores. We also propose a power-aware test planning technique to effectively utilize port-scalable testers under constraints of test power consumption. Experimental results are presented for power-aware test scheduling to illustrate the impact of power constraints on overall test time. Recent advances in technology have led to a tremendous increase in the complexity of system-on-chip (SoC) integrated circuits. Today's heterogeneous SoCs consist of embedded cores that not only operate in multiple clock domains [Goel et al. 2004; Lin and Thompson 2003; Schmid and Knablein 1999] , but also (due to differences in performance levels, design styles, and scan insertion methods) differ in their maximum scan clock frequencies [Vranken et al. 2003 ]. The difference in scan clock frequencies between embedded cores can also arise due to the integration of various cores derived from different, older-generation SoCs into a single, current-generation SoC [Chickermane et al. 2001] . The test time for such SoCs can be reduced by testing the embedded cores at data-transfer rates that match their maximum scan frequencies.. To test embedded cores at different scan frequencies, the test data needs to be simultaneously transported at multiple data rates on the tester channels. In order to meet this requirement, automatic test equipment (ATE) vendors have introduced a new class of testers that can simultaneously drive tester channels at different data rates [Dorsch et al. 2002] . Examples of such ATEs include the Agilent 93000 series [Agilent Technologies 2002; Agilent 93000 2008] and the Tiger system from Teradyne [Teradyne Technologies 2008] , which are based on port scalability and test processor-per-pin architecture. Port scalability allows every port of a tester to be configured at a desired data rate, where each port typically consists of multiple channels.. Modular testing of embedded cores offers a promising test solution for SoCs [Goel et al. 2004; Zorian et al. 1999] . It also lends itself well to the scenario of SoC testing using multiple scan data rates. It involves the isolation of an embedded core from surrounding logic using a test wrapper, and the design of a test access mechanism (TAM) to deliver test data from the I/O pins of the SoC. In many test access architectures, the SoC-level TAM wires are divided into fixedwidth TAM partitions [Goel and Marinissen 2002; Huang et al. 2002; Iyengar et al. 2003b; Larsson and Peng 2002] . In the scenario being considered here, all wires of a TAM partition belong to the same ATE port, which can be configured for a predefined scan data rate. TAM optimization methods published in the literature do not handle the general problem of designing TAM architectures that are driven by port-scalable ATEs.. The problem of designing a TAM architecture to minimize the SoC test time has been shown in the literature to be N P -hard . Therefore, efficient heuristic techniques have been developed for SoC test planning and TAM optimization [Goel and Marinissen 2002; Gonciari et al. 2003; Huang et al. 2002; Iyengar et al. 2003b; Larsson and Peng 2002; Zhao and Upadhyaya 2003; Yoneda et al. 2007; Yu et al. 2007; Xu and Nicolici 2005] . However, it is assumed in all these methods that at any instant in time, the ATE provides test stimuli to the SoC at a single data rate. As a result, existing optimization techniques cannot readily exploit the availability of simultaneous multiple data-transfer rates from the ATE to the SoC. In this work, we focus on the problem of designing an optimized TAM architecture that can benefit from the availability of port scalability in ATEs. As in Goel and Marinissen [2002] , Iyengar et al. The testing of embedded cores with multiple scan data rates was recently addressed in Nicolici [2004a, 2004b] , Sehgal et al. [2004] , and Sehgal and Chakrabarty [2007] . The key idea in Xu and Nicolici [2004a] is to use bandwidth matching and to determine appropriate scan frequencies for the TAM partitions to reduce test time; however, no limits are set on the maximum scan frequencies of the cores. In Sehgal and Chakrabarty [2007] , the number of data rates for the available ATE channels is set to two. The preceding assumptions are too restrictive in practice. In this article, we consider a more general scenario in which cores with different scan frequencies can be driven by ATE channels operating in a data-rate range given by the range of scan frequencies for the cores.. We optimize the TAM architecture for a set of cores with different maximum scan data rates. We compare this approach to a baseline case in which all cores are tested at their maximum scan frequencies. We use an iterative descent procedure that minimizes the testing time by jointly optimizing the widths of TAM partitions, TAM frequencies, and assignment of cores to TAM partitions. We also present a solution to this problem based on integer linear programming (ILP). Although ILP yields optimal results, it is computationally infeasible for large problem instances. Nevertheless, the ILP model can be used to evaluate the heuristic for small problem instances. We derive a lower bound on the SoC testing time and list these bounds for several ITC'02 SoC test benchmarks . We also present experimental results for several ITC'02 benchmark SoCs.. While the testing of multiple cores in parallel in a core-based SoC results in test schedules with low test times, the concurrent testing of these cores results in increased power consumption during test application. The permissible power envelope is often exceeded when power constraints are not considered during test scheduling [Chou et al. 1997] ; this can lead to thermal runaway, or cause severe irreparable damage to the SoC. Testing an SoC can lead to extremely high switching activity, more than when the circuit is in its functional mode [Larsson and Peng 2001; Zhao and Upadhyaya 2003] . It is therefore important to consider power constraints while designing test schedules for embedded cores with multiple scan data rates. We therefore formulate a TAM design and test scheduling problem for cores with different scan frequencies, and we extend the problem formulation to include constraints that are placed on the test power.. The rest of this article is organized as follows. In Section 2, we define the test planning problem that exploits the availability of port-scalable ATEs. We develop an integer linear programming model for this problem and derive a lower bound on the SoC testing time. In Section 3, we present a scalable heuristic approach to solve this problem. Experimental results for several ITC'02 benchmark SoCs are also presented. Section 4 describes a power-aware heuristic scheduling method to solve the test planning problem. Experimental results for the ITC'02 SoC test benchmarks are presented to illustrate the impact of 53:4. • A. Sehgal et al. power constraints on the test planning problem. Finally, we present conclusions and directions for future work in Section 5.

O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Unified computational methods for regression analysis of zero-inflated and bound-inflated data Bounded data with excess observations at the boundary are common in many areas of application. Various individual cases of inflated mixture models have been studied in the literature for bound-inflated data, yet the computational methods have been developed separately for each type of model. In this article we use a common framework for computing these models, and expand the range of models for both discrete and semicontinuous data with point inflation at the lower boundary. The quasi-Newton and EM algorithms are adapted and compared for estimation of model parameters. The numerical Hessian and generalized Louis method are investigated as means for computing standard errors after optimization. Correlated data are included in this framework via generalized estimating equations. The estimation of parameters and effectiveness of standard errors are demonstrated through simulation and in the analysis of data from an ultrasound bioeffect study. The unified approach enables reliable computation for a wide class of inflated mixture models and comparison of competing models.Published by Elsevier B.V. Bound-inflated data are prevalent in a wide variety of disciplines, such as health and safety studies, economics, finance and insurance risk analysis. Typically the responses are bounded below by zero with a significant mass of zero observations, resulting in data that are either discrete with too many zeros for a standard discrete distribution, or semi-continuous with positive continuous values combined with a substantial portion of zeros. In our collaborative research on ultrasound safety (O'Brien et al., 2006) , groups of laboratory rabbits were exposed to focused ultrasound in both lungs to investigate the risk of ultrasound-induced hemorrhage. Due to the designed low to moderate acoustic pressure levels, about 80% of the observations were free of lesions while 20% exhibited lesions. The goal was to evaluate the effect of acoustic pressure and other factors based on the clustered, zero-inflated lesion size data to develop insight into the safe pressure levels for diagnostic clinical ultrasound.. A two-part model handles zeros and positive values, discrete or continuous, separately through two model components: a binary model for the occurrence of an event, and a zero-truncated Poisson or a log-normal model for the strictly positive size of the event conditional on its occurrence (Welsh et al., 1996; Zhou and Tu, 1999) . For correlated counts with extra zeros, the zero-truncated Poisson and negative binomial models were extended by adding random effects to each model component (Yau and Lee, 2001; Min and Agresti, 2005) . Dobbie and Welsh (2001) constructed generalized estimating equations (GEEs) with working correlation matrices for both components of the zero-truncated Poisson model. For semicontinuous longitudinal or clustered data, two-part random effects models were considered by Olsen and Schafer (2001) and Tooze et al. (2002) . Albert and Shen (2005) proposed a two-part latent process model, which was recently adapted to incorporate random effects as well with Bayesian inference (Ghosh and Albert, 2009 ). For Bayesian two-part models with random effects, see also Zhang et al. (2006) .. A zero-inflated (ZI) latent mixture model adds the point mass at zero to a discrete or censored distribution also capable of producing zeros. Two sub-models are involved: a binary model for the partially observed mixture-component indicators, and a Poisson regression as in the ZI Poisson model (Lambert, 1992) or a left-censored normal as in the ZI Tobit model (Cragg, 1971) . A left-censored log-normal mixed with the point mass at a positive lower limit of detection was introduced by Moulton and Halsey (1995) . In the presence of correlation, random effects were incorporated into either one or both sub-models (Hall, 2000; Berk and Lachenbruch, 2002; Yau et al., 2003; Lee et al., 2006) . Alternatively, Moulton et al. (2002) implemented GEEs with the working independence correlation; Hall and Zhang (2004) developed a GEE approach for the class of ZI exponential family models.. Although much work has been done on fitting ZI data, most derivations have relied on special features of the individual models. We extend the existing latent mixture models through development of a unified framework, the left-inflated mixture (LIM) models for both discrete and semi-continuous data with point inflation at an arbitrary lower bound. This class not only includes current models but is broader by allowing various survival distributions (e.g., censored extreme value, logistic and t distributions) that add flexibility for modeling semi-continuous data. For correlated data, we construct GEEs with the working independence likelihood and estimate the covariance matrix of parameter estimates by the sandwich formula.. The quasi-Newton and EM algorithms are used for common estimation of model parameters. To find asymptotic standard errors associated with the EM, we investigate the generalized Louis method that extends the method of Louis (1982) to dependent data. For the quasi-Newton algorithm, a simulation study is conducted to assess the adequacy of estimating the outer Hessian matrix in the sandwich formula with the approximate Hessian at convergence. The performance and computational speed of the two methods are also compared empirically.. The rest of this article is organized as follows. Section 2 defines the left-inflated mixture models through a latent variable representation. Section 3 concerns maximum likelihood estimation for independent data and generalized estimating equation analysis for correlated responses. Section 4 discusses computational optimization of the estimating criteria by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton and EM algorithms. Standard error estimation associated with each algorithm is discussed. Section 5 presents a simulation study that assesses and compares the two computational methods. The practical utility of our unified approach is illustrated with an ultrasound-induced lung hemorrhage study in laboratory animals in Section 6. Concluding comments are given in Section 7. We have developed the general class of left-inflated mixture models that unifies and expands on a number of existing latent mixture models for bound-inflated data. Parameter and standard error estimation methodology that uses the quasiNewton and EM algorithms based on a GEE approach with the working independence likelihood has been implemented in the R language, so researchers can fit a wide range of bound-inflated mixture models with ease. We propose the generalized Louis method for computing EM standard errors, for which we have derived a simple explicit form of the missing information matrix. For the quasi-Newton method, our empirical results suggest that it is satisfactory to use the approximate Hessian at convergence to estimate the outer Hessian in the sandwich formula, which sheds some light on disagreements in the literature on this issue. The proposed unified framework can be readily extended to upper-bounded or double-bounded data with point inflation at the boundary value(s).. Simulation finds the two computational methods easily implemented, generally performing well, and collectively comparable. There is evidence showing that the EM algorithm is more numerically stable while the quasi-Newton converges faster with fewer iterations and less time per iteration. The EM may be preferred if the data are almost separable between values at the boundary and those exceeding it, or if each component of a LIM model has many parameters requiring estimation. When the data are barely separable and contain a high portion of values at the boundary, the quasi-Newton can save a great amount of time. A further issue, which does not appear to have been addressed previously, is the efficacy of bound-inflated modeling when a reduced one-component model suffices. Our simulation results indicate that, although the observed information may be non-positive definite due to the inflation probabilities approaching one, estimation of regression parameters remain well-behaved.. where, after using the single subscript k, k = 1, . . . , N, to replace the double subscript ij, i = 1, . . . , n, j = 1, . . . , m i ,

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines Abstract-We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations. O VERVIEW is one of the most basic user tasks for information visualization [65] and it is a vital building block for the more complex process of visual explorationusing visualization to form, test, and validate hypotheses about complex or large data sets [53] . However, overview is becoming increasingly difficult to effectively achieve with the ever-increasing size of real-world data sets. For most basic visualization techniques that endeavor to show each item in a data set-such as scatterplots [19] , parallel coordinates [49] , and treemaps [63] -a massive number of items will overload the visualization, resulting in clutter that both causes technical scalability problems [33] as well as hinders the user's understanding of its structure and contents [22] , [57] .. New visualization techniques, such as dense pixel displays [52] , have been proposed for dealing with large data sets, but most of these approaches still attempt to draw each item in the data set. This is not practical for massive data sets. Another solution to this data overload problem, be it technical or perceptual in nature, is to introduce abstraction that reduces the amount of items to display, either in data space or in visual space [22] ; examples include hierarchical parallel coordinates [34] , color histograms [29] , [30] , and clustered time-series data [75] .. In this paper, we draw from this existing work on data abstraction to present a model for transforming virtually any visualization technique into a multiscale structure using hierarchical aggregation. The model is based on coupling aggregation in data space with a corresponding visual representation of the aggregation as a visual entity in the graphical space. This visual aggregate can convey additional information about the underlying contents, such as an average value, minima and maxima, or even its data distribution. Any visualization technique that implements this basic model becomes a multiscale structure, and can be interacted with using generic interaction techniques for multiscale navigation. In this paper, we attempt to unify the design space for this class of visualization techniques, showing implementations for a number of visual representations such as scatterplots, parallel coordinates, and nodelink diagrams. We also give a basic vocabulary of interaction techniques for aggregated visualizations.. There are several benefits to having this kind of model: 1) it represents a way to turn existing visualization techniques into multiresolution versions that scale better to massive data sets; 2) it suggests a method for designing new visualization techniques that directly support multiresolution functionality; and 3) it gives users a unified way of interacting with this class of multiscale visualizations. This paper is organized as follows: we first present the related work on classifying information visualization techniques. We, then, describe the idea behind visual aggregation as a method of data abstraction. We go on to study some examples of hierarchical aggregation in visualization techniques to see how they fit our model. This yields a set of guidelines for how to design and implement hierarchical aggregated visualizations. We exemplify the model and the design guidelines by showing how to add aggregation to a standard zoomable treemap [14] , [63] as well as simple line graphs. We close the paper with our conclusions and ideas on how to extend this work in the future. Our ambition with this paper has been to present a model for hierarchical aggregation in information visualization for the purpose of improving overview and scalability of largescale visualization. While the focus of our work is primarily on supporting the limited perceptual capabilities of the human viewer, many of these techniques will also come in useful for overcoming the technical challenges of rendering massive data sets. We have surveyed a set of standard visualization techniques and seen how they can be extended with hierarchical aggregation functionality. We have also described a set of general interaction techniques for manipulating aggregated visualizations. From this work, we have been able to formulate a set of common design guidelines for building new aggregated visualization techniques as well as extending existing ones with hierarchical aggregation.. Our future work will include refining the model further and deriving new interaction techniques that are common to this whole class of aggregated visualizations. We are also interested in experimentally examining the trade-off between simplification and accuracy that is inherent in all data abstraction models. Furthermore, coupling geometric zoom and level-of-detail expands the model of space-scale diagrams by providing an additional parameter: the granularity/density of the visualized items, an aspect that we want to study further. Finally, this paper so far only discussed tree-shaped hierarchical aggregation schemes-a more powerful concept to explore in the future would be to use DAGs for hierarchical aggregation of certain data structures. Data abstraction for the purposes of reducing visual clutter and data set size is not a novel idea. Shneiderman's taxonomy of information visualization tasks [65] touches upon important data abstraction operations such as filter and zoom as well as the importance of an overview. Oliveira and Levkowitz [23] survey the use of visualization for data mining and discuss some standard data mining techniques for abstracting large-scale data sets, including dimension reduction (e.g., Principal Component Analysis [3] ), subsetting (e.g., random sampling [25] , [56] ), segmentation (e.g., cluster analysis [50] , [51] ), and aggregation. Our work in the present paper is based on the latter technique-hierarchical aggregation of data items, where the original data items are used to create new and aggregated items.. Andrienko and Andrienko [4] give a coherent treatment of data aggregation and its statistical foundations in exploratory data analysis [73] , including a wide range of examples and techniques. On a related note, Billard and Diday [13] introduce symbolic data analysis for statistical analysis of very large data sets. Many of the techniques described in the present paper draw inspiration from this large body of knowledge, but our focus here is on hierarchical aggregation that support more dynamic visual exploration as opposed to the static aggregations traditionally employed in statistics. These techniques can, often, be adapted for hierarchical aggregation, both in data and in visual space.. Most recently, Ellis and Dix [27] present a taxonomy that captures virtually all aspects and strategies of data abstraction for visualization. They identify 11 different strategies for data abstraction and give examples of each, classifying existing work into an annotated taxonomy using a set of criteria derived from the literature. Their approach involves three rough groupings of strategies: appearance (affecting the visual representation of individual data items), spatial distortion (displacing visual data items), and temporal (animation).. However, the work of Ellis and Dix ignores the potentially useful distinction between performing abstraction in data space and in visual space that is highlighted by Cui et al. [22] . More specifically, some abstraction methodsincluding filtering [64] , clustering [50] , [51] , and sampling [25] -operate on the actual items in the data set, whereas others-such as distortion [55] and zooming [58] -affect the visual presentation of the items. However, many authors fail to acknowledge the benefit of providing a link between abstraction methods in data space and their representation in visual space.. Drawing visual representations of abstractions performed in data space support creating simplified versions of a visualization while still retaining the general overview [34] . By dynamically changing the abstraction parameters, the user can also retrieve details-on-demand [65] . Some existing work is based on this kind of visual aggregates; many of these techniques are presented in the body of this paper in the relevant sections. To give a representative sampling, much of the inspiration for this paper comes from the opacity representation of cluster results in parallel coordinates and starglyphs by Fua et al. [34] , edge aggregation in adjacency matrices by Elmqvist et al. [28] , and hierarchical edge bundles for node-link diagrams by Holten [47] (although the latter is not strictly a hierarchical aggregation technique).. Yang et al. [82] present a framework for interactive hierarchical displays that bears much resemblance to our work. In particular, they present hierarchical implementations for parallel coordinates [49] , starplots [66] , scatterplot matrices [19] , and dimensional stacking [54] . However, unlike our model, their framework is designed solely for multivariate data sets and specifies a concrete visual representation and color coding for the aggregates. Therefore, our model is slightly more general, although by virtue of being more general, we also provide less guidance in actually implementing new multiscale visualizations.. As for the technical challenges of large-scale data sets, Fekete and Plaisant [33] show how to overcome them for data sets on the order of 10 6 items using scatterplots and treemaps. However, their approach is to preserve all visible items in the visualizations without any data abstraction, so while their findings-as well as similar findings on the scalability of visualization-are useful for tool architects, they are not directly relevant to the more conceptual nature of this work.

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Parallels between the analytic hierarchy and network processes (AHP/ANP) and fractal geometry The aim of this work is to show the parallelisms and analogies that exist in modeling and measuring of dependence and feedback processes, in physical and in decision making processes, that is, to compare among the scales of measurement of the physical world (geometry) and the scales of measurement of the human being's internal decision process, in other words, the brain's internal generation of a relative measure scale. Concerning the phrase:". . . the brain's internal generation of relative measure scales" in the Abstract above, we can quote the following from Saaty [1] : "Trading off is the human form of doing things. Nature even makes tradeoffs in the human body by setting up hormones that operate on compensation principles. The brain is empowered to supervise equilibrium by ordering or blocking the secretion of one substance or another to orchestrate the workings of the entire body. I believe that the brain itself must operate on a relative principle, as it has no stored scales of measurement to determine the absolute amount of each substance needed to maintain balance".. As an example of this we can add that the values described as "normal" in the medical literature for lipoproteins represent a range defined by the statistical average for a population; therefore, individuals may exist with levels in the lower bound of the accepted range, and others in the upper bound, still being physiologically healthy with differences up to 50% in their values. Any intent of modifying these values in a pharmacological way or by means of diet may result in overcompensation that shows up by driving hormonal responses into a variety of other pathological symptoms (hyperglycemia or hypoglycemia, variations in the triglycerides, etc.).. Indeed, from the work of Saaty [1] "the creative mind, (it) is driven by the imagination and our ability and reasoning ability. Imagination is fragmentary and needs purpose and cohesion unity. In science, imagination always precedes unity. Synthesizing its creations takes time. A long-standing and troubling observation to me has been the fragmentary and evanescent nature of our knowledge. This is due both to the diversity of our experience and to an absence of goal-oriented thought structures -hierarchies -needed to link knowledge from one set of goals to a higher set and ultimately for our survival.".. As an intermediate objective of this work, we examine the basic concepts of measurement interpretation and quantification, and their development in history (Euclidean, Cartesian and fractal geometry) [4] . Metric concepts and their interpretation in the physical world (for example, the concept of distance), and in the decision-making world, are examined and we point out the analogies between metric axioms and the Analytic Hierarchy Process (AHP) axioms.. This work also considers some of the mathematical parallelisms that exist in the representation of reality by both systems (ANP & fractal geometry) and in the modeling processes used in both systems such as: limits, convergence, stability, attractors, and still others like errors, randomness, consistencies and their physical interpretations, all of which are shown in a graphical way with simple explanations and interpretations.

O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Fast factorization of Cartesian products of (directed) hypergraphs Cartesian products of graphs and hypergraphs have been studied since the 1960s. For (un)directed hypergraphs, unique prime factor decomposition (PFD) results with respect to the Cartesian product are known. However, there is still a lack of algorithms, that compute the PFD of directed hypergraphs with respect to the Cartesian product.In this contribution, we focus on the algorithmic aspects for determining the Cartesian prime factors of a finite, connected, directed hypergraph and present a first polynomial time algorithm to compute its PFD. In particular, the algorithm has time complexity O(|E||V|r 2 ) for hypergraphs H = (V, E), where the rank r is the maximum number of vertices contained in an hyperedge of H. If r is bounded, then this algorithm performs even in O(|E| log 2 (|V|)) time. Thus, our method additionally improves also the time complexity of PFD-algorithms designed for undirected hypergraphs that have time complexity O(|E||V|r 6 ∆ 6 ), where ∆ is the maximum number of hyperedges a vertex is contained in. Products are a common way in mathematics of constructing larger objects from smaller building blocks. For graphs, hypergraphs, and related set systems several types of products have been investigated, see [18, 14] for recent overviews.. In this contribution we will focus on the Cartesian product of directed hypergraphs that are the common generalization of both directed graphs and (undirected) hypergraphs. In particular, we present a fast and conceptually very simple algorithm to find the decomposition of directed hypergraphs into prime hypergraphs (its so-called prime factors), where a (hyper)graph is called prime if it cannot be presented as the product of two nontrivial (hyper)graphs, that is, as the product of two (hyper)graphs with at least two vertices.

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Quantitative support for UX methods identification: how can multiple criteria decision making help? Abstract This paper presents views on how quantitative multiple criteria decision-making (MCDM) approaches may be applied to certain aspects of user experience design and evaluation (D&E) methods identification, emphasizing its strengths and weaknesses for this task. Often D&E methods need to be applied in contexts different of those they had been applied before and as such must be transferred to those new contexts. This work presents a model for the quantitative method matching step of the transfer process, describes how different MCDM methods can be applied to this task, and discusses the results of an experience that tried to apply a couple of MCDM procedures to method selection. To support the task of usability and user experience design and evaluation, several procedures, called usability or sometimes, user experience (UX) evaluation methods, have been proposed through the years [13] . However, not all methods are well suited or may be used in all situations. In general, methods present a set of characteristics, which may make them better matched for particular situations or problems (usually those to which they were proposed to solve). This work intends to show how MCDM could be applied to help perform this selection using the knowledge of those characteristics.. Notice that while it can be argued that a method is not necessarily the adequate unit to be used when describing evaluation transfer [25] , the same considerations could be extended to smaller or larger units (e.g., ingredients or meals in the work cited, although in this work only the term ''method'' will be used to describe a component of a design or evaluation process with reproducible characteristics that can be applied to different problems).. A few additional scenarios may also require finding a particular method for a particular situation:. • You know what kind of characteristics you require on your UX evaluation, but do not know which methods will provide them.. • You want to learn about methods sharing characteristics with a ''favorite'' UX method.. • You want to find the methods compatible with universal access, or create universal design resources to allow the selection of such methods (see e.g., [12] ).. To find which methods are best suited to particular UX usage circumstances or usage situation (henceforth just called ''situation''), it is necessary to match the known characteristics of the methods to the requirements of the situation (and likewise, the known necessities of the methods to what the method user is able to provide in that situation). If a method is being applied in a different application domain (the so-called method transfer), domain-specific information may need to be made explicit as situation characteristics and method characteristics (e.g., some characteristics may be present in a domain but not in another, so a method that requires such characteristics would not be able to be applied). In this work, it is assumed that the methods will not see their characteristics changed by adaptation during the process of application to another domain (or, at minimum, the characteristics that are considered relevant for the choice problem will not be changed by the process).. Besides the ''characteristics immutability'' prerequisite described, the procedures discussed in this article also require that the information regarding method and situation characteristics is knowable, at some level (or at least that a subset of those is knowable, if procedures handling incomplete information are used). That is, at the level one requires the ''method'' identification to happen (be it as ''methods,'' ''patterns,'' ''ingredients'' or ''recipes,'' see [25] ; its immutable characteristics (both in what it provides to its practitioners and what requires to be used) must be able to be described. An additional prerequisite of the procedure is that the characteristics of the situation to which the method is to be applied are also known (both in what is required of the method in that situation-situation requirements-and of which resources can be provided). In an ideal situation, one would then proceed to match the mutual requirements to the mutual resources, to find the ''best match.'' In this work, it will be shown how multicriteria decision-making (MCDM) techniques could be applied to this task and through an example depict some results and uncover a few practical problems in using such techniques.. In the following section, a model for quantitative method matching is proposed (Sect. 2), followed by the description of a practical exercise using this model (Sect. 3) and analysis of the results of this exercise. Next, alternative approaches are discussed (Sect. 4), and the paper concludes by commenting the strengths and weaknesses of the approach followed (Sect. 5). The field of usability and user experience design and evaluation has generated a significant number of methods and approaches through the years (see for instance, [13] for a characterization of some of them). Their different characteristics imply that their application to all situations may not be desirable, or indeed even possible. As such, work has been developed to help with the task of selecting the adequate methods to particular situations, from dedicated sets of methods [18] to method characteristics reviews [13] to interactive tools to help method selection [7] . However, in these approaches, the actual task of method selection is either left to the user, or is handled by a simplified approach, that does not provide the decision maker with a decision ''audit trail'' to justify the choice.. On the other hand, the selection from a set of alternatives possessing different characteristics of the most relevant subset is a long known problem addressed by multicriteria decision methods, both in individual and group situations (see e.g., [2, 6] ). Those methods have been applied to a large number of problems, namely in computer science and service application matching [1, 15, 23] ; however, the authors know of no previously published MCDM applications relating directly to the problem of matching UX evaluation methods.. The model proposed uses a simple distance measure to generate multiple criteria matching. Different scalarizing functions have been proposed in multi-criteria optimization (see [16] ), and others could be proposed as adequate in this situation (namely to prevent, in the absence of veto, large differences in one criterion to overwhelm the comparisons). However, the choice of another scalarizing function by itself would not alter the process proposed (and could make the process less understandable for the decision makers, if harder to compute, less intuitive metrics were used instead).. The limitations of using distance-based approaches to matching problems should not be overlooked. Some alternatives have also been proposed to characteristics matching problems, such as semantic matching [9, 21] , which may be constructed removing some of these limitations. However, it can be argued that the multiple criteria approach proposed is both simpler and less dependent on the previous construction of a full domain ontology required to overcome the use of arithmetic distance definitions (although, as stated, the requirements on information collection are not irrelevant even in the proposed approach).

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Fast Insertion-Based Optimization of Bounding Volume Hierarchies We present an algorithm for fast optimization of bounding volume hierarchies (BVH) for efficient ray tracing. We perform selective updates of the hierarchy driven by the cost model derived from the surface area heuristic. In each step the algorithm updates a fraction of the hierarchy nodes in order to minimize the overall hierarchy cost. The updates are realized by simple operations on the tree nodes: removal, search, and insertion. Our method can quickly reduce the cost of the hierarchy constructed by the traditional techniques such as the surface area heuristic. We evaluate the properties of the proposed method on fourteen test scenes of different complexity including individual objects and architectural scenes. The results show that our method can improve a BVH initially constructed with the surface area heuristic by up to 27% and a BVH constructed with the spatial median split by up to 88%. The current ray tracing based algorithms allow to capture even complex illumination effects and reach high degree of realism of the rendered images. With advances in computational power and algorithmic efficiency the ray tracing based methods have also become an alternative to rasterization for interactive and real time applications. Unlike rasterization, ray tracing relies on a highly efficient acceleration data structure which allows to trace tens or hundreds million rays per second.. Constructing such high quality acceleration data structures is thus very important and it has received a strong attention in the last two decades. Even a relatively small improvement in performance can bring an interactive ray tracer closer to real-time or provide significant time savings when rendering many high quality images of complex and detailed scenes in the movie industry.. There are two major classes of data structures used for ray tracing acceleration: spatial subdivisions (such as kd-trees, octrees, or grids) and bounding volume hierarchies (BVH). In this paper we propose a method which allow to optimize a given BVH beyond the current state of the art techniques.. The method is based on an iterative algorithm that changes the topology of inner nodes of a bounding volume hierarchy in order to improve the quality of the hierarchy initially built with arbitrary method. An example visualization of the reduction of traversal steps achieved by the proposed method is shown in Figure 1 .. Our method constructs more efficient bounding volume hierarchies in shorter time than previous approaches, while it allows to easily trade-off the time used for updating the BVH and the expected traversal cost. Compared to the current state of the art approach for high quality BVHs proposed by Kensler [Ken08] (simulated annealing), our algorithm is 25 to 147 times faster for architectural scenes, while achieving BVHs of comparable or even better quality. The method is applicable to a BVH built with an arbitrary technique, it is simple to implement and thus it has a potential to become a common optimization approach following the construction of the hierarchy.. The paper is further structured as follows. In Section 2 we describe the related work, Section 3 describes the proposed algorithm, Section 4 shows the results and Section 5 concludes the paper. We proposed an algorithm for building a high quality BVH by incremental updates of the BVH initially constructed by a top down method with surface area heuristic.. The method is based on performing selective updates of the BVH by identifying problematic nodes and reinserting them back in appropriate positions in order to minimize the total BVH cost. The updates are prioritized and the resulting method is highly flexible in terms of the update time with respect to the quality of the hierarchy.. We have shown that for complex scenes our method achieves very good cost reduction in much shorter time than previous methods. In fact the results indicate that the method constructs the best currently known BVHs under the SAH cost model and thus it has a potential to become a common optimization technique, which further reduces the cost of the SAH builders used in practical applications.. Currently, we work on a parallel version of the algorithm on modern GPUs in CUDA, where both the update and the rendering algorithms run solely on the GPU. We also want to study the properties of the hierarchy for other visibility computations such as occlusion and view-frustum culling for large scenes. Another possible future work is to study other types of bounding volume hierarchies such as those with explicit spatial splits or with higher branching factor. The results for architectural scenes where the base hierarchy for all methods is created with a spatial median top down driven build (method 1), which for methods 2 to 5 is followed by an update phase. These are as follows: 2 -our method with random selection, 3 -our method with the combined measure of inefficiency, 4 -Kensler's method, hill climbing, and 5 -Kensler's method, simulated annealing. The values in brackets are the computed ratios against reference values (1.00). Table 2 , 3, 4, and 5), the figures in even columns to compacted trees with more triangles per leaf (ocost in Table 2 , 3, 4, and 5). The odd rows show the sum of costs for interior nodes and the even rows the sum of costs for leaves in the dependence on the depth. The results for trees constructed by top down building algorithm with surface area heuristic are in red colour, the results for the trees after optimization by our algorithm are in green colour. The idea of our optimization algorithm is to repeatedly optimize the BVH, which is similar to the paper of Kensler [Ken08]. There are two major differences in the core of the optimization procedure that result in a different behavior of our method. First, Kensler's method always traverses the whole tree in a depth-first-search order to change the hierarchy around the traversed nodes. Our approach, on the other hand, selects the nodes to be optimized either randomly or using importance based decision. Second, the Kensler's method changes the topology of the tree only locally using one of four possible rotations. Although the sequence of rotations can principally lead to any BVH including the tree with a globally optimized cost, the changes in a BVH for one optimization step are much smaller than in our algorithm, where the subtrees are removed and then reinserted into the most appropriate position. That is, in our algorithm one optimization step can change (and often changes) the tree globally and hence has the potential of improving the cost more efficiently.. Unlike Eisemann et al.. [EGMM07] our search for the best candidate to reinsert the node intentionally starts at the root to find the best suitable position in the scope of the whole tree. Using the upwards traversal in the method of Eisemann et al.. [EGMM07] reduces the number of traversal steps during the optimization, but does not necessarily find a good position for the inserted node. We have implemented the proposed algorithm in a single threaded C++ application. The results of BVH optimization and CPU ray tracing were evaluated on a PC with Linux OS, Intel(R) Xeon(R) CPU E5440 with the frequency 2.83GHz and 40GB of RAM, and GNU C compiler version 4.40. To compute the cost according to equations 1 and 2 we used c T = 3.0 and c I = 2.0. As termination criteria described in Section 3.4 and 3.5 we used k = 1%, p T = 10, and p R = 5.. We have evaluated the method on fourteen models of different complexity which we classify into two distinct groups: (1) individual objects and (2) architectural scenes, i.e., models of larger spatial extent typically containing many objects. We have constructed the initial BVH by top down recursive procedure using a precise SAH builder which evaluates all discontinuities (two positions for each triangle) in the cost function for all three axes. The rendered images for objects and scenes are shown in Figure 9 and 10. There is a large body of literature on efficient spatial data structures for rendering. We restrict our discussion to the BVH methods for efficient rendering of static scenes.. The bounding volume hierarchies have a long tradition in the context of ray tracing. Rubin and Whitted used rectangular bounding volumes to create a BVH manually [RW80] . Weghorst et al.. [WHG84] studied different types of bounding volumes for BVH and used a modeling hierarchy to build it. Kay and Kajiya [KK86] suggested to create a BVH by an automatic top down recursive algorithm with a spatial median method and studied the efficiency of different bounding volumes. Goldsmith and Salmon [GS87] proposed the measure currently known as the surface area heuristic (SAH) which predicts the efficiency of the hierarchy already during the BVH construction. However, as they have built up the BVH incrementally by insertion, the BVH was usually inefficient as shown in performance study by Havran [Hav00] and later by Masso et al. [ML03] . The improvements for the efficiency of BVH were discussed in several articles in Ray Tracing News [RTN] and suggested in literature more than a decade ago, for example in the paper by Smits [Smi98] . [HMF07] who suggest reusing the organization of geometric data in the scene graph. They show that when certain assumptions about the scene graph hold, it is possible to achieve linear time complexity of BVH construction. Dammertz et al. [DHK08] proposed the variation of BVH using the branching factor of four to gain better utilization of SIMD units in modern CPUs.. Wald [Wal07] proposed a CPU based parallel BVH construction by separating the build up process into two stages -horizontal and vertical parallel execution. Another asynchronous construction method was presented by Ize et al. [IWP07] . More recently, the parallel build-up of BVH has been demonstrated also on GPU by Lauterbach et al. [ LGS * 09], using 3D space-filling curve. Aila and Laine [AL09] identify the sources of inefficiency for traversing BVH on a GPU and propose a simple solution to improve the performance. Wald studied the possibility of fast rebuilds from scratch on upcoming Intel architecture with many cores [Wal12] . Pantaleoni and Luebke [PL10] and Garanzha et al.. [GPM11] recently proposed GPU based methods for parallel BVH construction that are able to very quickly construct a BVH, however for complex scenes the BVH quality for these methods can be significantly lower than that of the full SAH builder.. Several papers have been published that relax the condition of having only one reference to each primitive. Ernst and Greiner [EG07] suggest to precompute several bounding boxes for each primitive and use them independently during top down construction. More recently, to achieve higher performance Stich et al. [SFD09] and Popov et al. [PGDS09] suggest to use spatial splits during the BVH construction, hence reintroducing some properties of kd-trees to BVHs.. Walter et al. [WBKP08] , propose to use bottom-up agglomerative clustering for constructing high quality BVH. Kensler [Ken08] propose to optimize the BVH in postprocess using tree rotations with the hill climbing or simulated annealing optimization algorithms. Both these approaches allow to decrease the expected cost of BVH compared to the top down approach.. The goal of our work is the most similar to the paper of Kensler [Ken08] , where the BVH is optimized beyond the common golden standard, i.e. BVHs built in top down fashion with the surface area heuristic. Compared to the method of Kensler [Ken08] our optimization procedure is significantly faster, and for complex scenes it results in hierarchies with lower costs.

O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


@Note: A workbench for Biomedical Text Mining Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists' needs is crucial to solve real-world problems and promote further research.We present @Note, a platform for BioTM that aims at the effective translation of the advances between three distinct classes of users: biologists, text miners and software developers. Its main functional contributions are the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a Text Mining Module supporting dataset preparation and algorithm evaluation. @Note improves the interoperability, modularity and flexibility when integrating in-home and opensource third-party components. Its component-based architecture allows the rapid development of new applications, emphasizing the principles of transparency and simplicity of use. Although it is still on-going, it has already allowed the development of applications that are currently being used. Nowadays, the ability to link structured biology-related database information to the essentially unstructured scientific literature and to extract additional information is invaluable for Computational Biology. Although an ever growing number of repositories is available, crucial theoretical and experimental information still resides in free text [1] .. Biomedical Text Mining (BioTM) is a new research field [2] aiming at the extraction of novel, non-trivial information from the large amounts of biomedical related documents and its encoding into a computer-readable format. Traditionally, the act of literature curation, i.e. the inspection of a document and the extraction of relevant information, was exclusively manual. However, the outstanding scientific publication rate, the continuous evolution of the biological terminology and the ever more complex analysis requirements brought by systems-level approaches urge for automated curation processes [3] [4] [5] .. BioTM encompasses Information Retrieval (IR), Information Extraction (IE) and Hypothesis Generation (HG) as its main areas. IR deals with the automatic search and retrieval of relevant documents from the Web, taking advantage of available bibliographic catalogues and providing for local copies of potentially interesting publications whenever possible. IE embraces all activities regarding automated document processing, namely Named Entity Recognition (NER) [6] [7] [8] [9] (also referred, along this work, as semantic tagging), Relationship Extraction (RE) [10] [11] [12] , Document Classification [13, 14] , Document Summarisation (DS) [15, 16] and the visualisation and traversal of literature data [17, 18] . Its foremost aim is to emulate human curators, annotating biological entities of interest and relevant events (relationships between entities) in such a way that both document visualisation and further content analysis can deliver valuable knowledge. HG addresses the conciliation of literature-independent data (e.g. from laboratory or in-silico experiments) with the specific annotations derived from the literature, confirming IE results and assigning additional functional, cellular or molecular context [19] [20] [21] . In this paper, we will focus only on the IR and IE areas. ABNER [27] AliBaba [28] BiolE [29] Chilibot [20] EBIMed [25] EDGAR [30] EMPathIE [31] GAPSCORE [32] GeneWays [33] GIS [34] GoPubMed [35] iHOP [36] LitMiner [37] MedEvi [38] The @Note project aims at fulfilling the existing gap between BioTM researchers and BioTM potential users. It was designed to target three different user roles: biologists, text miners and application developers. It provides user-friendly tools that aid users without BioTM expertise in managing and processing the ever growing literature. Furthermore, it accounts for BioTM research needs, providing means for experts to prepare and deploy NLP and ML experiments using well-known tools such as GATE, YALE and WEKA. Also, it is built on top of AIBench framework, which facilitates the design and deployment of new applications as well as low-level tool integration.. At the best of our knowledge, @Note is the first tool to integrate these three usage roles.. Another of its strengths is its integrated design that allows the development and evaluation and of state-of-the-art BioTM techniques. The manual curation of automatic document annotation contributes to enhance lexicon support as well as to produce controlled corpora, an invaluable asset for BioTM research.. Given the nature of this project, the main effort in future work will be the development and integration of new functionalities, to be integrated in new @Note plug-ins. We demonstrate the strengths of @Note in a real-world scenario embracing different usage roles. Imagine that a biologist or biotechnologist is interested in the study of stress responses triggered by nutrient starvation (i.e. stringent response) in Escherichia coli. This phenomenon is quite common when E. coli is used for the production of biopharmaceuticals and it reduces the productivities that can be obtained [69] . Therefore, its full characterization is indispensable for the identification of strategies to overcome it.. The ultimate goal of this study would then be to obtain a systems-level view of the biological process, by characterizing the mechanisms in the basis of this response. A major part of this characterization is the identification of the main entities involved (genes, proteins, metabolites, etc.), since they can be targets for strategies to inhibit this stress response. Although some data can be retrieved from public databases, the major part of the information still lies in the literature. Moreover, it is not likely that such information could be retrieved from abstracts, since detailed molecular mechanisms are usually described in the full text. A user-friendly tool like @Note could then be of major interest for cases like this, since it allows automatically retrieving and partly curating relevant documents. From the biologist perspective and for this problem, the workflow would set as follows (Fig. 4) : search PubMed for available information and, whenever possible, retrieve full-text documents; automatically process the set of publications and, in particular, perform lexical-based NER with an organismspecific dictionary; manually revise some or all annotations, ensuring the reliability of further information extraction; and inspect the overall corpus results.. Implicitly, this process is incremental, as new documents are always appearing and their contents should be incorporated in the overall view. As such, the biologist is interested in refining not just document annotations, but also the lexical resources supporting NER which will enhance posterior knowledge acquisition stages. Furthermore, the biologist may consider using more advanced approaches for NER by posing his problem to a text miner that will create NER classification models. The text miner will be responsible for dataset preparation and the evaluation of different ML techniques. By comparing the performance of those techniques, he will propose a configuration for ML-based model for NER. The biologist will determine whether the model outperforms lexicon based NER results and, if not, may provide the miner additional information about the problem. 3.1. The Biologist perspective -looking for documents on E. coli stringent response and delivering an annotated corpus All operations defined within this section can be performed using the @Note Basics (+ML-based models) application provided in the project's web site. Furthermore, the detailed steps of this task and relevant screenshots are given in Supplementary Material (also available at the web site). At first, the biologist defines the keyword-based query as ''Escherichia coli stringent response". Automatically, @Note deploys a PubMed search and information on the documents (including abstracts) is retrieved (Fig. 5) . As we are interested in full-text contents, pdf document retrieval is also issued, but only for documents that are not already catalogued (obtained in previous queries). Process duration will depend on the . Information retrieval with @Note: obtaining and visualising documents from PubMed related to a specific query. The biologist asks PubMed for publications related to a specific topic and, in order to get detailed information, journal crawling is also issued. @Note stores the outputted documents into the bibliographic catalogue, supporting further access and processing. amount of documents already in catalogue, available Internet bandwidth (retrieval speed) and the performance of journal servers (web site and catalogue administration). Nevertheless, the process is completely transparent to the user that will only acknowledge the set of documents outputted for the query.. PDF conversion into plain text and basic document structuring are also transparent to the user that will only see the documents already in HTML format. Biologist intervention is requested only at L-NER, where he configures the process by selecting the most adequate dictionary, and eventually, some additional lookup tables and rules. The user can choose one of the existing dictionaries (like the E. coli dictionary we have built from a Biowarehouse repository) or may deploy the construction of a particular one from the current set of supported data sources. Also, if using a multi-class dictionary, the user may specify the subset of supported biological classes to be annotated (for example, only genes and proteins). Automatically annotated documents (Fig. 6 ) are made available to the user in the manual curation environment for expert revision. In this environment, identified terms appear in different colours, depending on their class, facilitating the view of the annotation performed. When correcting or adding a new annotation, the biologist uses expert knowledge to deal with dictionary incompleteness and eventual inconsistencies. Moreover, problems such as the disambiguation of distinct mentions using the same term (e.g. same gene, protein and RNA name) is a classical example where manual curation is invaluable.. The biologist is also able to search for additional information about text mentions (already annotated or not) in Web-accessible databases such as UniProt or Biocyc, accounting for unfamiliar terminology and confirming some classifications.. After performing expert revision or immediately after automated L-NER, the user has the opportunity to look at some statistics (Fig. 7) regarding, for example, the frequencies of a given entity within the documents analysed.

O O O O B I I B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O