Efficient Substructure RMSD Query Algorithms Protein structure analysis is a very important research topic in the molecular biology of the postgenomic era. The RMSD (root mean square deviation) is the most frequently used measure for comparing two protein 3-D structures. In this paper, we deal with two fundamental problems related to the RMSD. We first deal with a problem called the 'range RMSD query' problem. Given an aligned pair of structures, the problem is to compute the RMSD between two aligned substructures of them without gaps. This problem has many applications in protein structure analysis. We propose a linear-time preprocessing algorithm that enables constant-time RMSD computation. Next, we consider a problem called the 'substructure RMSD query' problem, which is a generalization of the above range RMSD query problem. It is a problem to compute the RMSD between any substructures of two unaligned structures without gaps. Based on the algorithm for the range RMSD problem, we propose an O(nm) preprocessing algorithm that enables constant-time RMSD computation, where n and m are the lengths of the given structures. Moreover, we propose O(nm log r/r)-time and O(nm/r)-space preprocessing algorithm that enables O(r) query, where r is an arbitrary integer such that 1 ≤ r ≤ min(n, m). We also show that our strategy also works for another measure called the URMSD (unit-vector root mean square deviation), which is a variant of the RMSD. In the post-genomic molecular biology, analysis of protein 3-D structures becomes more and more important. Recently, enormous amount of protein 3-D structures are solved and the number of them are still increasing, as seen in the PDB database [Berman et al. 2000] . As a result, faster algorithms to deal with such structures are seriously needed. Two proteins are said to have similar functions if their 3-D structures are similar to each other, and structure comparison is one of the keys to the protein function analysis. There are many structure comparison algorithms for proteins [Eidhammer et al. 2000] . Many of these algorithms are dedicated to align the structures by inserting gaps so as to minimize some score, as in the cases of ordinary sequence alignment algorithms. But the alignment is not the goal of the protein analysis. For example, we must search for specific domains like conserved substructures or hinge-mediated domains from the aligned result.. The most famous similarity measure for protein structures is the RMSD (root mean square deviation) [Arun et al. 1987 , Eggert et al. 1997 , Kabsch 1976 , Kabsch 1978 , Schawrtz et al. 1987 . There are several variations of the RMSD measure. The URMSD (unit-vector root mean square deviation) [Chew et al. 1999 is an example. To analyze the aligned protein structures, we often need to compute local RMSDs (or URMSDs) of aligned substructures. For example, in [Huang et al. 1993] , they compute all the RMSDs between all the possible aligned substructures in the aligned pair of structures to find hinge-mediated domains. Similar techniques are used also in many others [Nigham et al. 2007 , Shibuya 2007 . Hence, we consider the following problem in this paper. . Without any preprocessing, we need O(k) time to compute it, where k = j − i (see section 2 for details). We propose an algorithm that answers the above problem in constant time, with linear-time preprocessing. Note that naive preprocessing algorithm would require O(n 2 ) time or more, as there are O(n 2 ) possible aligned substructures. Moreover, we will show that the same strategy works for the URMSD.. We next consider the following problem, which is a generalization of the above range RMSD query problem. . By straightforwardly using our range RMSD query algorithm, we can solve this problem in constant time after O(nm)-time preprocessing. Note that we would require O(nm min(n, m)) time or more for preprocessing if we do it naively. For this problem, we also propose O(nm log r/r)-time and O(nm/r)-space preprocessing algorithm that enables O(r) query, where r is an arbitrary integer such that 1 ≤ r ≤ min(n, m).. The substructure RMSD query problem is more general than the range RMSD query problem, and must have many applications in protein structure analysis. This problem appears everywhere, e.g., as a subroutine for alignment algorithms. Most protein alignment algorithms take very large time and is sometimes requires more than O(nm) time. Thus we assume our algorithm can be very valuable for designing such alignment algorithms.. The organization of this paper is as follows. In section 2, we present the definitions of the RMSD and the URMSD. Then, we present an algorithm for the range RMSD query problem in section 3, and we present algorithms for the substructure RMSD query problem in section 4. Finally in section 5, we conclude our results. In this paper, we dealt with two fundamental problems called the 'range RMSD query problem' and the 'substructure RMSD query problem', both of which are very important for protein structure analysis. For the range RMSD query problem, we proposed a constant-time query algorithm after linear-time preprocessing. Based on the algorithm, we showed that we can solve the substructure RMSD query problem in constant time after O(nm) time preprocessing, where n and m are the lengths of the structures to be compared. Moreover, for the latter problem, we also developed an O(r) query algorithm after O(nm log r/r) time and (nm/r) space preprocessing, where r is an arbitrary integer such that 1 ≤ r ≤ min(n, m).. Our algorithms can be used in various scenes in protein structure research. Actually, we have developed a hinge detection algorithm for flexible protein 3-D structures based on our algorithm [Shibuya 2007 ]. Development of faster query algorithms, or more flexible query algorithms remains as future work.

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


Evaluating the retrieval effectiveness of Web search engines using a representative query sample Search engine retrieval effectiveness studies are usually small-scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, data was collected using specialised software, the Relevance Assessment Tool (RAT). We found that while Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, as users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines. Given the importance of search engines in everyday use (Purcell, Brenner, & Raine, 2012; van Eimeren & Frees, 2012) which is also expressed by the many millions of queries entered into generalpurpose Web search engines every day ("comScore Reports Global Search Market Growth of 46 Percent in 2009 Percent in ," 2010 , it is surprising to find how low the number of research articles on search engine quality still is. One might assume that large-scale initiatives to monitor the quality of results from general-purpose search engines such as Google and Bing already exist. Users could refer to such studies to decide which search engine is most capable of retrieving the information they are looking for. Yet as we demonstrate below, existing search engine retrieval effectiveness studies have covered a much smaller sample so far. This small size has cast a degree of doubt on their conclusions. One could argue that it is the search engine vendors' sole responsibility to conduct tests on the retrieval effectiveness of their engines in relation to their competitors (and they surely conduct such studies). The goal of studies conducted by the vendors and academic studies respectively, however, are quite different. While the goal of studies conducted by search engine vendors is to improve a certain search engine (and test additional capabilities such as dealing with search engine spam), the purpose of academic studies is to advance retrieval effectiveness testing and provide a valid picture of the quality of different search engines. Decision-making, revolving for instance around funding alternative search engines, is facilitated with this information. Although search engines are using click-through data to a large degree to improve their services and existing research focuses on this area (Chuklin & Serdyukov, 2013; Joachims et al., 2007; Joachims, Granka, Pan, Hembrooke, & Gay, 2005) , search engine providers still conduct classic retrieval effectiveness tests using human jurors recruited for that task (Google Search Quality Rating Guidelines, 2012) . Retrieval effectiveness tests are just one part of the overall evaluation of search engine quality (Lewandowski & Höchstötter, 2008) . Measuring the effectiveness of search engine results is an integral part of such frameworks and is usually based on a TREC-style setting. However, these methods have been criticised as being inadequate for evaluating Web search engines, where results presentation differs from other information retrieval systems (Lewandowski, 2013a) . Furthermore, retrieval effectiveness tests have been criticized for not taking into account actual user behaviour, i.e., they are based on the query-response paradigm and do not consider interaction in the searching process (Ingwersen & Järvelin, 2005) . While these criticisms should not be ignored, continued enhancement of query-response-based methods is still a valuable pursuit.. Many search engine retrieval effectiveness tests have been conducted in the past. One problem they all share is that they only use a relatively low number of queries. The selection of queries is also not representative of the queries entered into actual search engines. In this paper, we present a study using 1,000 queries representatively selected from the transaction logs of a major German search portal. To our knowledge, this is the first attempt to use a representative query sample for a search engine retrieval effectiveness study. The general goal of this paper is to show how large-scale search engine retrieval effectiveness studies can be conducted, and to present valid results based on a representative query sample. The rest of this article is structured as follows: First, we will review related work on search engine retrieval effectiveness. Here we will focus on testing for different query types, the general design of such studies, and the selection process for queries in such tests. Next we will present our objectives and research questions, followed by our methods and findings. We then discuss our findings and summarize them in the conclusions section, explaining limitations and providing some suggestions for further research. To summarize our findings, we can conclude that Google outperforms Bing overall (RQ1). With respect to RQ2, there are clear differences regarding the navigational queries, where Google outperforms Bing significantly. While there are also differences regarding the informational queries, these are not too pronounced, and we argue that the differences will not be noticeable to the general user, but only when scientifically comparing search engines. Regarding the use of graded relevance judgments (RQ3), we argue that while the overall outcome when comparing the two search engines is not different, graded relevance judgments allow one to detect additional effects (i.e., Bing producing a considerably larger amount of completely irrelevant results). We therefore recommend using graded instead of binary relevance judgments. The results from our study are comparable to previous studies. However, due to the improved sample size, the validity of our study is much greater. The software tool and the crowdsourcing approach make much larger studies possible than before. We encourage researchers to improve the validity of their work through larger studies and better query sampling. When considering that there is only one correct result for navigational queries, whereas for informational queries, there may be multiple results of equal relevance (and different users may find different results relevant), we argue that Google's superiority as perceived by the search engine users can (at least in part) be explained by its performance on navigational queries. This explanation adds to the literature, where aspects beyond results quality, such as search engine brand (Jansen et al., 2009) , are considered when discussing user satisfaction with the results of their favourite search engine. The main difference between the relevance of the results produced by the two search engines in this study is that Google outperforms Bing on the informational queries, and even more so on the navigational queries. As navigational queries account for a large ratio of the total queries -between 15% and 40%, dependent on the source ) -we can reason that the user satisfaction with Google can in part be explained by Google's good performance on such queries. Users are definitely able to judge the relevance of results for navigational queries, as there is only one correct result for such queries. For informational queries, not only is there more than one relevant result in most cases, it is also hard for users to determine whether or not the results set presented by the search engine is indeed the best possible results. It may well be that (1) one result or even numerous relevant results are missing in the results set of the search engine in use, or (2) there may be equally relevant results presented by another search engine, that contribute aspects not considered in the results presented by the search engine in use. The latter case is especially important since two different search engines could produce results that are of equal relevance, but completely different from one another. Studies examining the overlap of results sets of the major search engines (Spink, Jansen, Blakely, & Koshman, 2006) found that the overlap in the top 10 results was quite low. However, this does not necessarily mean that one search engine produces more relevant results than the others. It could also mean that there are enough relevant results for most queries, so that two completely distinct results sets may be of equal relevance. While this is surely a field for further research, it leads us to the conclusion that further aspects of the results should be considered to determine which search engine produces the best results and to advanced search engine comparisons methodologically. Promising aspects include the credibility of the results (Kammerer & Gerjets, 2012; Lewandowski, 2012) , the diversity of the results (Denecke, 2012) and the commercial influence in the results lists (Lewandowski, 2011b) . When looking at the distribution of relevance judgments on the Likert scale, the most remarkable finding from the study is that Google outperforms Bing mainly because it produces significantly fewer results graded as completely irrelevant. We can therefore assume that Bing's problem does not lie in the fact that it produces fewer relevant results than Google, but rather in identifying whether results are at least somehow relevant to a user. This can be seen more as a spam detection problem than as a ranking problem. In this section, we first present the results for the navigational queries and then the results for the informational queries. The latter section comprises the analysis of the binary and graded relevance judgments. Our study will also focus on informational and navigational queries. Transactional queries have been omitted because they present a different kind of evaluation problem (see above), since it is not possible to judge on the relevance of a certain amount of documents found for the query, but instead, one would need to evaluate the searching process from query to transaction.

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


Biclustering Sparse Binary Genomic Data Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories. We presented an algorithm, BicBin, that is able to find biclusters in large-scale sparse binary datasets. While many approaches have been proposed to bicluster gene expression data, only two methods exist to bicluster binary matrices. The first is Bimax (Prelić et al., 2006) , which produces an unmanageable, unprioritized list of biclusters, and cannot detect biclusters that have some elements equal to zero. BicBin is a major improvement over Bimax since it produces a manageable, prioritized list of biclusters as output, and is capable of finding biclusters that are not fully dense with ones. The second is Cmnk, the algorithm proposed in Koyutürk et al. (2004) , which returns biclusters that contain a large number of predicted elements, where most of these predicted elements are not part of the true bicluster. BicBin clearly improves on this algorithm, since it is able to detect biclusters, even in a noisy background with a much lower false positive rate. We extensively demonstrated this on a series of artificial experiments. More importantly, we show that we can find biclusters in a TFBS dataset such as TRANSFAC, in a purely data-driven way. We correct for sequence similarities of the motifs and employ motif merging to ensure that these biclusters could not have been found by simple one-way hierarchical clustering of the sequences. The genes of the biclusters that BicBin detects are enriched for GO categories, and contain both known and novel interacting transcription factors.

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 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


Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study Abstract Deep brain stimulation (DBS) and lesioning are two surgical techniques used in the treatment of advanced Parkinson's disease (PD) in patients whose symptoms are not well controlled by drugs, or who experience dyskinesias as a side effect of medications. Although these treatments have been widely practiced, the mechanisms behind DBS and lesioning are still not well understood. The subthalamic nucleus (STN) and globus pallidus pars interna (GPi) are two common targets for both DBS and lesioning. Previous studies have indicated that DBS not only affects local cells within the target, but also passing axons within neighboring regions. Using a computational model of the basal gangliathalamic network, we studied the relative contributions of activation and silencing of local cells (LCs) and fibers of passage (FOPs) to changes in the accuracy of information transmission through the thalamus (thalamic fidelity), which is correlated with the effectiveness of DBS. Activation of both LCs and FOPs during STN and GPi-DBS were beneficial to the outcome of stimulation. During STN and GPi lesioning, effects of silencing LCs and FOPs were different between the two types of lesioning. For STN lesioning, silencing GPi FOPs mainly contributed to its effectiveness, while silencing only STN LCs did not improve thalamic fidelity. In contrast, silencing both GPi LCs and GPe FOPs during GPi lesioning contributed to improvements in thalamic fidelity. Thus, two distinct mechanisms produced comparable improvements in thalamic function: driving the output of the basal ganglia to produce tonic inhibition and silencing the output of the basal ganglia to produce tonic disinhibition. These results show the importance of considering effects of activating or silencing fibers passing close to the nucleus when deciding upon a target location for DBS or lesioning. Surgical intervention continues to be an option for treatment of patients with advanced Parkinson's disease who do not respond well or have severe side effects from medication. DBS and lesioning can affect both local cells within the target nuclei and fibers of passage from neighboring nuclei, and the relative contributions of local cell and fiber activation and silencing were found to be variable between DBS and lesioning. Activation of both local cells and passing fibers during STN and GPi DBS reduced thalamic transmission errors. During STN lesioning, silencing of GPi fibers contributed to its effectiveness in improving thalamic information transmission. Silencing of STN cells, however, increased bursting of GPe and GPi neurons, resulting in a worsening of the thalamic fidelity. This increase in GP bursting after silencing STN local cells could be a possible mechanism for hemiballism following STN lesioning. Finally, following GPi lesioning, silencing of both local cells and passing GPe fibers contributed to the increase in thalamic fidelity. Our results may provide explanations for the observations that lesioning and selecting active contacts in the dorsal STN and within the central region of the GPi produce the most desired outcomes. Better understanding of the mechanisms behind these surgical techniques could lead to improved surgical treatment options for Parkinson's disease. . .05. Membrane potentials (v) of the STN cells were governed by the equations: We modified and validated a computational model of the basal ganglia-thalamic network, and subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced, respectively. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency (Moro et al. 2002; Timmermann et al. 2004; Fogelson et al. 2005; Birdno and Grill 2008; Eusebio et al. 2008) . The model revealed two distinct mechanisms by which both DBS and lesion led to improvements in thalamic throughput fidelity-either driving the output of the basal ganglia to reduce bursting in the GPi, producing tonic inhibition of the thalamus, consistent with experimental recordings (Hahn et al. 2008) , and the previous computational work of Rubin and Terman (2004) and Hahn and McIntyre (2010) ; or silencing the output of the basal ganglia to produce tonic disinhibition of the thalamus, consistent with the classical view of the "virtual lesion" effects of DBS. The regularized pattern of thalamic inhibition with stimulation or lesion was in strong contrast to the phasic inhibition of the thalamus driven by the GPi bursting activity present in PD, and regular inhibition is correlated with symptom reduction during DBS in MPTP treated monkeys (Guo et al. 2008 ) and persons with PD (Dorval et al. 2010 ).

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A Novel Algorithm for Non-Bonded-List Updating in Molecular Simulations Simulations of molecular systems typically handle interactions within non-bonded pairs. Generating and updating a list of these pairs can be the most time-consuming part of energy calculations for large systems. Thus, efficient non-bonded list processing can speed up the energy calculations significantly. While the asymptotic complexity of current algorithms (namely O(N), where N is the number of particles) is probably the lowest possible, a wide space for optimization is still left. This article offers a heuristic extension to the previously suggested grid based algorithms. We show that, when the average particle movements are slow, simulation time can be reduced considerably. The proposed algorithm has been implemented in the DistanceMatrix class of the molecular modeling package MESHI. MESHI is freely available at <www.cs.bgu.ac.il/∼meshi>. In this work, we present a new algorithm of non-bonded list update. Although similar in spirit to several existing algorithms (most notably to the second option in Glikman et al., 1996) , it is unique in being both robust and very efficient. Similarly to the grid-based methods, its accuracy does not depend on any assumptions about the simulated system. At the same time, it resembles the Verlet table algorithm by taking full advantage of the slow movements of the system. Further, since each particle is updated separately, efficiency is not reduced when some particles are faster than the others. We tested the efficiency of our approach using two systems. The first system (the helium cluster minimization) was chosen for its simplicity, which helped us in the development process. The other system (poly-alanine minimization) was chosen since, while it is still simple and manageable, it is more related to protein structure prediction (Keasar and Levitt, 2003) . Comparing the performance of alternative algorithms is a delicate task. The computation time depends heavily on the available hardware and the details of implementation. To circumvent the hardware problem, we only compared simulations that were done on identical computers. The implementation problem is harder to solve. It may well be that our implementation of a given algorithm is not the optimal one. As a partial remedy, we presented, in addition to simulation time, the number of distance calculations. Distance calculation is required by all pair-wise energy terms that we are aware of. Thus, the time required for the distance calculations is a lower limit on the simulation time. A single distance calculation takes constant time, and thus, the number of distance calculations is a good measure of the algorithm's efficiency. It should be noted that the number of distances below R cut is a lower limit of the number of distance calculations. On both systems and with both efficiency measures, our algorithm was shown to outperform grid-based methods.. Similarly to the original grid-based algorithms, the current method is orthogonal to the Verlet-table and the particle-grouping algorithms. Thus, it can be combined with them to yield an even more efficient algorithm. Preliminary work (data not shown) suggests that the Verlet-table approach, important as it is in the field of MD, may not be useful in our systems. Due to the low numerical stability of the minimization algorithms, wide buffer regions are required. The particle-grouping algorithm, on the other hand, may prove useful.. A Java implementation of the algorithm is freely available as part of the MESHI package (Kalisman et al., 2005) . The new algorithm is implemented in the DistanceMatrix class of the molecular modeling package MESHI (Kalisman et al., 2006) . Within DistanceMatrix, the NBL is represented by a sparse matrix of distances. These distances are updated once during the NBL update. The various energy functions (e.g., Lennard-Jones, solvation, angles, torsions, hydrogen-bonds) acquire the distances they need from this matrix. Thus, each distance between non-bonded particles is calculated at most once per energy evaluation regardless of how many energy terms require it. The same heuristics that allow the efficient update of the NBL also allow a time and space efficient design of the DistanceMatrix.

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Simulation modelling and analysis of a border security system Border control is vital to the security of a nation and its citizens. All countries look at measures to improve the security of their borders. But increasing security can bring a substantial financial burden. In this study, we analyze the border security problem of Turkey using a simulation approach. Our main objective is to find more efficient ways of improving border control and security along Turkey's land borders. To achieve this, we examine the structure of the border security system and its major elements, examine the relationships between performance measures, and assess the effectiveness of security elements on each system performance measure. We also look into the issues of planned changes and additional resources, and we evaluate new alternative system designs. The results of simulation experiments are analyzed by statistical methods. Border control is vital to the security of a nation and its citizens. International terrorism, worldwide illegal immigration and refugee problems, and drug and arms smuggling are of concern to all states. Every country employs some measures to secure its borders. Since today's security systems depend on technology and personnel, efforts to increase border security will result in substantial financial costs. Hence all states try to optimize their resources while remaining effective.. The objective of this study is to identify the possible ways of increasing border control and efficiency of security along Turkey's borders. Specifically, we model the operational activities of a border company supported by a battalion and examine the existing system via simulation. First we analyze the main structure and components of the present system and to assess its effectiveness using the performance measures such as the ratio of illegal infiltrations caught, degree of controllability, and frequency of controlling. Second, we attempt to understand the relationship between security elements and performance measures. Third, we study the effect of each security element on selected performance measures and determine the degree of importance of each security element. Fourth, we identify the factors that significantly affect the performance measures and measure the sensitivity of the system responses to changes in the system environment. Finally, we evaluate new system design alternatives to improve system performance. In all of these stages, our aim is to find possible ways to increase border security in a cost effective manner.. Although the topic is important and vital to the security of a nation, we could not find any studies in the literature that analyzed border security systems. There are several GAO (General Accounting Office is the investigative arm of Congress in US) and CRS (Congressional Research Service) reports related to border control and security. In their CRS report (June 18, 2001 ), William J. Krouse (Analyst in Social Legislation; Domestic Social Policy Division) and Raphael F. Perl (Specialist in International Affairs; Foreign Affairs, Defense, and Trade Division) explain the importance of border security and propose some options to prevent illegal entry into the United States. In GAO reports, some precautions are proposed and evaluated. These include: (1) concentrating personnel and technology resources, first in the sectors having the highest level of illegal infiltration activity and moving to the areas with lesser activity, (2) making maximum use of physical barriers to deter entry along the border, (3) increasing the proportion of time border patrol agents spend on patrol, and (4) identifying the appropriate mix of technology and personnel needed to control the border.. The rest of the paper is organized as follows: In Section 2, we define the problem and its scope. In Section 3, we explain the model development process. In Section 4, we examine the system behavior, interactions between system components and performance measures; and assess the effects of each security element on the selected performance measures. In Section 5, we present the results of the experimental design to identify the significant factors. In Section 6, we compare alternatives using ranking and selection and multi-criteria decisionmaking procedures. Finally, we give concluding remarks and future research directions in Section 7.

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An Unusual 500,000 Bases Long Oscillation of Guanine and Cytosine Content in Human Chromosome 21 An oscillation with a period of around 500 kb in guanine and cytosine content (GC%) is observed in the DNA sequence of human chromosome 21. This oscillation is localized in the rightmost one-eighth region of the chromosome, from 43.5 Mb to 46.5 Mb. Five cycles of oscillation are observed in this region with six GC-rich peaks and five GC-poor valleys.The GC-poor valleys comprise regions with low density of CpG islands and, alternating between the two DNA strands, low gene density regions. Consequently, the long-range oscillation of GC% result in spacing patterns of both CpG island density, and to a lesser Periodicities and characteristic length scales in biological sequences have been of longstanding interest in sequence analysis. The codon structure and the corresponding length scale of three bases in protein coding sequences (Shulman et al., 1981; Fickett, 1982; Staden and McLachlan, 1982) is one of the main features being used for computational gene recognization (see, e.g., Borodovsky et al., 1986; Borodovsky and McIninch, 1993; Burge and Karlin, 1997; Tiwari et al., 1997; Guigo, 1999; Grosse et al., 2000; Li et al., 2002) . by a tendency for certain base types to be at certain codon positions. A correlation at a distance of 10-11 bases was also detected Trifonov and Sussman, 1980; Baldi et al., 1996; Widom, 1996; Tomita et al., 1999) . Two possible explanations have been put forward to explain this (Herzel et al., 1998) . The first explanation is that this periodicity is related to DNA bending and nucleosome formation (Trifonov and Sussman, 1980) . The second explanation is that it is a reflection of a periodicity in protein sequences (Zhurkin, 1981) , because most sequences that exhibit 10-11 base correlations are protein coding sequences (see, however, the result in (Holste et al., 2003) , where a 10-11 base correlation is detected in mostly non-coding human chromosomes 20,21,22 sequences). Proponents of the second explanation also argue that nucleosome formation is only aided by a sequence property on 5-6 bases (Zhurkin, 1981) . Periodicities in protein sequences are seen to be natural, because their presence aids the secondary structure (Shiba et al., 2002) . Besides these two well known length scales in DNA sequences, 3 and 10-11 bases, other length scales, e.g., 120, 200, and 450 bases, were also proposed, based mainly on a theoretical argument on the size of an exon, the size of nucleosome unit, and the size of a typical prokaryote protein (Trifonov, 1998) .. Tandem repeats of DNA segments introduce local or even global sequence periodicities depending on their distribution. If a sequence of k bases tandemly repeats many times, there would be base-base correlations at separations of k, 2k, 3k, . . . bases, e.g., the periodicity of k =2 bases observed in non-coding sequences (Arques, 1987; Konopka et al., 1987) .. If the repeat is not perfect, such as those in many subtelometric sequences, a correlation may not appear at the exact multiples of the basic unit size (Pizzi et al., 1990) . Interspersed repeats in mammalian genomes (Smit, 1999) should in principle not introduce characteristic length scales in correlation patterns because their spatial distribution is not regular (except for length scales shorter than the size of one copy of the repeat). A recent survey of characteristic length scales in many eukaryote genome sequences by spectral analysis reveals peaks in power spectra at about 68, 59 and 94 bases in C. elegans (chromosomes I, II, and III, respectively), at about 248, 167, 126 bases in A. thaliana chromosome 3, at about 174, 88, 59 bases in chromosome 4, at about 356, 174, 88, 59 bases in chromosome 5, and at about 167, 84 bases in H. sapiens chromosomes 21 and 22 (Fukushima et al., 2002) . A connection between these length scales and tandem or interspersed repeats for the three human chromosomes (20, 21, and 22) has been discussed in (Holste et al., 2003) .. At distances much longer than hundreds of bases, it is more difficult to observe a correlation at the level of individual bases. It is, however, easier to observe correlations between base compositions, such as the guanine and cytosine content (GC%). The reason is as follows: instead of requiring a matching base by base at the exact spacing, correlation at the base composition level only requires GC% to be similarly high (or low) at certain range of spacings. In this paper, we report an unusual long-range oscillation of GC% with a periodicity of around 500 kb (1 kb= 10 3 bases) in the DNA sequence of human chromosome 21. This periodicity is longer than any periodicity in DNA sequences detected so far.. 2 The DNA sequence of human chromosome 21 exhibits higher correlations Sequence data were downloaded from the UCSC human genome repository (available at http://genome.ucsc.edu/), for the version of NCBI build-34 release. We evenly partition each human chromosome into N = 2 k non-overlapping windows (e.g., k = 17. and N =131,072). GC% of each window is calculated, forming a GC% series: {x i } (i = 1, 2, . . . N). The correlation function Γ(d) of this series is defined as the Pearson's correlation coefficient of two truncated subseries: a right-hand side truncated {x. , and a left-hand side truncated {x. where the covariance is defined as Cov(. and the variance is defined as Var(x) = (x − x ) 2 ( is the average of the {x} series). Here, the parameter w is used to indicate the fact that the GC% series, and thus detected patterns of correlation, implicitly depends on the window size w. When the spacing d ≪ N, the following approximation formula can be used:. For each human chromosome, Fig.1 shows Γ(d) for both the GC% series derived from 2 17 windows (bottom) and the GC% series derived from 2 15 windows (top). Fig.1 shows that the magnitude of Γ(d) depends on the window size.. We illustrate how the magnitude of the correlation function depends on the window size by plotting in Fig.2 Clay et al., 2001; Li and Holste, 2004) . This "resistance to reduction of variance" is directly related to the 1/f α (α ≈ 1) power spectrum (Clay et al., 2001; Clay et al., 2003; Clay, 2003; Li, 2005) previously observed in DNA sequences (Li and Kaneko, 1992) by the relationship α ≈ 1 − β. 3 An oscillation of 500 kb in the correlation function is located at the rightmost one-eighth of the chromosome There is a striking oscillation in Γ(d) which peaks at about 0.5, 1.1, 1.6, and 2.1 Mb with an approximately constant spacing between peaks of about 500 kb. From Fig.1 , it can be seen this long-range oscillation is solely present in human chromosome 21, but not in other human chromosomes. As discussed above, a sequence periodicity can be caused either by an exact repeat or by a tendency for a particular base to be located in a periodic location.. In either case, to maintain base-level periodicity for such a distance of hundreds of kb or longer requires a selection pressure against insertion and deletion mutations.. To find out whether this 500 kb periodicity is localized in a particular region on chromosome 21, we segment chromosome 21 evenly into eight segments. Because interspersed repeats tend to have higher GC% than the rest of the sequence,. we address the question of whether a regular spatial distribution of the repeat sequences is responsible for the observed 500 kb oscillation. In this paper, we have observed a localized 500 kb long oscillation in GC% of human chromosome 21. We checked whether the region of chromosome 21 with this oscillation has been the focus of investigations in previously large-scale correlation analyses of the human genome, and we found that a segmental duplication of size of 200 kb has been identified on chromosome 21 (Golfier et al., 2003) . However, the region reported in (Golfier et al., 2003) was in the chromosome band 21q22.1, whereas the last one-eighth segment of the human chromosome 21 reported here was in the band 21q22.3.. The 21q22.3 band is both GC-rich and gene-rich, in marked contrast to the 7 Mb GCpoor isochore localized in 21q21. (McKusick, 1998) number 240300), was shown to be linked to markers in 21q22.3 (Aaltonen et al., 1994) .. The linked region is further narrowed down to the autoimmune regulator (AIRE) gene (Aaltonen et al., 1997; Nagamine et al., 1997) , located at the position 44.561-44.574 Mb. Genes on two DNA strands are plotted separately. When known genes on both strands are considered together, there is no visible gaps in their spatial distribution. However, strand-specific gene distribution seems to match the amplitudes of GC% oscillations. We observe that the forward and the reverse strand alternately exhibits comparatively lower local gene densities in GC-poor valleys: 1 and 3 for the reverse strand (−), 2 and 4 for the forward strand (+). Both strands show a lower local gene density in GC-poor valley region 5.. We next investigated the spatial distribution of quantities that are directly related to GC%. vation since one of the criteria for CpG island detection is its GC%. In particular, one of the oftenly used methods for CpG island detection requires GC% to be higher than 50% (Larsen et al., 1992) . The GC-high peaks and GC-poor valleys in Fig.5 are separated by the GC%=50% line, and this may explain why CpG islands are less likely to be found in these GC-poor valleys.. In summary, we have detected a unique long-range oscillation in a localized region in human chromosome 21, which is absent in the remaining human chromosomes. It will be of interests to determine the key biological features either causing or resulting from this ultralong-ranging periodicity in human chromosome 21. While it cannot be excluded that this particular oscillation is due to chance events, one of the promising directions to pursue is its connection to chromatin structure and DNA loops, along the similar line of research on the connection between these structural units and GC% (Saccone et al., 2002; Bernardi, 2004 GC% is calculated at the window size of 358 bases, which is 1/2 17 of the total chromosome length. The distances of 0.5, 1, 1.5, and 2 Mb are marked by long vertical lines, and the spacing of 100 kb is marked by short vertical lines.

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


Seamless Warping of Diffusion Tensor Fields Abstract-To warp diffusion tensor fields accurately, tensors must be reoriented in the space to which the tensors are warped based on both the local deformation field and the orientation of the underlying fibers in the original image. Existing algorithms for warping tensors typically use forward mapping deformations in an attempt to ensure that the local deformations in the warped image remains true to the orientation of the underlying fibers; forward mapping, however, can also create "seams" or gaps and consequently artifacts in the warped image by failing to define accurately the voxels in the template space where the magnitude of the deformation is large (e.g., Jacobian 1). Backward mapping, in contrast, defines voxels in the template space by mapping them back to locations in the original imaging space. Backward mapping allows every voxel in the template space to be defined without the creation of seams, including voxels in which the deformation is extensive. Backward mapping, however, cannot reorient tensors in the template space because information about the directional orientation of fiber tracts is contained in the original, unwarped imaging space only, and backward mapping alone cannot transfer that information to the template space. To combine the advantages of forward and backward mapping, we propose a novel method for the spatial normalization of diffusion tensor (DT) fields that uses a bijection (a bidirectional mapping with one-to-one correspondences between image spaces) to warp DT datasets seamlessly from one imaging space to another. Once the bijection has been achieved and tensors have been correctly relocated to the template space, we can appropriately reorient tensors in the template space using a warping method based on Procrustean estimation. Techniques that have been widely used to normalize scalar images to a template have been difficult to extend to DTI datasets. The greatest impediments to spatial normalization of DTI datasets have been the two-fold challenge of avoiding the introduction of seams (and thereby artifacts) into the template space while reorienting tensors in a way that preserves both their morphology and the integrity of underlying fiber tracts as identified in the original imaging space. Our seamless warping algorithm using bijection combined with Procrustean Estimation has successfully overcome these difficulties. DT datasets contain high-dimensional measurements of diffusivity that must be preserved during the warping procedure because they reflect the local structure of biological tissues. Thus any technique for spatial normalization that is extended from the domain of traditional scalar imaging to DTI likely requires careful and substantial modification to ensure preservation of that biologically relevant information. Indeed, this same cautionary note applies to the extension of scalar techniques to any MRI modality other than anatomical imaging. Future work should develop similar valid and robust algorithms for the processing and analysis of data from multiple MRI modalities within a spatially normalized template. We have proposed a method for warping DT datasets that uses a bijection DF along with Procrustean Estimation to reorient tensors appropriately in a template imaging space. By combining forward and backward mapping under the constraint of consistency (see Section III-A), our method achieved seamless warping and proper geometric reorientation of tensors in the warped template space using information about the local deformation and geometric orientation of tensors in the original imaging space. Moreover, our method generated reverse deformation fields with a high degree of accuracy. We demonstrated that the bijection map generated using this algorithm is a consistent one-to-one map (as defined quantitatively in Section III), having an average error of less than 0.1 mm between the forward and backward mapping components of the bijection. We also found that using a level of detail of 2 in super-sampling is optimal in terms of the cost-benefit trade-off of accuracy versus computational demands, in that more extensive super-sampling provides only slightly better mapping accuracy but with exponentially increasing computational demands. Nevertheless, in areas of extreme deformation, a higher level of super-sampling may be needed to achieve better accuracy, and in these instances, Fig. 10 . Averaged DTI dataset from 13 subjects using bijection warping. (a) PD colormap (using the same color schema as in Fig. 7 ) of a single axial slice in the averaged tensor map. Blue fiber bundles (arrows) are exquisitely sharp, allowing easy fiber tracking. Fibers on the left (L-Fiber) or right (R-Fiber) side of the image are tracked in (c) using these two blue strips as seed points. (b) Close-up comparison of the blue strips in (a) for the averaged results using a warping based on forward mapping (upper panel) and a warping based on bijection mapping (lower panel). Although the two views look similar, subtle differences can be found: the blue strips at the arrows in the upper panel are thicker and fuzzier compared with the same blue strips in the lower panel, representing a less distinct delineation of fiber bundles; in addition, the boundary of the reddish region in the yellow dotted loop is sharper in the lower panel than is the same region in the upper panel, again showing a clearer delineation of tissue organization. Both indicate that the bijection warping method preserves signal better than forward mapping does. (c) Tracked fibers are embedded in a triplanar view of the color map of average PDs in the template space. Note that the colors of the two reconstructed fiber bundles (red and green) do not carry directional information, but are used purely for the purposes of identifying the two fiber bundles. This is a left dorsal anterior perspective (viewed from the left, above the forehead). The reconstructed fiber bundles track to the dorsum of the brain in the region of premotor and sensorimotor cortices. CC=corpus callosum; L-Fiber=fiber tracts in the left hemisphere; R-Fiber = fiber tracts in the right hemisphere.. adaptive super-sampling that is based on the magnitude of deformation may be desirable for generating a more accurate bijection mapping. Finally, although throughout this paper we have presumed that the bijection is initiated using a forward mapping, the bijection can be initiated with a backward mapping as well.. Using FA measurements as indices of tensor morphology, we have demonstrated that seamless warping of DT datasets using bijection robustly preserves tensor morphology. In contrast, we also demonstrated quantitatively that when using forward mapping, the SNR of DT datasets decreased significantly at locations of extreme deformation , thereby creating seams and consequently artifacts (Fig. 9) .. We used Procrustean Estimation to estimate tensor rotation instead of directly averaging their PDs. Although Procrustean Estimation may appear more complex than other methods for estimating rotation, it is actually quite elegant mathematically and computationally efficient. By comparison, averaging tensor orientations based on their PDs to estimate tensor rotation is much more complicated, because many more orientations are considered within a given neighborhood. This is the case because both of the eigenvectors and represent the same orientation, and thus both and are reasonable and meaningful average orientations of and . Procrustean Estimation, on the other hand, essentially calculates the rotation that minimizes, in terms of least-squares, the differences of corresponding coordinate pairs, and these differences are rotation-invariant. Therefore, whereas the coordinates entered in matrixes and (in algorithm ERM) are relative positions derived from PD vectors, Procrustean Estimation is free from the dilemma of interpolating orientations.. A central and long standing issue in the processing of DT datasets has been the technical difficulty of achieving an accurate normalization of images from this modality alone. Normalization of DT datasets before generating FA maps, fiber tracts, PD color maps, and other measurements derived from DT data is more efficient than performing normalization on these measurements after these measures are generated because, without an initial normalization, each set of measurements must be normalized individually to the template space. In contrast, if all DT datasets are first normalized to a template space, all of these measurements can then be derived directly in the template space and therefore do not require spatial normalization individually. Thus, normalizing DT datasets first, before generating measurements derived from DT data, drastically reduces the number of times that normalization must be performed (as well as eliminating the duplication of data and need for additional storage space that are required in order to normalize each set of measurements separately).. Despite these considerable advantages of spatial normalization prior to calculation of derivative measures, investigators sometimes choose the less efficient of these two options because of the known technical difficulty of accurately normalizing datasets that contain high-dimensional information. Indeed a common practice is to normalize each set of derived measures (e.g., FA maps, fiber tracts) without normalizing the DT datasets themselves (although even this approach is challenging, in that the derived measurements may also contain high-dimensional information that makes them difficult to normalize). Our algorithm addresses these various problems and challenges by providing a means for normalizing DT datasets more accurately prior to calculation of derivative measures.. Our seamless warping procedure in principle makes possible the accurate interpolation of tensors because of its use of bijection (see Section III-D). In general, interpolating DWI data and then calculating a new tensor based on those data is the most precise way to interpolate a tensor. However, because this method requires the use of all raw DW data for interpolating tensors, raw DW data must either be saved or recovered, the latter being com-putationally demanding (e.g., the Stejskal equation [34] can be applied in reverse to tensors, the calculated baseline image, and gradient directions in order to recover DWI data, similar to our approach in the Procedure SN, as described in Section IV-C). Moreover, conventional DTI warping typically uses forward mapping procedures that require interpolation of tensors in the target space. DWI data, however, are usually not readily available for interpolation in the target space, the methods for interpolating tensors directly in the target space are computationally demanding, and their accuracy is questionable. Our method for seamless warping, in contrast, accurately interpolates tensors in the original space because the backward mapping component in the bijection allows convenient access to the raw DWI data in the original imaging space, thereby allowing for the efficient and accurate interpolation of DWI data.. In our experiments, we used trilinear interpolation of tensor components for tensor interpolation, because we found that the slight gain in accuracy obtained from interpolating DWI data in the original space did not justify its additional expense of data management and computation. Use of trilinear interpolation is justified because DTI data usually do not change significantly within a small neighborhood (particularly not within the distance of the single voxel over which we interpolated). Linear interpolation of tensors thus provides a very nice and efficient approximation of the true values of tensors reconstructed from interpolated DWI data, and for these reasons it is the method most commonly used for tensor interpolation.

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


Improving the Transfer of Near Infrared Prediction Models by Orthogonal Methods Eight calibration transfer methods based on the removal of orthogonal signal were compared for the standardization of whole soybean protein and oil models. Dynamic orthogonal projection (DOP), transfer by orthogonal projection (TOP), error removal by orthogonal subtraction (EROS), orthogonal signal correction (OSC), and orthogonal projections to latent structures (O-PLS) as well as the modification and extension of some of these methods were compared in the transfer of models in intra and inter brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units

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


Social signals: from theory to applications Abstract The Special Issue Editorial introduces the research milieu in which Social Signal Processing originates, by merging computer scientists and social scientists and giving rise to this field in parallel with Human-Computer Interaction, Affective Computing, and Embodied Conversational Agents, all similarly characterized by high interdisciplinarity, stress on multimodality of communication, and the continuous loop from theory to simulation and application. Some frameworks of the cognitive and social processes underlying social signals are identified as reference points (Theory of Mind and Intersubjectivity, mirror neurons, and the ontogenesis and phylogenesis of communication), while three dichotomies (automatic vs. controlled, individualistic vs. intersubjective, and meaning vs. influence) are singled out as leads to navigate within the theoretical and applicative studies presented in the Special Issue.

B I 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 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


INSTANCE

MIGRATION

BETWEEN

ONTOLOGIES

HAVING

STRUCTURAL

DIFFERENCES

Ontology

instance

migration

is

one

of

the

complex

and

not

fully

solved

problems

in

knowledge

management.

A

solution

is

required

when

the

ontology

schema

evolves

in

the

life

cycle

and

the

assertions

have

to

be

transferred

to

the

newer

version.

The

problem

may

become

more

complex

in

distributed

settings

when,

for

example,

several

autonomous

software

entities

use

and

exchange

partial

assertional

knowledge

in

a

domain

that

is

formalized

by

different

though

semantically

overlapping

descriptive

theories.

Such

an

exchange

is

essentially

the

migration

of

the

assertional

part

of

an

ontology

to

other

ontologies

belonging

to

or

used

by

different

entities.

The

paper

presents

our

method

and

tool

for

migrating

instances

between

the

ontologies

that

have

structurally

different

but

semantically

overlapping

schemas.

The

approach

is

based

on

the

use

of

the

manually

coded

transformation

rules

describing

the

changes

between

the

input

and

the

output

ontologies.

The

tool

is

implemented

as

a

plug-in

for

the

ProjectNavigator

prototype

software

framework.

The

article

also

reports

the

results

of

our

three

evaluation

experiments.

In

these

experiments

we

evaluated

the

degree

of

complexity

in

the

structural

changes

to

which

our

approach

remains

valid.

We

also

chose

the

ontology

sets

in

one

of

the

experiments

to

make

the

results

comparable

with

the

ontology

alignment

software.

Finally

we

checked

how

well

our

approach

scales

with

the

increase

of

the

quantity

of

the

migrated

ontology

instances

to

the

numbers

that

are

characteristic

to

industrial

ontologies.

In

our

opinion

the

evaluation

results

are

satisfactory

and

suggest

some

directions

for

the

future

work.

Instance

migration

is

an

important

phase

in

ontology

engineering

and

management

activities.

A

large

number

of

ontologies

describing

similar

domains

from

different

Maxim

Davidovsky,

Vadim

Ermolayev,

Vyacheslav

Tolok

viewpoints

have

been

developed

to

date.

Therefore

an

effective

re-use

of

their

assertional

knowledge

is

rational.

Ontology

instance

re-use

is

also

essential

in

ontology

evolution.

When

a

new

ontology

version

is

developed

it

is

often

necessary

to

transfer

the

instances

of

the

previous

version(s)

to

the

newer

version.

The

development

of

a

newer

ontology

version

starts

with

the

implementation

of

the

required

changes

in

its

TBox.

Therefore

the

reuse

of

the

ABox

could

be

ensured

if

the

elements

of

the

ABox

are

accordingly

transformed.

Assertional

parts

of

ontologies

can

contain

a

large

quantity

of

instances

that

in

turn

makes

manual

transformations

and

instance

transfer

a

laborous

task..

The

article

presents

our

approach

to

semi-automated

instance

migration

based

on

the

use

of

the

formal

rule

patterns

describing

transformations

required

for

transferring

instances

from

the

source

ontology

to

a

target

one.

The

approach

is

implemented

in

our

software

prototype

that

has

been

developed

as

a

plug-in

for

the

Project

Navigator

(PN)

Framework

for

carrying

out

instance

migration

between

the

versions

of

PSI

a

ontologies

PSI

project

developed

the

methodology

and

toolset

for

assessing,

predicting,

and

optimizing

the

performance

of

engineering

design

systems

in

microelectronics

and

integrated

circuits

(MIC)

A

multi-agent

system

(MAS)

for

holonic

simulation

of

an

engineering

design

system

in

the

domain

of

MIC

design

is

developed

as

a

part

of

the

PN

Framework

The

MAS

assists

in

analyzing

and

assessing

the

performance

of

a

design

system

as

a

tool

for

simulation

experiments..

The

article

also

reports

on

the

evaluation

of

our

methodology

and

the

prototype

software

tool

for

iterative

semi-automated

instance

migration

between

the

versions

of

industrial

ontologies

with

evolving

schemas,

between

distributed

ontologies

of

moderate

size

with

overlapping

or

partially

matching

schemas,

and

between

ontologies

of

industrial

size

in

the

same

domain

having

different

schemas.

The

evaluation

experiments

were

set

up

in

line

with

these

three

use

cases.

The

first

experiment

evaluates

the

quality

of

instance

migration

between

the

versions

of

the

PSI

Suite

of

Ontologies.

PSI

ontologies

are

used

by

cooperating

software

agents

that

simulate

planning

and

execution

of

dynamic

engineering

design

processes

in

industrial

MIC

design

domain.

This

use

case

is

tailored

to

support

the

evolution

of

the

PSI

Suite

of

Ontologies

which

assertional

part

is

used

as

the

knowledge

of

a

multi-agent

system

of

cooperative

software

agents

-in

distributed

settings.

The

aim

of

the

evaluation

experiment

based

on

the

second

use

case

is

generating

reproducible

migration

quality

measurements

for

the

publicly

available

set

of

ontologies.

For

that

the

benchmark

set

of

ontologies

of

the

Ontology

Alignment

Evaluation

Initiative

-(OAEI)

Campaign

b

has

been

used.

The

majority

of

these

ontologies

are

artificially

built

using

the

common

parent

ontology

by

injecting

different

sorts

of

changes.

By

that

a

distributed

collection

of

similar

ontologies

describing

the

same

domain

is

modeled.

The

objective

of

our

third

evaluation

experiment

was

to

measure

the

performance

of

our

tool

for

assessing

its

applicability

to

the

industrially

large

volumes

of

instances.

We

have

chosen

two

industrial

ontologies

with

overlapping

schemas

that

are

both

based

on

the

When

ontologies

evolve

the

structural

changes

are

reflected

in

the

ontology

schemas.

A

new

schema

version

is

developed

by

implementing

these

changes.

As

a

rule

structural

changes

are

carried

out

manually

using

various

ontology

engineering

methodologies

and

tools

The

next

step

of

the

development

of

the

newer

ontology

version

is

transferring

the

instances

from

the

older

set

of

ontology

individuals

(assertions)

to

the

newer

one.

Doing

this

manually

proves

to

be

very

laborious.

The

required

effort

appreciably

increases

with

the

number

of

transferable

assertions.

Furthermore

it

can

be

error

prone..

In

part

of

the

assertions

has

been

mined

from

the

design

project

logs

in

the

ACTIVE

e

project.

The

set

of

assertions

describing

a

moderately

complex

project

in

MIC

design

may

comprise

dozens

of

thousands

instances.

Hence,

performing

a

transition

of

the

knowledgebase

to

a

newer

version

of

the

ontology

has

become

a

challenging

problem

because

of

the

required

effort

and

the

sensitivity

to

the

possible

transformation

errors.

A

tool

for

assisting

a

knowledge

engineer

in

migrating

the

assertions

from

the

older

ontology

version

to

the

newer

one

has

therefore

been

demanded..

Very

similarly,

if

distributed

ontologies

are

populated

based

on

the

assertions

taken

from

the

other

ontologies

in

the

same

domain

f

the

patterns

apply

for

transforming

the

instances

according

to

the

structural

differences.

However,

in

this

case

manual

transformation

is

even

more

inappropriate

than

in

the

previous

case

of

ontology

evolution.

It

often

has

to

happen

at

run

time

and

between

the

software

entities

that

own

respective

distributed

knowledge

representations.

Luckily,

different

distributed

variants

of

the

domain

ontology

may

be

regarded

as

different

versions

of

the

same

ontology

that

differ

by

the

set

of

structural

changes

and

their

assertional

parts.

One

of

the

experimental

cases

in

our

research

deals

with

the

migration

of

instances

between

the

GoodRelationscompliant

(see

also

industrial

ontologies.

This

case

is

of

particular

interest

because:

(i)

the

ontologies

are

the

collections

of

assertions

describing

real

world

industrial

domain;

and

(ii)

the

ontologies

are

big

enough

to

assess

the

efficiency

and

performance

of

the

prototype

tool..

Our

objective

in

the

research

reported

in

this

article

was

to

develop

and

evaluate

the

prototype

software

tool

for

semi-automated

instance

migration

from

an

older

version

to

a

newer

version

of

a

set

of

ontology

modules.

As

explained

above,

the

tool

may

also

be

used

for

merging

distributed

ontologies

or

exchanging

assertions

among

these

for

mutual

enrichment

or

harmonization.

The

predecessor

work

to

the

research

presented

in

this

article

has

been

done

by

Vladimirov

et

al.

Current

research

and

development

further

extend

and

refine

these

results.

As

opposed

to

our

current

approach

the

predecessor

work

is

based

on

the

use

of

an

ontology

instance

migration

scenario

driving

the

migration

process

and

encoded

as

a

Python

script.

A

more

comprehensive

survey

of

alternative

approaches

to

ontology

instance

migration

is

given

in

our

technical

report

A

good

survey

of

the

related

work

has

been

done

by

Serafini

and

Tamilin

In

addition

to

their

overview,

more

frameworks

for

ontology

evolution

and

ontology

versioning

need

to

be

mentioned

-for

example

the

results

published

by

Maedche

et

al.

Klein

et

al.

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


Using Case-Based Reasoning and Principled Negotiation to provide decision support for dispute resolution The growing use of Information Technology in the commercial arena leads to an urgent need to find alternatives to traditional dispute resolution. New tools from fields such as artificial intelligence (AI) should be considered in the process of developing novel online dispute resolution (ODR) platforms, in order to make the ligation process simpler, faster and conform with the new virtual environments. In this work, we describe UMCourt, a project built around two sub-fields of AI research: Multi-agent Systems and Case-Based Reasoning, aimed at fostering the development of tools for ODR. This is then used to accomplish several objectives, from suggesting solutions to new disputes based on the observation of past similar disputes, to the improvement of the negotiation and mediation processes that may follow. The main objective of this work is to develop autonomous tools that can increase the effectiveness of the dispute resolution processes, namely by increasing the amount of meaningful information that is available for the parties. Let us now describe the main results of the work presented in this paper. UMCourt is being tested and assessed in a setting with the characteristics that follow. Given the complexity of the Portuguese labor law, for test purposes we are dealing with a restricted set of norms.. In that sense, we are considering a group of 36 norms of the DL 7/2009, from February Fig. 12 The process of guiding a negotiation coordinated by the blackboard Fig. 13 A description of the database detailing which aspects of the Portuguese labor law are addressed by the cases and in which proportion. Article 118 concerns functions performed by the employee; Article 117 is about the effects of the lack of professional title; Article 129 concerns employee's rights; Article 128 depicts employee's obligations; and Article 126 concerns the general obligations of the parties 12, 2009 . These norms were selected because they are generally present in most of the labor-related disputes and address the following domains: (1) functions performed by the employee; (2) effects of the lack of professional title; (3) employee's rights; (4) employee's obligations and (5) general obligations of the parties. The database used in these tests contains a total of 127 indexed cases (Fig. 13) . The representation of these cases in vectors of binary entries was generated previously by the system and was also made available, in the form of a file. Proving the validity and relevancy of this research field, a group of interesting projects with the objective of giving birth to dispute resolution mechanisms can be pointed out. In this section, we describe some of the projects which intersect with this work.. Rule-based legal decision-making system (LDS) dates from 1980 and was one of the first negotiation support systems to be developed [46] . The domain of this system was liability law. This field of law holds responsible product distributors and manufacturers for the injuries their products may cause. The system created embodied the skills and knowledge of a human expert in the shape of antecedent-consequent rules. The project had the objective of formalizing the decision-making processes of attorneys and claims adjusters involved in product liability litigation in the shape of rule-based models so that the effects that changes in legal doctrine have in settlement strategies and practices could be studied. The authors formalized the strictliability concept on ROSIE language, so that the defendant could or could not be considered liable.. A more recent project focusing on providing support for decisions is EXPERTIUS. This is a decision support system that advices Mexican novice judges and clerks upon the determination of whether the plaintiff is or is not eligible for granting him/her a financial pension (on the basis of the "feeding obligation") and if so upon the determination of the amount of that pension [11] . The system has three main modules: a tutorial module, an inferential module and a financial module. The tutorial module guides the user through the accomplishment of several tasks. The inferential module evaluates evidence based on weights that the user assigns to each piece of evidence. It determines which prepositions are defeated and which prevail. At last, the financial module assists the user on the calculus of the value of pensions according to some criteria.. For reasoning in these terms, EXPERTIUS has an extensive way of representing the knowledge about the several parameters. Judicial expert knowledge was represented as having three interrelated levels: one for representing the expert knowledge, one for representing the decisions internal to each procedural stage as regulated by procedural law and a third one that corresponds to the dialogical confrontation pattern of the case that arises simultaneously to the decisions taken in the intermediate level.. The knowledge embodied in the system was divided into five layers. The formal doctrine contained rules from the legislation and common law. The informal principles contained rules that are not explicitly expressed in the law but are generally agreed upon by legal practitioners. Under the strategies layer, the authors coded the methods used by legal practitioners to accomplish a given goal. The subjective considerations layer contains rules that anticipate the subjective responses of people involved in legal interactions. At last, the secondary effects layer contained rules that describe the interactions between rules. The authors concluded that despite the number of rules needed for formalizing the law and the strategies, the rule-based model was feasible and suited for this particular domain.. In the field of negotiation support, a major work is the Family_Winner project, which integrates game theory and heuristics in the field of family law, more specifically in disputes arising from divorce processes [53] . The research is largely based on the Adjusted Winner algorithm [53] , an algorithm that merges insights from the fields of game and decision theory.. This algorithm aims at the distribution of a group of items or issues by the parties, according to a mechanism of point allocation. In this process, each party quantifies how much he/she wants the item and whoever values more a given item receives it.. Still in the negotiation support, another project worth mentioning is SmartSettle [41, 43] . It is an online negotiation system that can be described as a generic tool for decision-makers. It is intended for parties with conflicting objectives that wish to reach a formal agreement. For achieving it, tools that help to define the interests and possible trade-offs are used, with an emphasis on recognizing the parties' satisfaction in the search for optimal global solutions. This platform can be used to solve problems relating to family, insurance, real estate, labor management, contract negotiations, among others.. When comparing the work presented in this paper with the related work mentioned in this section, the main difference that can be noted is that, contrary to the most common approach, we do not aim to provide support for dispute resolution in court. We rather aim for providing valuable information for the parties and eventually the neutrals, so that better and more informed decisions can be taken. While doing so, we also expect disputing parties from going into court, by making them aware of the possible consequences, fostering cooperative off-court processes. Nevertheless, the tool presented can still be used throughout a complete dispute resolution process, ranging from the initial providing and compilation of information to the definition of an outcome. Another major difference is that in this work, there is a close integration between the phase of generating information and the phase of actual dispute resolution through traditional means. That is, the information that is compiled to inform the disputing parties is also used to support and help define the mediation or negotiation process that may follow.

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