bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Eccentric Cycling Performed at Long vs. Short Muscle Lengths on Heart Rate, Rate Perceived Effort, and Muscle Damage Markers
Authors: Peñailillo Luis , Aedo Carolina , Cartagena Mayari , Contreras Alejandra , Reyes Alvaro , Ramirez-Campillo Rodrigo , Earp Jacob E. , Zbinden-Foncea Hermann
Published: July 2018

Peñailillo, L, Aedo, C, Cartagena, M, Contreras, A, Reyes, A, Ramirez-Campillo, R, Earp, JE, and Zbinden-Foncea, H. Effects of eccentric cycling performed at long vs. short muscle lengths on heart rate, rate perceived effort, and muscle damage markers. J Strength Cond Res 34(10): 2895–2902, 2020—It is possible that the magnitude of muscle damage in eccentric cycling is dependent on seat position that could affect the muscle length changes during eccentric contractions. However, no previous study has investigated the effect of seat position on muscle damage and metabolic demand in eccentric exercise. Thus, this study compared 2 seating positions; seat forward in which knee extensor muscles were stretched longer (LONG) and seat back in which the muscles were stretched shorter (SHORT). Young men performed 30 minutes of eccentric cycling at 80% of maximal concentric power output at either LONG (n = 10) or SHORT (n = 10) condition. Heart rate and rate perceived effort (RPE) during exercise, changes in maximal voluntary isometric contraction (MVC) strength of the knee extensors, muscle soreness assessed by a visual analog scale (VAS), and pressure-pain threshold (PPT) before, immediately after, and 24–72 hours after exercise were compared between conditions. Heart rate and RPE were significantly greater in LONG than SHORT group (9.5 and 19.0%, respectively). Post-exercise reduction in MVC was 8.4–14.0% greater in LONG than SHORT group from immediately after to 48 hours after exercise. In addition, VAS was greater in LONG than SHORT group from 48 to 72 hours after exercise, and vastus medialis PPT was greater in SHORT than LONG group from 24 to 72 hours after exercise (p < 0.05). These results suggest that muscle damage and metabolic demand are greater when seat is set back than forward. Thus, when prescribing eccentric cycling to individuals who are unaccustomed to eccentric cycling, it is better to set the seat far from the pedals.
Induced Positive Mood and Cognitive Flexibility: Evidence from Task Switching and Reversal Learning
Authors: Nusbaum Amy T. , Wilson Cristina G. , Stenson Anthony , Hinson John M. , Whitney Paul
Published: July 2018

Positive mood often facilitates cognitive functions. Facilitation is hypothesized to be due to an increase in dopamine occurring in positive mood states. However, facilitation has not been consistently found in studies of cognitive flexibility. This inconsistent relationship may reflect the numerous ways cognitive flexibility is measured. Moreover, there is evidence that the role of dopamine in cognitive flexibility performance depends on the type of measure used. In the current study, we employed a probabilistic two-card reversal learning task (n = 129) and a Stroop-like task switching procedure (n = 188) in a college student population. We used a standardized set of mood videos to induce a positive, negative, or neutral mood state. Negative mood states were included to account for possible effects of arousal on performance, which is seen in both positive and negative mood inductions. Based on current theories of positive mood and cognition, we hypothesized that there would be differences in the effects of a positive mood induction on cognitive flexibility as assessed by task switching and reversal learning tasks. The mood induction successfully induced high levels of amusement and increased valence in the positive mood group and high levels of repulsion and decreased valence in the negative mood group. However, there were no differences in cognitive flexibility across any of the mood groups, as assessed by switch costs in task switching and correct choices after the reversal in reversal learning. Overall, these findings do not support the hypothesis that positive mood improves cognitive flexibility.
Bancada de testes de armas de fogo por acionamento remoto
Authors: Arakelian Marcos Barkev , Henriques Antonio Manoel Dias , Andrade Charles Albert
Published: July 2018

Anteriormente a execução do presente projeto, ou seja, julho de 2017, todos os testes de armas de fogo realizados na Seção de Balística Forense da Polícia Civil do Distrito Federal eram realizados manual e presencialmente. Considerando que, nem todas as armas de fogo e munições apreendidas possuem confiabilidade em sua integridade, o perito corria sérios riscos ao realizar os testes. No intuito de modificar esta situação, este trabalho teve o objetivo de projetar e construir uma bancada de testes para armas de fogo em que seu acionamento é feito de forma remota e segura. O projeto foi dividido em quatro etapas. Sendo a primeira etapa a de análise de alternativas e tomada de decisões, seguida pela etapa de dimensionamento, e, por fim, a fabricação e montagem da bancada de testes, assim como seus respectivos testes. Como resultado final, obteve-se uma bancada de testes operando com eficiência e segurança.
Novos horizontes
Authors: Silva Adelino Pinheiro , Telles Bruno , Dias Filho Claudemir Rodrigues , Gomes Juliano de Andrade , Tocantins Rogerio de Medeiros
Published: July 2018

Não há pauta mais urgente do que aquela relacionada à segurança pública. E a proximidade das eleições de 2018 oferece um momento especial para esta pauta. Invariavelmente, os candidatos incluirão em seus programas eleitorais estratégias de enfrentamento da violência, vez que os índices de criminalidade continuam altos e isso é apontado como uma das principais preocupações do eleitor.
Integrating SQ4R Technique with Graphic Postorganizers in the Science Learning of Earth and Space
Authors: Djudin T. , Amir R.
Published: July 2018

This study examined the effect of integrating SQ4R reading technique with graphic post organizers on the students’ Earth and Space Science learning achievement and development of metacognitive knowledge. The pretest-posttest non-equivalent control group design was employed in this quasi-experimental method. The sample which consists of 103 seventh grade of secondary school students of SMPN 1 Pontianak was drawn by using intact group random sampling technique. An achievement test and a questionnaire of  Reading-Self Awareness were administered. The findings assert that there are significant difference of students’ achievement (F=5.594, p ‹ 0.05) and development of metacognitive knowledge (F= 13.906, p ‹ 0.05) among groups after having received the three distinctive treatments. Integrating SQ4R reading technique with graphic post organizers reveals an effective impact on the academic achievement (ES= 0.69) and the metacognitive knowledge in reading text (ES = 0.48). It confirms that a science teacher has to execute and model metacognitive strategies intentionally.
Students’ Mental Models of Acid and Base Concepts Taught Using Cognitive Apprenticeship Learning Model
Authors: Amalia F. R. , Ibnu S. , Widarti H. R. , Wuni H.
Published: July 2018

A mental model is a student's perception or description of a concept. Chemistry learning requires deep understanding including three levels of representation; macroscopic, sub-microscopic, and symbolic. These three levels of representation are interconnected to form a meaningful understanding and students' mental model. The purpose of this study was to look at the mental models of students on acid and base topic taught using cognitive apprenticeship learning model. This research employed explanatory sequential mix method with 65 research subjects. The research instrument was in the form of a description test of acid-base concepts and an interview guideline for the students’ answers on the test. The results showed that students had good mental models (63.64%) on acid and base taught using cognitive apprenticeship learning model.
Mechanisms of beneficial effects of metformin on fatty acid-treated human islets
Authors: Cen Jing , Sargsyan Ernest , Forslund Anders , Bergsten Peter
Published: July 2018

Elevated levels of palmitate accentuate glucose-stimulated insulin secretion (GSIS) after short-term and cause beta-cell dysfunction after prolonged exposure. We investigated whether metformin, the first-line oral drug for treatment of T2DM, has beneficial effects on FFA-treated human islets and the potential mechanisms behind the effects. Insulin secretion, oxygen consumption rate (OCR), AMPK activation, endoplasmic reticulum (ER) stress and apoptosis were examined in isolated human islets after exposure to elevated levels of palmitate in the absence or presence of metformin. Palmitate exposure doubled GSIS after 2 days but halved after 7 days compared with control. Inclusion of metformin during palmitate exposure normalized insulin secretion both after 2 and 7 days. After 2-day exposure to palmitate, OCR and the marker of the adaptive arm of ER stress response (sorcin) were significantly raised, whereas AMPK phosphorylation, markers of pro-apoptotic arm of ER stress response (p-EIF2α and CHOP) and apoptosis (cleaved caspase 3) were not affected. Presence of metformin during 2-day palmitate exposure normalized OCR and sorcin levels. After 7-day exposure to palmitate, OCR and sorcin were not significantly different from control level, p-AMPK was reduced and p-EIF2α, CHOP and cleaved caspase 3 were strongly upregulated. Presence of metformin during 7-day culture with palmitate normalized the level of p-AMPK, p-EIF2α, CHOP and cleaved caspase 3 but significantly increased the level of sorcin. Our study demonstrates that metformin prevents early insulin hypersecretion and later decrease in insulin secretion from palmitate-treated human islets by utilizing different mechanisms.
Klorheksidin uygulamasının bulk-fill ve nanofil kompozitlerin tamir bağlanma dayanımı üzerine etkisi
Authors: KILIÇ Vahti , HÜRMÜZLÜ Feridun
Published: July 2018

Amaç: Bu çalışmanın amacı
klorheksidin uygulamasının nanofil ve bulk-fill kompozitlerin tamir bağlanma
dayanımı üzerine etkisini değerlendirmektirGereç ve Yöntemler: Çalışmamızda
kullanılmak üzere oluşturulan akrilik bloklar üzerinde 160 adet kavite
hazırlandı. Kavitelerin yarısı nanofil kompozitlerle (Filtek Ultimate) doldurulurken yarısı da
bulk-fill kompozitlerle (Tetric EvoCeram Bulkfill) dolduruldu. Bütün örnekler 1 hafta 37ºC distile suda
bekletildikten sonra her restoratif materyalin örnekleri başlıca 2 deney
grubuna ayrıldı. Grup 1’de örnekler 60 saniye klorheksidin uygulamasına maruz
bırakıldı. Grup 2’deki örneklere ise bir uygulama yapılmadı (kontrol).
Restoratif materyallerin uygun adezivleri her iki grupta ara tamir ajanı olarak
uygulandı. Her gruptan örneklerin yarısı test öncesi termal siklusa ( 5000
siklus, 5-55 ºC, kalma süresi 30 saniye, transfer zamanı 15 saniye) maruz
bırakıldı, diğer yarısı hemen teste alınarak tamir bağlanma dayanımı makaslama
testi ile değerlendirildi. Deney gruplarının makaslama bağlanma dayanımı analiz
etmek için çift yönlü ANOVA testi kullanıldı. Önemlilik seviyesi (p<0.05)
alındı. Bağlanma dayanımı testi sonrasında oluşan kırık tipleri optik loop ile
incelendi ve gruplara ayrıldı. Kırık tipleri ayrıca Taramalı Elektron Mikroskobu ile görüntülendi.Bulgular:  Klorheksidin ve termal
siklus uygulaması tamir bağlanma dayanımını anlamlı bir şekilde etkilememiştir
(p>0.05). Bulk-fill kompozitler daha düşük tamir bağlanma dayanımı
göstermiştir. En yüksek bağlanma değeri Nanofil kompozitlerle 24 saat sonra
kontrol grubunda  elde
edilirken en düşük değer klorheksidin uygulaması sonrası termal siklus yapılan
Bulk-fill kompozitlerde bulunmuştur. Mix tip kırılmalar en fazla rastlanan tip
kırılmalar olmuştur.

Sonuçlar: Bu in vitro çalışmanın sınırları içinde
klorheksidin ve ek olarak uygulanan termal siklus ile yaşlandırma metodu
bulk-fill ve nanofil bazlı kompozitlerin tamir bağlanma dayanımını
etkilememiştir.
Crystal growth of Ge2Sb2Te5 at high temperatures
Authors: Ronneberger I. , Zhang W. , Mazzarello R.
Published: July 2018

Phase-change materials (PCMs) have important applications in optical and electronic storage devices. Ge2Sb2Te5 (GST) is a prototypical phase-change material (PCM) employed in state-of-the-art storage-class memories. In this work, we investigate crystallization of GST at temperatures 600–800 K by ab initio molecular dynamics. We consider large models containing 900 atoms, which enable us to investigate finite-size effects by comparison with smaller models. We use the metadynamics method to accelerate the formation of a large nucleus and then study the growth of the nucleus by unbiased simulations. The calculated crystal growth speed and its temperature-dependent behavior are in line with recent experimental work.
Catechesis and its Six Tasks
Authors: Sultana Carl Mario
Published: July 2018

The aim of catechesis is not simply to give information to interlocutors about the Catholic faith. Catechesis seeks to do much more than this, with its ultimate aim being that of putting the human being in communion with Christ. This is achieved through the six tasks of catechesis which need to be deeply embedded in human experiences. The six tasks through which catechesis seeks to achieve its endeavours include: knowledge of the faith, liturgical education, moral formation, formation in prayer and methods of prayer, education for community life and missionary initiation.
La administración de los bienes de la Iglesia hasta el siglo X: algunas de las disposiciones, especialmente de los concilios, y su recepción en el Código de Derecho Canónico de Juan Pablo II
Authors: Di Nicco Jorge Antonio
Published: July 2018

This article analyses the evolution of the goods of the Church and its administration during the first millennium of the Christian era. It reveals the first community of Jerusalem where believers had everything in common, discusses the centrality of the Bishop and severely damaging mismanagement, and some of the provisions on the subject under study, especially those arising from the various councils. It talks about the attempts and tendencies towards the disintegration of the diocesan heritage as a unitary whole from the sixth century; notwithstanding which, the Bishop has the responsibility for his subordinate entities, keeping the administrative power over the property of the diocese. In the last part, the article presents several of these provisions in the Code of Canon Law by John Paul II.
HIV/AIDS care: analysis of actions and health services integration
Authors: Magnabosco Gabriela Tavares , Lopes Lívia Maria , Andrade Rubia Laine de Paula , Brunello Maria Eugênia Firmino , Monroe Aline Aparecida , Villa Tereza Cristina Scatena
Published: July 2018

Abstract Objective: To analyze the integration of actions and health services in care provided by Specialized Services (SS) in Ribeirão Preto/SP in people living with HIV/AIDS (PLWHA) perception. Methods: Exploratory descriptive study, survey type in which 253 PLWHA were interviewed using a structured questionnaire. The data were analyzed by indicators (classified as unsatisfactory-average from 1 to 2.5; regular-between 2.6 and 3.5; satisfying-3.6 to 5) and multiple correspondence analysis. Results: The integration of health care actions had an average 3.7 (SD = 1.7) classified as satisfactory. The horizontal integration was satisfactory regarding the use of medical records, and the vertical one regarding the referral to other health services. The low utilization of Basic Care and Emergency Care services was also identified. Conclusion: Despite of the satisfactory rating on the integration of assistance to PLWHA, challenges still be done, highlighting the need for greater empowerment of individuals and strengthening reference mechanisms with support of SCS to other points of attention in HIV/AIDS specificities.
Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams
Authors: Zeng Y. , Doshi P.
Published: July 2018

We focus on the problem of sequential decision making in partially observable environments shared with other agents of uncertain types having similar or conflicting objectives. This problem has been previously formalized by multiple frameworks one of which is the interactive dynamic influence diagram (I-DID), which generalizes the well-known influence diagram to the multiagent setting. I-DIDs are graphical models and may be used to compute the policy of an agent given its belief over the physical state and others' models, which changes as the agent acts and observes in the multiagent setting.

As we may expect, solving I-DIDs is computationally hard. This is predominantly due to the large space of candidate models ascribed to the other agents and its exponential growth over time. We present two methods for reducing the size of the model space and stemming its exponential growth. Both these methods involve aggregating individual models into equivalence classes. Our first method groups together behaviorally equivalent models and selects only those models for updating which will result in predictive behaviors that are distinct from others in the updated model space. The second method further compacts the model space by focusing on portions of the behavioral predictions. Specifically, we cluster actionally equivalent models that prescribe identical actions at a single time step. Exactly identifying the equivalences would require us to solve all models in the initial set. We avoid this by selectively solving some of the models, thereby introducing an approximation. We discuss the error introduced by the approximation, and empirically demonstrate the improved efficiency in solving I-DIDs due to the equivalences.
Generalized Biwords for Bitext Compression and Translation Spotting
Authors: Sánchez-Martínez F. , Carrasco R. C. , Martínez-Prieto M. A. , Adiego J.
Published: July 2018

Large bilingual parallel texts (also known as bitexts) are usually stored in a compressed form, and previous work has shown that they can be more efficiently compressed if the fact that the two texts are mutual translations is exploited. For example, a bitext can be seen as a sequence of biwords ---pairs of parallel words with a high probability of co-occurrence--- that can be used as an intermediate representation in the compression process. However, the simple biword approach described in the literature can only exploit one-to-one word alignments and cannot tackle the reordering of words. We therefore introduce a generalization of biwords which can describe multi-word expressions and reorderings. We also describe some methods for the binary compression of generalized biword sequences, and compare their performance when different schemes are applied to the extraction of the biword sequence. In addition, we show that this generalization of biwords allows for the implementation of an efficient algorithm to look on the compressed bitext for words or text segments in one of the texts and retrieve their counterpart translations in the other text ---an application usually referred to as translation spotting--- with only some minor modifications in the compression algorithm.
The Logical Difference for the Lightweight Description Logic EL
Authors: Konev B. , Ludwig M. , Walther D. , Wolter F.
Published: July 2018

We study a logic-based approach to versioning of ontologies. Under this view, ontologies provide answers to queries about some vocabulary of interest. The difference between two versions of an ontology is given by the set of queries that receive different answers.
We investigate this approach for terminologies given in the description logic EL extended with role inclusions and domain and range restrictions for three distinct types of queries: subsumption, instance, and conjunctive queries. In all three cases, we present polynomial-time algorithms that decide whether two terminologies give the same answers to queries over a given vocabulary and compute a succinct representation of the difference if it is non-
empty. We present an implementation, CEX2, of the developed algorithms for subsumption and instance queries and apply it to distinct versions of Snomed CT and the NCI ontology.
Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction
Authors: Albacete E. , Calle J. , Castro E. , Cuadra D.
Published: July 2018

The focus of this paper is the calculation of similarity between two concepts from an ontology for a Human-Like Interaction system. In order to facilitate this calculation, a similarity function is proposed based on five dimensions (sort, compositional, essential, restrictive and descriptive) constituting the structure of ontological knowledge. The paper includes a proposal for computing a similarity function for each dimension of knowledge. Later on, the similarity values obtained are weighted and aggregated to obtain a global similarity measure. In order to calculate those weights associated to each dimension, four training methods have been proposed. The training methods differ in the element to fit: the user, concepts or pairs of concepts, and a hybrid approach. For evaluating the proposal, the knowledge base was fed from WordNet and extended by using a knowledge editing toolkit (Cognos). The evaluation of the proposal is carried out through the comparison of system responses with those given by human test subjects, both providing a measure of the soundness of the procedure and revealing ways in which the proposal may be improved.
Automatic Aggregation by Joint Modeling of Aspects and Values
Authors: Sauper C. , Barzilay R.
Published: July 2018

We present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Our model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each aspect. This approach directly enables discovery of highly-rated or inconsistent aspects of a product. Our generative model admits an efficient variational mean-field inference algorithm. It is also easily extensible, and we describe several modifications and their effects on model structure and inference. We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries. We evaluate the performance of the model on aspect identification, sentiment analysis, and per-word labeling accuracy. We demonstrate that our model outperforms applicable baselines by a considerable margin, yielding up to 32% relative error reduction on aspect identification and up to 20% relative error reduction on sentiment analysis.
Transforming Graph Data for Statistical Relational Learning
Authors: Rossi R. A. , McDowell L. K. , Aha D. W. , Neville J.
Published: July 2018

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) system- atically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also dis- cuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
Coalition Structure Generation over Graphs
Authors: Voice T. , Polukarov M. , Jennings N. R.
Published: July 2018

We give the analysis of the computational complexity of coalition structure generation over graphs. Given an undirected graph G = (N,E) and a valuation function v : P(N) → R over the subsets of nodes, the problem is to find a partition of N into connected subsets, that maximises the sum of the components values. This problem is generally NPcomplete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected membersthat is, two nodes have no effect on each others marginal con- tribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minorfree graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive linear time bounds for graphs of bounded treewidth. However, as we show, the problem remains NPcomplete for planar graphs, and hence, for any K_k minorfree graphs where k ≥ 5. Moreover, a 3-SAT problem with m clauses can be represented by a coalition structure generation problem over a planar graph with O(m^2) nodes. Importantly, our hardness result holds for a particular subclass of valuation functions, termed edge sum, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph.
Short and Long Supports for Constraint Propagation
Authors: Nightingale P. , Gent I. P. , Jefferson C. , Miguel I.
Published: July 2018

Special-purpose constraint propagation algorithms frequently make implicit use of short supports -- by examining a subset of the variables, they can infer support (a justification that a variable-value pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work -- but short supports have not been studied in their own right. The two main contributions of this paper are the identification of short supports as important for constraint propagation, and the introduction of HaggisGAC, an efficient and effective general purpose propagation algorithm for exploiting short supports. Given the complexity of HaggisGAC, we present it as an optimised version of a simpler algorithm ShortGAC. Although experiments demonstrate the efficiency of ShortGAC compared with other general-purpose propagation algorithms where a compact set of short supports is available, we show theoretically and experimentally that HaggisGAC is even better. We also find that HaggisGAC performs better than GAC-Schema on full-length supports. We also introduce a variant algorithm HaggisGAC-Stable, which is adapted to avoid work on backtracking and in some cases can be faster and have significant reductions in memory use. All the proposed algorithms are excellent for propagating disjunctions of constraints. In all experiments with disjunctions we found our algorithms to be faster than Constructive Or and GAC-Schema by at least an order of magnitude, and up to three orders of magnitude.
Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine Translation
Authors: Costa-jussà M. R. , Henríquez C. A. , Banchs R. E.
Published: July 2018

Although, Chinese and Spanish are two of the most spoken languages in the world, not much research has been done in machine translation for this language pair. This paper focuses on investigating the state-of-the-art of Chinese-to-Spanish statistical machine translation (SMT), which nowadays is one of the most popular approaches to machine translation. For this purpose, we report details of the available parallel corpus which are Basic Traveller Expressions Corpus (BTEC), Holy Bible and United Nations (UN). Additionally, we conduct experimental work with the largest of these three corpora to explore alternative SMT strategies by means of using a pivot language. Three alternatives are considered for pivoting: cascading, pseudo-corpus and triangulation. As pivot language, we use either English, Arabic or French. Results show that, for a phrase-based SMT system, English is the best pivot language between Chinese and Spanish. We propose a system output combination using the pivot strategies which is capable of outperforming the direct translation strategy. The main objective of this work is motivating and involving the research community to work in this important pair of languages given their demographic impact.
A Hybrid LP-RPG Heuristic for Modelling Numeric Resource Flows in Planning
Authors: Coles A. , Coles A. , Fox M. , Long D.
Published: July 2018

Although the use of metric fluents is fundamental to many practical planning problems, the study of heuristics to support fully automated planners working with these fluents remains relatively unexplored. The most widely used heuristic is the relaxation of metric fluents into interval-valued variables --- an idea first proposed a decade ago. Other heuristics depend on domain encodings that supply additional information about fluents, such as capacity constraints or other resource-related annotations.

A particular challenge to these approaches is in handling interactions between metric fluents that represent exchange, such as the transformation of quantities of raw materials into quantities of processed goods, or trading of money for materials. The usual relaxation of metric fluents is often very poor in these situations, since it does not recognise that resources, once spent, are no longer available to be spent again.

We present a heuristic for numeric planning problems building on the propositional relaxed planning graph, but using a mathematical program for numeric reasoning. We define a class of producer--consumer planning problems and demonstrate how the numeric constraints in these can be modelled in a mixed integer program (MIP). This MIP is then combined with a metric Relaxed Planning Graph (RPG) heuristic to produce an integrated hybrid heuristic. The MIP tracks resource use more accurately than the usual relaxation, but relaxes the ordering of actions, while the RPG captures the causal propositional aspects of the problem. We discuss how these two components interact to produce a single unified heuristic and go on to explore how further numeric features of planning problems can be integrated into the MIP. We show that encoding a limited subset of the propositional problem to augment the MIP can yield more accurate guidance, partly by exploiting structure such as propositional landmarks and propositional resources. Our results show that the use of this heuristic enhances scalability on problems where numeric resource interaction is key in finding a solution.
Boolean Equi-propagation for Concise and Efficient SAT Encodings of Combinatorial Problems
Authors: Metodi A. , Codish M. , Stuckey P. J.
Published: July 2018

We present an approach to propagation-based SAT encoding of combinatorial problems, Boolean equi-propagation, where constraints are modeled as Boolean functions which propagate information about equalities between Boolean literals. This information is then applied to simplify the CNF encoding of the constraints. A key factor is that considering only a small fragment of a constraint model at one time enables us to apply stronger, and even complete, reasoning to detect equivalent literals in that fragment. Once detected, equivalences apply to simplify the entire constraint model and facilitate further reasoning on other fragments. Equi-propagation in combination with partial evaluation and constraint simplification provide the foundation for a powerful approach to SAT-based finite domain constraint solving. We introduce a tool called BEE (Ben-Gurion Equi-propagation Encoder) based on these ideas and demonstrate for a variety of benchmarks that our approach leads to a considerable reduction in the size of CNF encodings and subsequent speed-ups in SAT solving times.
Learning by Observation of Agent Software Images
Authors: Costa P. R. , Botelho L. M.
Published: July 2018

Learning by observation can be of key importance whenever agents sharing similar features want to learn from each other. This paper presents an agent architecture that enables software agents to learn by direct observation of the actions executed by expert agents while they are performing a task. This is possible because the proposed architecture displays information that is essential for observation, making it possible for software agents to observe each other.

The agent architecture supports a learning process that covers all aspects of learning by observation, such as discovering and observing experts, learning from the observed data, applying the acquired knowledge and evaluating the agent's progress. The evaluation provides control over the decision to obtain new knowledge or apply the acquired knowledge to new problems.

We combine two methods for learning from the observed information. The first one, the recall method, uses the sequence on which the actions were observed to solve new problems. The second one, the classification method, categorizes the information in the observed data and determines to which set of categories the new problems belong.

Results show that agents are able to learn in conditions where common supervised learning algorithms fail, such as when agents do not know the results of their actions a priori or when not all the effects of the actions are visible. The results also show that our approach provides better results than other learning methods since it requires shorter learning periods.
Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System
Authors: Androutsopoulos I. , Lampouras G. , Galanis D.
Published: July 2018

We present NaturalOWL, a natural language generation system that produces texts describing individuals or classes of OWL ontologies. Unlike simpler OWL verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like NaturalOWL, one can publish information in OWL on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of NaturalOWL, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent llinguistic resources are available, NaturalOWL produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.
Defeasible Inheritance-Based Description Logics
Authors: Casini G. , Straccia U.
Published: July 2018

Defeasible inheritance networks are a non-monotonic framework that deals with hierarchical knowledge. On the other hand, rational closure is acknowledged as a landmark of the preferential approach to non-monotonic reasoning. We will combine these two approaches and define a new non-monotonic closure operation for propositional knowledge bases that combines the advantages of both. Then we redefine such a procedure for Description Logics (DLs), a family of logics well-suited to model structured information. In both cases we will provide a simple reasoning method that is built on top of the classical entailment relation and, thus, is amenable of an implementation based on existing reasoners. Eventually, we evaluate our approach on well-known landmark test examples.
Selfishness Level of Strategic Games
Authors: Apt K. R. , Schaefer G.
Published: July 2018

We introduce a new measure of the discrepancy in strategic games between the social welfare in a Nash equilibrium and in a social optimum, that we call selfishness level. It is the smallest fraction of the social welfare that needs to be offered to each player to achieve that a social optimum is realized in a pure Nash equilibrium. The selfishness level is unrelated to the price of stability and the price of anarchy and is invariant under positive linear transformations of the payoff functions. Also, it naturally applies to other solution concepts and other forms of games.

We study the selfishness level of several well-known strategic games. This allows us to quantify the implicit tension within a game between players' individual interests and the impact of their decisions on the society as a whole. Our analyses reveal that the selfishness level often provides a deeper understanding of the characteristics of the underlying game that influence the players' willingness to cooperate.

In particular, the selfishness level of finite ordinal potential games is finite, while that of weakly acyclic games can be infinite. We derive explicit bounds on the selfishness level of fair cost sharing games and linear congestion games, which depend on specific parameters of the underlying game but are independent of the number of players. Further, we show that the selfishness level of the $n$-players Prisoner's Dilemma is c/(b(n-1)-c), where b and c are the benefit and cost for cooperation, respectively, that of the n-players public goods game is (1-c/n)/(c-1), where c is the public good multiplier, and that of the Traveler's Dilemma game is (b-1)/2, where b is the bonus. Finally, the selfishness level of Cournot competition (an example of an infinite ordinal potential game), Tragedy of the Commons, and Bertrand competition is infinite.
MDD Propagation for Sequence Constraints
Authors: Bergman D. , Cire A. A. , Van Hoeve W.
Published: July 2018

We study propagation for the Sequence constraint in the context of constraint programming based on limited-width MDDs. Our first contribution is proving that establishing MDD-consistency for Sequence is NP-hard. Yet, we also show that this task is fixed parameter tractable with respect to the length of the sub-sequences. In addition, we propose a partial filtering algorithm that relies on a specific decomposition of the constraint and a novel extension of MDD filtering to node domains. We experimentally evaluate the performance of our proposed filtering algorithm, and demonstrate that the strength of the MDD propagation increases as the maximum width is increased. In particular, MDD propagation can outperform conventional domain propagation for Sequence by reducing the search tree size and solving time by several orders of magnitude. Similar improvements are observed with respect to the current best MDD approach that applies the decomposition of Sequence into Among constraints.
HC-Search: A Learning Framework for Search-based Structured Prediction
Authors: Doppa J.R. , Fern A. , Tadepalli P.
Published: July 2018

Structured prediction is the problem of learning a function that maps structured inputs to structured outputs. Prototypical examples of structured prediction include part-of-speech tagging and semantic segmentation of images. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called HC-Search. Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then employs a separate learned cost function C to select a final prediction among those outputs. The overall loss of this prediction architecture decomposes into the loss due to H not leading to high quality outputs, and the loss due to C not selecting the best among the generated outputs. Guided by this decomposition, we minimize the overall loss in a greedy stage-wise manner by first training H to quickly uncover high quality outputs via imitation learning, and then training C to correctly rank the outputs generated via H according to their true losses. Importantly, this training procedure is sensitive to the particular loss function of interest and the time-bound allowed for predictions. Experiments on several benchmark domains show that our approach significantly outperforms several state-of-the-art methods.
Algorithms and Applications for the Same-Decision Probability
Authors: Chen S. J. , Choi A. , Darwiche A.
Published: July 2018

When making decisions under uncertainty, the optimal choices are often difficult to discern, especially if not enough information has been gathered. Two key questions in this regard relate to whether one should stop the information gathering process and commit to a decision (stopping criterion), and if not, what information to gather next (selection criterion). In this paper, we show that the recently introduced notion, Same-Decision Probability (SDP), can be useful as both a stopping and a selection criterion, as it can provide additional insight and allow for robust decision making in a variety of scenarios. This query has been shown to be highly intractable, being PP^PP-complete, and is exemplary of a class of queries which correspond to the computation of certain expectations. We propose the first exact algorithm for computing the SDP, and demonstrate its effectiveness on several real and synthetic networks. Finally, we present new complexity results, such as the complexity of computing the SDP on models with a Naive Bayes structure. Additionally, we prove that computing the non-myopic value of information is complete for the same complexity class as computing the SDP.
A Procedural Characterization of Solution Concepts in Games
Authors: Halpern J. Y. , Moses Y.
Published: July 2018

We show how game-theoretic solution concepts such as Nash equilibrium, correlated equilibrium, rationalizability, and sequential equilibrium can be given a uniform definition in terms of a knowledge-based program with counterfactual semantics. In a precise sense, this program can be viewed as providing a procedural characterization of rationality.
Convergence of a Q-learning Variant for Continuous States and Actions
Authors: Carden S. W.
Published: July 2018

This paper presents a reinforcement learning algorithm for solving infinite horizon Markov Decision Processes under the expected total discounted reward criterion when both the state and action spaces are continuous. This algorithm is based on Watkins' Q-learning, but uses Nadaraya-Watson kernel smoothing to generalize knowledge to unvisited states. As expected, continuity conditions must be imposed on the mean rewards and transition probabilities. Using results from kernel regression theory, this algorithm is proven capable of producing a Q-value function estimate that is uniformly within an arbitrary tolerance of the true Q-value function with probability one. The algorithm is then applied to an example problem to empirically show convergence as well.
Sentiment Analysis of Short Informal Texts
Authors: Kiritchenko S. , Zhu X. , Mohammad S. M.
Published: July 2018

We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words.

The system ranked first in the SemEval-2013 shared task `Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.
Probabilistic Inference in Credal Networks: New Complexity Results
Authors: Maua D. D. , De Campos C. P. , Benavoli A. , Antonucci A.
Published: July 2018

Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence. We show that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one. We prove that under epistemic irrelevance the polynomial-time complexity of inferences in credal trees is not likely to extend to more general models (e.g., singly connected topologies). These results clearly distinguish networks that admit efficient inferences and those where inferences are most likely hard, and settle several open questions regarding their computational complexity. We show that these results remain valid even if we disallow the use of zero probabilities. We also show that the computation of bounds on the probability of the future state in a hidden Markov model is the same whether we assume epistemic irrelevance or strong independence, and we prove a similar result for inference in naive Bayes structures. These inferential equivalences are important for practitioners, as hidden Markov models and naive Bayes structures are used in real applications of imprecise probability.
Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives
Authors: Veit A. , Xu Y. , Zheng R. , Chakraborty N. , Sycara K.
Published: July 2018

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents' incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants' flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.