This book was conceived as a result of many years research with students
and postdocs in molecular simulation, and shaped over several courses on
the subject given at the University of Groningen, the Eidgen¨ossische Technische
Hochschule (ETH) in Z¨urich, the University of Cambridge, UK, the
University of Rome (La Sapienza), and the University of North Carolina
at Chapel Hill, NC, USA.
This paper explores techniques to take advantage of the fundamental difference in structure between hidden Markov models (HMM) and hierarchical hidden Markov models (HHMM). The HHMM structure allows repeated parts of the model to be merged together. A merged model takes advantage of the recurring patterns within the hierarchy, and the clusters that exist in some sequences of observations, in order to increase the extraction accuracy.
To explain the bias in favor of domestic securities known as the “international diversification puzzle,” the
literature has considered many possible irritants of capital movements across national boundaries but the
results remain inconclusive. This paper demonstrates that this complex multivariate problem can be
addressed within an analytic hierarchy process (AHP)-driv en expert system.
[ Team LiB ] 9.5 Useful System Tasks In this section, we discuss the system tasks that are useful for a variety of purposes in Verilog. We discuss system tasks  for file output, displaying hierarchy, strobing, random number generation, memory initialization,
We investigate the relevance of hierarchical topic models to represent the content of Web gists. We focus our attention on DMOZ, a popular Web directory, and propose two algorithms to infer such a model from its manually-curated hierarchy of categories. Our ﬁrst approach, based on information-theoretic grounds, uses an algorithm similar to recursive feature selection. Our second approach is fully Bayesian and derived from the more general model, hierarchical LDA.
In this paper, we encode topic dependencies in hierarchical multi-label Text Categorization (TC) by means of rerankers. We represent reranking hypotheses with several innovative kernels considering both the structure of the hierarchy and the probability of nodes. Additionally, to better investigate the role of category relationships, we consider two interesting cases: (i) traditional schemes in which node-fathers include all the documents of their child-categories; and (ii) more general schemes, in which children can include documents not belonging to their fathers. ...
We present a novel hierarchical prior structure for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language data sets. The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research.
This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a linear discriminative function is determined in a topdown way using a perceptron algorithm with the lower-level weight vector derived from the upper-level weight vector.
One of the promising approaches to analyzing taskoriented dialogues has involved modeling the plans of the speakers in the task domain. In general, these models work well as long as the topic follows the task structure closely, but they have difficulty in accounting for clarification subdialogues and topic change. We have developed a model based on a hierarchy of plans and metaplans that accounts for the clarification subdialogues while maintaining the advantages of the plan-based approach.
A set of labeled classes of instances is extracted from text and linked into an existing conceptual hierarchy. Besides a significant increase in the coverage of the class labels assigned to individual instances, the resulting resource of labeled classes is more effective than similar data derived from the manually-created Wikipedia, in the task of attribute extraction over conceptual hierarchies.
I am an admitted object-oriented fanatic. I have been designing and implementing object-oriented
software for more than twenty years. When I started designing and implementing object-oriented
, I encountered many detractors. They would say things like “The object model isn’t
complete,” “You can’t have public variables,” “The development environment doesn’t work well
with objects,” “Objects and vector operations don’t mix,” “Object-oriented code is too hard to
debug,” and “MATLAB objects are too slow.
This edition of Fundamentals of Computer Graphics adds four new contributed
chapters and contains substantial reorganizations and improvements to the core
material. The new chapters add coverage of implicit modeling and of two important
graphics applications: games and information visualization. The fourth new
contributed chapter is a major upgrade to the material on color science. As with
the chapters added in the second edition, we have chosen the contributors both for
their expertise and for their clear way of expressing ideas....
In this paper we develop a contingent valuation model for zero-coupon bonds with de-
fault. In order to emphasize the role of maturity time and place of the lenders claim in
the hierarchy of debt of a Þrm, we consider a Þrm that issues two bonds with different ma-
turities and different seniorage. The model allows us to analyze the implications of both
debt renegotiation and capital structure of a Þrm on the prices of bonds.
In the Western world, the arrival in the 20th century of the Welfare State meant that the basic
needs of citizens in terms of health, hygiene and socio-economic considerations were met to
a greater extent than ever before. It soon however became apparent that, as Maslow’s
hierarchy of need predicts (Maslow, 1943), people continued to want more, they needed
choices, and they sought opportunities to fulfil ambitions and goals.
Any new driver may be required to take a driving exam in a vehicle representing the
same size and weight classification as the driver’s license for which they are apply-
ing. All drivers must take a written exam every eight years, except those having no
traffic convictions. New drivers age 75 and older and any driver turning 75 or older
who is renewing his/her driver’s license must take a driving exam in a representative
Novice users engaged in task-oriented dialogues with an adviser to learn how to use an unfamiliar statistical package. The users', task was analyzed and a task structure was derived. The task structure was used to segment the dialogue into subdialogues associated with the subtasks of the overall task. The representation of the dialogue structure into a hierarchy of subdialogues, partly corresponding to the task structure, was validated by three converging analyses.
Whether automatically extracted or human generated, open-domain factual knowledge is often available in the form of semantic annotations (e.g., composed-by) that take one or more speciﬁc instances (e.g., rhapsody in blue, george gershwin) as their arguments. This paper introduces a method for converting ﬂat sets of instance-level annotations into hierarchically organized, concept-level annotations, which capture not only the broad semantics of the desired arguments (e.g., ‘People’ rather than ‘Locations’), but also the correct level of generality (e.g.
We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-ofart systems.
We specify an algorithm that builds up a hierarchy of referential discourse segments from local centering data. The spatial extension and nesting of these discourse segments constrain the reachability of potential antecedents of an anaphoric expression beyond the local level of adjacent center pairs. Thus, the centering model is scaled up to the level of the global referential structure of discourse. An empirical evaluation of the algorithm is supplied.
This paper deals with the reference choices involved in the generation of argumentative text. Since a natual segmentation of discourse into attentional spaces is needed to carry out this task, this paper first proposes an architecture for natural language generation that combines hierarchical planning and focus-guided navigation, a work in its own right. While hierarchical planning spans out an attentional hierarchy of the discourse produced, local navigation fills details into the primitive discourse spaces.