bluuebunny/crossref_metadata_2025 huggingface dataset:
Solving Set Constraint Satisfaction Problems using ROBDDs
Authors: Hawkins P. J. , Lagoon V. , Stuckey P. J.
Published: July 2018

In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems.
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis
Authors: Cimiano P. , Hotho A. , Staab S.
Published: July 2018

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
Perseus: Randomized Point-based Value Iteration for POMDPs
Authors: Spaan M. T.J. , Vlassis N.
Published: July 2018

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent's belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems.
Optiplan: Unifying IP-based and Graph-based Planning
Authors: Van den Briel M.H.L. , Kambhampati S.
Published: July 2018

The Optiplan planning system is the first integer programming-based planner that successfully participated in the international planning competition. This engineering note describes the architecture of Optiplan and provides the integer programming formulation that enabled it to perform reasonably well in the competition. We also touch upon some recent developments that make integer programming encodings significantly more competitive.
Binary Encodings of Non-binary Constraint Satisfaction Problems: Algorithms and Experimental Results
Authors: Samaras N. , Stergiou K.
Published: July 2018

A non-binary Constraint Satisfaction Problem (CSP) can be solved directly using extended versions of binary techniques. Alternatively, the non-binary problem can be translated into an equivalent binary one. In this case, it is generally accepted that the translated problem can be solved by applying well-established techniques for binary CSPs. In this paper we evaluate the applicability of the latter approach. We demonstrate that the use of standard techniques for binary CSPs in the encodings of non-binary problems is problematic and results in models that are very rarely competitive with the non-binary representation. To overcome this, we propose specialized arc consistency and search algorithms for binary encodings, and we evaluate them theoretically and empirically. We consider three binary representations; the hidden variable encoding, the dual encoding, and the double encoding. Theoretical and empirical results show that, for certain classes of non-binary constraints, binary encodings are a competitive option, and in many cases, a better one than the non-binary representation.
Domain Adaptation for Statistical Classifiers
Authors: Daume III H. , Marcu D.
Published: July 2018

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
How the Landscape of Random Job Shop Scheduling Instances Depends on the Ratio of Jobs to Machines
Authors: Streeter M. J. , Smith S. F.
Published: July 2018

We characterize the search landscape of random instances of the job shop scheduling problem (JSP). Specifically, we investigate how the expected values of (1) backbone size, (2) distance between near-optimal schedules, and (3) makespan of random schedules vary as a function of the job to machine ratio (N/M). For the limiting cases N/M approaches 0 and N/M approaches infinity we provide analytical results, while for intermediate values of N/M we perform experiments. We prove that as N/M approaches 0, backbone size approaches 100%, while as N/M approaches infinity the backbone vanishes. In the process we show that as N/M approaches 0 (resp. N/M approaches infinity), simple priority rules almost surely generate an optimal schedule, providing theoretical evidence of an "easy-hard-easy" pattern of typical-case instance difficulty in job shop scheduling. We also draw connections between our theoretical results and the "big valley" picture of JSP landscapes.
Resource Allocation Among Agents with MDP-Induced Preferences
Authors: Dolgov D. A. , Durfee E. H.
Published: July 2018

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size.
Learning Symbolic Models of Stochastic Domains
Authors: Pasula H. M. , Zettlemoyer L. S. , Kaelbling L. P.
Published: July 2018

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.
Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal Periodic Sets
Authors: Bettini C. , Mascetti S. , Wang X. S.
Published: July 2018

In the recent years several research efforts have focused on the concept of time granularity and its applications. A first stream of research investigated the mathematical models behind the notion of granularity and the algorithms to manage temporal data based on those models. A second stream of research investigated symbolic formalisms providing a set of algebraic operators to define granularities in a compact and compositional way. However, only very limited manipulation algorithms have been proposed to operate directly on the algebraic representation making it unsuitable to use the symbolic formalisms in applications that need manipulation of granularities.

This paper aims at filling the gap between the results from these two streams of research, by providing an efficient conversion from the algebraic representation to the equivalent low-level representation based on the mathematical models. In addition, the conversion returns a minimal representation in terms of period length. Our results have a major
practical impact: users can more easily define arbitrary granularities in terms of algebraic operators, and then access granularity reasoning and other services operating efficiently on the equivalent, minimal low-level representation. As an example, we illustrate the application to temporal constraint reasoning with multiple granularities.

From a technical point of view, we propose an hybrid algorithm that interleaves the conversion of calendar subexpressions into periodical sets with the minimization of the period length. The algorithm returns set-based granularity representations having minimal period length, which is the most relevant parameter for the performance of the considered reasoning services. Extensive experimental work supports the techniques used in the algorithm, and shows the efficiency and effectiveness of the algorithm.
NP Animacy Identification for Anaphora Resolution
Authors: Orasan C. , Evans R. J.
Published: July 2018

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.
Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting
Authors: Domshlak C. , Hoffmann J.
Published: July 2018

We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FF's techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the search space and the heuristic function. Our evaluation of Probabilistic-FF shows its fine scalability in a range of probabilistic domains, constituting a several orders of magnitude improvement over previous results in this area. We use a problematic case to point out the main open issue to be addressed by further research.
Graph Abstraction in Real-time Heuristic Search
Authors: Bulitko V. , Sturtevant N. , Lu J. , Yau T.
Published: July 2018

Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be independent of the problem size. Such agents plan only a few actions at a time in a local search space and avoid getting trapped in local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically-built state abstraction and prove completeness and convergence of the resulting family of algorithms. We then analyze the impact of abstraction in an extensive empirical study in real-time pathfinding. Abstraction is found to improve efficiency by providing better trading offs between planning time, learning speed and other negatively correlated performance measures.
Chain: A Dynamic Double Auction Framework for Matching Patient Agents
Authors: Bredin J. L. , Parkes D. C. , Duong Q.
Published: July 2018

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with ``zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.
On the Semantics of Logic Programs with Preferences
Authors: Greco S. , Trubitsyna I. , Zumpano E.
Published: July 2018

This work is a contribution to prioritized reasoning in logic programming in the presence of preference relations involving atoms. The technique, providing a new interpretation for prioritized logic programs, is inspired by the semantics of Prioritized Logic Programming and enriched with the use of structural information of preference of Answer Set Optimization Programming. Specifically, the analysis of the logic program is carried out together with the analysis of preferences in order to determine the choice order and the sets of comparable models. The new semantics is compared with other approaches known in the literature and complexity analysis is also performed, showing that, with respect to other similar approaches previously proposed, the complexity of computing preferred stable models does not increase.
The Ariadne's Clew Algorithm
Authors: Mazer E. , Ahuactzin J. M. , Bessiere P.
Published: July 2018

We present a new approach to path planning, called the ``Ariadne's clew algorithm''. It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments --- ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved manner. EXPLORE builds a representation of the accessible space while SEARCH looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.
The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem
Authors: Vandegriend B. , Culberson J.
Published: July 2018

Using an improved backtrack algorithm with sophisticated pruning techniques, we revise previous observations correlating a high frequency of hard to solve Hamiltonian Cycle instances with the Gn,m phase transition between Hamiltonicity and non-Hamiltonicity. Instead all tested graphs of 100 to 1500 vertices are easily solved. When we artificially restrict the degree sequence with a bounded maximum degree, although there is some increase in difficulty, the frequency of hard graphs is still low. When we consider more regular graphs based on a generalization of knight's tours, we observe frequent instances of really hard graphs, but on these the average degree is bounded by a constant. We design a set of graphs with a feature our algorithm is unable to detect and so are very hard for our algorithm, but in these we can vary the average degree from O(1) to O(n). We have so far found no class of graphs correlated with the Gn,m phase transition which asymptotically produces a high frequency of hard instances.
An Empirical Approach to Temporal Reference Resolution
Authors: Wiebe J. M. , O'Hara T. P. , Ohrstrom-Sandgren Thorsten , McKeever K. J.
Published: July 2018

Scheduling dialogs, during which people negotiate the times of appointments, are common in everyday life. This paper reports the results of an in-depth empirical investigation of resolving explicit temporal references in scheduling dialogs. There are four phases of this work: data annotation and evaluation, model development, system implementation and evaluation, and model evaluation and analysis. The system and model were developed primarily on one set of data, and then applied later to a much more complex data set, to assess the generalizability of the model for the task being performed. Many different types of empirical methods are applied to pinpoint the strengths and weaknesses of the approach. Detailed annotation instructions were developed and an intercoder reliability study was performed, showing that naive annotators can reliably perform the targeted annotations. A fully automatic system has been developed and evaluated on unseen test data, with good results on both data sets. We adopt a pure realization of a recency-based focus model to identify precisely when it is and is not adequate for the task being addressed. In addition to system results, an in-depth evaluation of the model itself is presented, based on detailed manual annotations. The results are that few errors occur specifically due to the model of focus being used, and the set of anaphoric relations defined in the model are low in ambiguity for both data sets.
Cooperation between Top-Down and Bottom-Up Theorem Provers
Authors: Fuchs D. , Fuchs M.
Published: July 2018

Top-down and bottom-up theorem proving approaches each have specific advantages and disadvantages. Bottom-up provers profit from strong redundancy control but suffer from the lack of goal-orientation, whereas top-down provers are goal-oriented but often have weak calculi when their proof lengths are considered. In order to integrate both approaches, we try to achieve cooperation between a top-down and a bottom-up prover in two different ways: The first technique aims at supporting a bottom-up with a top-down prover. A top-down prover generates subgoal clauses, they are then processed by a bottom-up prover. The second technique deals with the use of bottom-up generated lemmas in a top-down prover. We apply our concept to the areas of model elimination and superposition. We discuss the ability of our techniques to shorten proofs as well as to reorder the search space in an appropriate manner. Furthermore, in order to identify subgoal clauses and lemmas which are actually relevant for the proof task, we develop methods for a relevancy-based filtering. Experiments with the provers SETHEO and SPASS performed in the problem library TPTP reveal the high potential of our cooperation approaches.
Probabilistic Deduction with Conditional Constraints over Basic Events
Authors: Lukasiewicz T.
Published: July 2018

We study the problem of probabilistic deduction with conditional constraints over basic events. We show that globally complete probabilistic deduction with conditional constraints over basic events is NP-hard. We then concentrate on the special case of probabilistic deduction in conditional constraint trees. We elaborate very efficient techniques for globally complete probabilistic deduction. In detail, for conditional constraint trees with point probabilities, we present a local approach to globally complete probabilistic deduction, which runs in linear time in the size of the conditional constraint trees. For conditional constraint trees with interval probabilities, we show that globally complete probabilistic deduction can be done in a global approach by solving nonlinear programs. We show how these nonlinear programs can be transformed into equivalent linear programs, which are solvable in polynomial time in the size of the conditional constraint trees.
Identifying Mislabeled Training Data
Authors: Brodley C. E. , Friedl M. A.
Published: July 2018

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.
Popular Ensemble Methods: An Empirical Study
Authors: Opitz D. , Maclin R.
Published: July 2018

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Authors: Dietterich T. G.
Published: July 2018

This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. The decomposition, known as the MAXQ decomposition, has both a procedural semantics---as a subroutine hierarchy---and a declarative semantics---as a representation of the value function of a hierarchical policy. MAXQ unifies and extends previous work on hierarchical reinforcement learning by Singh, Kaelbling, and Dayan and Hinton. It is based on the assumption that the programmer can identify useful subgoals and define subtasks that achieve these subgoals. By defining such subgoals, the programmer constrains the set of policies that need to be considered during reinforcement learning. The MAXQ value function decomposition can represent the value function of any policy that is consistent with the given hierarchy. The decomposition also creates opportunities to exploit state abstractions, so that individual MDPs within the hierarchy can ignore large parts of the state space. This is important for the practical application of the method. This paper defines the MAXQ hierarchy, proves formal results on its representational power, and establishes five conditions for the safe use of state abstractions. The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges with probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction. The paper evaluates the MAXQ representation and MAXQ-Q through a series of experiments in three domains and shows experimentally that MAXQ-Q (with state abstractions) converges to a recursively optimal policy much faster than flat Q learning. The fact that MAXQ learns a representation of the value function has an important benefit: it makes it possible to compute and execute an improved, non-hierarchical policy via a procedure similar to the policy improvement step of policy iteration. The paper demonstrates the effectiveness of this non-hierarchical execution experimentally. Finally, the paper concludes with a comparison to related work and a discussion of the design tradeoffs in hierarchical reinforcement learning.
Cox's Theorem Revisited
Authors: Halpern J. Y.
Published: July 2018

The assumptions needed to prove Cox's Theorem are discussed and examined. Various sets of assumptions under which a Cox-style theorem can be proved are provided, although all are rather strong and, arguably, not natural.
Axiomatizing Causal Reasoning
Authors: Halpern J. Y.
Published: July 2018

Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl. In addition, the complexity of the decision procedures is characterized for all the languages and classes of models considered.
Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes
Authors: Zhang N. L. , Zhang W.
Published: July 2018

Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems.
Goal Recognition through Goal Graph Analysis
Authors: Hong J.
Published: July 2018

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.
Computational Approach to Anaphora Resolution in Spanish Dialogues
Authors: Palomar M. , Martinez-Barco P.
Published: July 2018

This paper presents an algorithm for identifying noun-phrase antecedents of pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora resolution requires numerous sources of information in order to find the correct antecedent of the anaphor. These sources can be of different kinds, e.g., linguistic information, discourse/dialogue structure information, or topic information. For this reason, our algorithm uses various different kinds of information (hybrid information). The algorithm is based on linguistic constraints and preferences and uses an anaphoric accessibility space within which the algorithm finds the noun phrase. We present some experiments related to this algorithm and this space using a corpus of 204 dialogues. The algorithm is implemented in Prolog. According to this study, 95.9% of antecedents were located in the proposed space, a precision of 81.3% was obtained for pronominal anaphora resolution, and 81.5% for adjectival anaphora.
Inferring Strategies for Sentence Ordering in Multidocument News Summarization
Authors: Barzilay R. , Elhadad N.
Published: July 2018

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.
Proceedings of the II National Congress «Oncology of reproductive organs: from prevention and early detection to effective treatment», 11-13 May, 2017
Authors: Article Editorial
Published: July 2018

.
Arm-wrestling injuries – A systematic review of the medical literature
Authors: Correia Renan Felipe , Ribeiro Alex Natalino , Araújo Roberto Pires de
Published: July 2018

Background: Arm wrestling is a popular sport due to its simplistic nature and great appeal in popular culture. Unfortunately, it can lead to serious fractures. An understanding of these injuries and its mechanisms is vital to the undertaking of safe practice methods for this sporting modality. Objectives: This study aimed to review articles that demonstrate case reports about arm-wrestling related fractures. Methods: PubMed’s database was searched with the keywords “Arm Wrestling,” Arm-wrestling,” and “Armwrestling.” Ofthe 34 articles found, 11 were fit to compose this review, being grouped into studies that dealt with adults and studies that dealt with adolescents. Results: Of the 63 adult subjects reported, 62 suffered fractures of the distal third of the humerus, occasionally accompanied by butterfly fractures and radial nerve palsy. Of the 22 adolescent subjects reported, 20 suffered fractures of the humerus medial epicondyle, never accompanied by butterfly fractures, and sometimes accompanied by ulnar nerve palsy. All injuries stemmed from unbalanced torsional forces suffered by the humerus from its own musculature. Conclusions: The injury profile presented by both populations is very homogeneous between the two different groups, which leads us to believe that correct technique can be a great ally in arm wrestling injury prevention. Athletes, as well as common practitioners, should be coached as to avoid these unfavorable positions and techniques
Aproximación a la figura del arquitecto Sebastián de Portu (1716-1776)
Authors: Ferreira Fernández Myriam
Published: July 2018

Sebastián de Portu fue un arquitecto activo en La Rioja y Álava a mediados del siglo XVIII. Su obra, de la que destacan sus retablos, portadas y otras obras religiosas como cajas de órgano, refleja la evolución del arte desde la fase final del barroco hacia los inicios del rococó. Este artículo ofrece un esbozo para su biografía y una recopilación de su obra artística, con algunas de las características más destacadas de su estilo.
Proyectos y reforma de la iglesia de Santa María de la Redonda de Logroño (La Rioja) para convertirla en catedral
Authors: Ramírez  Maya Sara
Published: July 2018

Las obras más importantes ejecutadas en Santa María de la Redonda durante el siglo XIX obedecieron a la intención de que se convirtiese en catedral. Este artículo es un estudio de los proyectos más importantes que articularon esta reforma: el primero realizado por el arquitecto Francisco Enríquez Ferrer en 1853, la modificación del mismo propuesta por Maximiano Hijón en 1880, y el proyecto definitivo realizado conjuntamente por los arquitectos Maximiano Hijón, Francisco de Luis y Tomás y Luis Barrón en 1882.
El vino de Rioja y su expansión desde la posguerra hasta 1970: Antonio Larrea, el hombre decisivo
Authors: Luena López César
Published: July 2018

Se exponen en este artículo las razones que permiten comprender la expansión del mundo del Rioja, los orígenes de la conquista de la calidad, la presencia en los mercados del mundo y la evolución del concepto “cultura del vino”, a través de la obra de Antonio Larrea, el hombre que dirigió las instituciones fundamentales del Rioja entre 1944 y 1971 y que abordó con decisión los problemas para llegar a la Denominación Calificada y al éxito rotundo del “modelo Rioja”.
La moralidad y los valores del alumnado de “aulas de la experiencia” de la Universidad del País Vasco. Una aproximación desde la psicología moral
Authors: Domingo Gómez José
Published: July 2018

En el presente artículo se presenta una investigación acerca del Razonamiento Moral y los Valores del alumnado de “Aulas de la Experiencia”, de la Universidad del País Vasco, del campus de Álava, en Vitoria. Se han utilizado, como instrumentos de medida, tres cuestionarios: 1.- el DIT de Rest; 2.- un cuestionario elaborado específicamente de datos personales (CDP) y 3.- la Escala de Valores de Schwartz. Se han tenido en cuenta las variables sociopersonales de: género, edad, estudios realizados, posicionamiento político y creencias religiosas. En los resultados obtenidos se observa, con significatividad estadística, que el Razonamiento Moral de este tipo de alumnado es, mayoritariamente, de rango Convencional (el 82% frente al 18% de Postconvencionalidad). Los resultados obtenidos son, en general, congruentes con las investigaciones de otros grupos de población analizada. Asimismo, aparecen también, con significatividad estadística, los Valores de: libertad, respeto, madurez, amistad, lealtad, honestidad, responsabilidad, perdón, creatividad, independencia, curiosidad, benevolencia, sentido de pertenencia.
Adela Cortina: el reto de la ética cordial
Authors: Sánchez Pachón Javier
Published: July 2018

La ética cordial es la más reciente contribución de Adela Cortina a la Filosofía Moral en España. La cordialidad es una categoría moral sobre la que se puede levantar todo el edificio ético, desde su fundamentación hasta la aplicación practica. Intentar dar razón de esa cordialidad, fundamentarla desde la antropología como exclusividad humana, caracterizarla desde diferentes ángulos, remitirnos a sus antecedentes filosóficos, concretar las implicaciones que tiene para la obligación moral y apuntar las consecuencias que pueden extraerse de todo ello nos ayudara a considerar la ética desde una perspectiva nueva.
A comparative study of breast feeding and complimentary food practices in catchment area of urban and rural health centres of Community Medicine Department, Gandhi Medical College, Bhopal
Authors: Meena Jeewan S. , Sethia Soumitra , Nagar Vivek , Thakur Aditya , Aarutagi Vishwanath , Tiwari Sharad
Published: July 2018

Background: Scientific research has clearly proved that breastfeeding provides the most suitable nutrition to the baby, protects against infections, allergies, promotes physical, physiological, motor-mental and psycho-social growth and development. Also it benefits the mother in various ways like reducing anemia and breast and ovarian cancers and helps in fertility control. The study was conducted to assess and compare the knowledge and practice regarding breastfeeding among mothers of infants in urban and rural area.Methods: This community based descriptive cross sectional study on 450 mothers of infant (0-12 completed months) residing in six wards of urban health centre and twenty three villages of block of rural health centre of Gandhi Medical College, Bhopal. Study was undertaken for the period of 1 year (November 2016 to October 2017). Study participants were interviewed by home visits using semi structured questionnaire.Results: Maximum mothers 40% in rural and 35.1% mothers in urban area age group 21-23 year. 39.1% mothers in rural area provided colostrums to their child compared to 50.6% in urban areas (p=0.0178). In rural area 60% mothers gave breast milk to their child on demand, 25% within 1-2 hours & 15.1% within 2-3 hours. In urban area 80% mothers gave breast milk on demand, 12.8% within 1-2 hours & rest within 2-3 hours.Conclusions: There was significant difference in breastfeeding practices in rural and urban area. Breastfeeding on demand was found to be more in urban area as compared to rural area.
Adubação potássica e época de semeadura em soja para a produção de etanol
Authors: Toller Márcio , Peluzio Joenes Mucci , Reina Evandro , Lima Maria Dilma de , Hackenhaar Celso , Hackenhaar Neusa
Published: July 2018

A cadeia produtiva do biodiesel gera coprodutos, como o melaço, com potencial para produção de biodiesel e que pode agregar valor e gerar outras fontes de renda para os produtores. Neste sentido, no presente estudo foram avaliados cultivares em diferentes épocas de semeadura quanto à eficiência de uso de potássio para teor e rendimento de carboidratos, visando à produção de etanol na região centro-sul do Estado do Tocantins. No ano agrícola de 2013/2014, foram realizados quatroensaios de competição de cultivares, dois instalados em 05/12/2013 e dois em 23/01/2014. Em cada época, foram utilizadas duas doses de potássio, sendo uma alta (200 kg.ha-1 de K2O) e outra baixa (40 kg.ha-1 de K2O). Foi utilizado o delineamento de blocos casualizados com quatro repetições e sete tratamentos: BRS 333, BRS 33871, BRS 325, M 9144, P 98Y70, TMG 1180 e TMG 1288. Em semeaduras tardias e sob alta dose de potássio (200 kg.ha-1 K2O), houve um maior acúmulo de açúcares redutores totais nos grãos. Altas temperaturas e baixos índices pluviométricos; a fase de enchimento de grãos favorece o acúmulo de açúcares redutores totais nos grãos. Os cultivares TMG 1288 RR, sob alto e baixo potássio, e TMG 1180 RR, sob baixo potássio, são promissores para produção de etanol, sendo este último também eficiente quanto ao uso de potássio.
Comparison of E-Cigarette Use among Menthol and Non-Menthol Smokers: Findings from a Community Based Sample
Authors: Webb Hooper Monica , Smiley Sabrina L.
Published: July 2018

Objective: E-cigarette use is increasing among adult cigarette smokers. With the availability and variety of appealing charac­teristics, including menthol flavor, e-ciga­rette use patterns may differ among menthol and non-menthol cigarette smokers. This study compared e-cigarette knowledge and use between current menthol and non-menthol smokers aged ≥18 years.

Design: Current adult cigarette smokers (N=223; M=42.1 years; SD=12.2; 68% menthol smokers) recruited in South Florida completed an interviewer-administered sur­vey via telephone during June to November 2014.

Main Outcome Measures: E-cigarette use (ever-use, past 30-day use, past 30-day flavored e-cigarette use, and past 30-day mentholated e-cigarette use), consideration of e-cigarette use for quitting/reduction of cigarettes, and knowledge assessments. Bivariate and multivariate analyses tested associations with menthol smoking.

Results: Menthol smokers were more likely to be African American or Hispanic (P<.001) and report lower income (P=.02) and education (P<.001) than non-menthol smokers. Adjusted analyses found no as­sociation between menthol cigarette use and e-cigarette ever-use. However, menthol smokers demonstrated less e-cigarette knowledge (P<.01) and were more likely to consider using e-cigarettes to quit/re­duce smoking (AOR=3.89, CI:1.55-9.78). Among ever-users, there was no association between menthol cigarette use and past 30-day e-cigarette use, yet menthol smokers were more likely to use menthol flavored e-cigarettes (AOR=6.65, CI: 1.94-12.78).

Conclusions: Our findings suggest that, compared with current non-menthol smokers, current menthol smokers are more likely to consider using e-cigarettes to help quit/reduce smoking, and are more likely to use menthol flavored e-cigarettes. Further research is needed to better ex­amine low e-cigarette knowledge among menthol smokers, which may represent an important intervention target.

Ethn Dis. 2018;28(3):153-160; doi:10.18865/ ed.28.3.153.
Feeding Habits of Polynemids: Polydactylus Quadrifilis, Galeoides Decadactylus, and Pentanemus Quinquarius of the Continental Shelf of Grand-Lahou, Côte d’Ivoire
Authors: Sylvain Konan Kouassi , Alla Yao Laurent , Nicolas Amon Yao , Konan N’da
Published: July 2018

This paper focuses on the threadfins Polydactylus quadrifilis, Galeoides decadactylus, and Pentanemus quinquarius captured by the maritime traditional fishing of Grand-Lahou in Côte d'Ivoire. It aims to determine their feeding habits based on weighing, measuring of body, and intestine height and identification of prey. The specimens of Polydactylus quadrifilis with height lower than 55 cm have an average of 12±1 intestinal filaments, whereas those of height higher than 100 cm have an average of 20±2 of them. This average is constant concerning Galeoides decadactylus (12±2) and Pentanemus quinquarius (11±2), for all height. The specimens of Polydactylus quadrifilis consume mainly fish (74.45%), followed by shrimps (10.06%). The juvenile ones of less than one year prefer shrimps (80.28%), whereas fish are preferred (87.07) by the older individuals. Whatever their age, Galeoides decadactylus and Pentanemus quinquarius mainly consume shrimps in the ratio of 83.11% and 68.75% respectively. The relative yearly food consumption reveals for Polydactylus quadrifilis a quantity of 0.124 T/Km2 /year, whereas that of Galeoides decadactylus is 0.02 T/Km2 /year. As for the specimens of Pentanemus quinquarius, they consume 0.013 T/Km2 /year. Threadfins have very short intestine (IC < 1) relatively to the intestinal coefficient. Their food habit is relating to stomach and intestine capabilities.
Análisis Del Efecto De La Adición De Fibra Cítrica Del Bagazo De La Naranja En Las Propiedades Nutrimentales Y Sensoriales De Un Embutido Y Determinación De La Calidad Microbiológica
Authors: Durán-Mendoza Temani , González-Pérez Manuel , Cruz-Leyva María Concepción de la , Cruz José Ulises González-de la , Cuenca-Soria Carlos Alberto , Guzmán-Ceferino Juan , Angulo-Guerrero Jesús Ofelia , Pérez-Sánchez Carolina del Carmen
Published: July 2018

The orange bagasse (Citrus sinensis) is a residue with a significant content of citrus fiber (FC); it is used as a functional ingredient in the development of new products. We consider it essential to know the effect of the nutritional and sensorial properties of FC. The objective of this research was to evaluate the effect of powdered the orange bagasse on the nutritional and sensorial properties of pork and beef sausage. The sausage was prepared by mixing the proportions of minced pork and beef with the corresponding FC, adding spices, orange juice and a solution of natural dye Bixa Orellana, after 24 hours of maturation was stuffed into cellulose casings. A randomized design with three repetitions was used. The treatments were formulations with 0, 5, 10 and 15% of FC respectively, named treatments E0, E5, E10 and E15. The chemical variables evaluated were: moisture content, ash, fat, protein (analyzed by the AOAC method, 1990), and total dietary fiber (by the methodology described by Asp et al., 1983 and Prosky et al, 1988); the microbiological variables: aerobic mesophiles, total and fecal coliforms, Salmonella and Staphylococcus aureus, detected and quantified with the methods of the corresponding Official Mexican Standards. The sensory variable was the level of liking which was evaluated in 128 consumers, and a commercial sausage called "EC" was used. As a comparison product. In E5, E10 and E15 the fat levels decreased (p <0.05). The ash increased as a function of the addition of FC. Regarding protein, no differences were observed between E0 and E5. In the treatments, E10 and E 15 the protein decreased significantly. Total dietary fiber at E5, E10, and E15 ranged from 4.38 to 10.50%. The consumer test favored E0, E5, and EC. We conclude that the orange bagasse exerts a positive effect by decreasing fat and increasing total dietary fiber. Aleson-Carbonell et al. (2003) report similar behavior. It is concluded that it is feasible to add FC no higher than 5% as an alternative to enrich sausages.