bluuebunny/crossref_metadata_2025 huggingface dataset:
Russell’s Two Lectures in China on Mathematical Logic
Authors: Zhou Lianghua , Linsky Bernard
Published: July 2018

In 1921 Bertrand Russell delivered two lectures on mathematical logic at Peking University. Manuscripts for the lectures have not been found, but two sets of Chinese notes, which were based on a simultaneous oral translation of Russell’s lecturing, were published. The notes are translated into English based on the best readings of both sets. An introduc­tion and notes with a glossary discuss the background and content of the lectures as well as the linguistic difficulties in translating logical terms.
SOĞUK MASERASYONUN KARALAHNA VE CABERNET SAUVIGNON ŞIRALARININ ANTOSİYANİN VE UÇUCU BİLEŞENLERİ ÜZERİNE ETKİSİ
Authors: ÇELEBİ UZKUÇ Nesrin Merve , ŞİŞLİ Burcu , KARAGÜL YÜCEER Yonca , BAYHAN Aslı , Kırca Toklucu Ayşegül
Published: July 2018

Bu çalışmada, Karalahna ve Cabernet Sauvignon
üzümlerinden elde edilen mayşeye uygulanan soğuk maserasyonun (7-8°C/4 gün)
şıranın genel bileşimi, antosiyanin ve uçucu bileşenleri üzerine etkileri
incelenmiştir. Şıraların antosiyanin kompozisyonları Yüksek Performanslı Sıvı
Kromatografisi, uçucu bileşenleri ise Gaz Kromatografisi-Kütle Spektrometresi
ile belirlenmiştir. Toplam monomerik antosiyanin (TMA) ve toplam fenolik madde
miktarları (TFM) ile renk yoğunluğu ve renk tonu değerleri ise spektrofotometrik
yöntemlerle saptanmıştır. Karalahna şırasının TMA içeriği başlangıçta 89.4 mg/L
iken, soğuk maserasyonla 145.1 mg/L düzeyine ulaşmıştır. Cabernet Sauvignon
şırasının TMA içeriği ise 169 mg/L’den 358 mg/L’ye yükselmiştir. Şıralarda
malvidin-3-glukozit hakim antosiyanin olup, soğuk maserasyon işlemi sonunda
Karalahna ve Cabernet Sauvignon şıralarında miktarı sırasıyla 1.3 ve 1.7 katına
çıkmıştır. Örneklerde 17 alkol, 8 ester ve 7 adet diğer bileşikler olmak üzere
toplam 32 adet uçucu bileşik tespit edilmiş olup, yüksek alkollerin ve
esterlerin konsantrasyonunun soğuk maserasyon sonunda artış gösterdiği
saptanmıştır.
Learning Experience of Pre-Service Physics Teachers in Developing Simple Project Loaded by Life Skills
Authors: Susilawati S. , Khoiri N. , Wijayanto W. , Masturi M. , Xaphakdy S.
Published: July 2018

This research aims to reveal the experience profile of future Physics teachers in developing simple multi-loaded life skill project. The research method used was a descriptive method with 62 future Physics teachers as the research subject. Based on observations, interviews, and questionnaires, it was drawn descriptions of the future teachers’ experiences in developing a simple project. The future teachers were assessed based on some Physics indicators such as project design, project creation, data collection, data analysis, and creativity. The results of the project preparation descriptions obtained by the experience data of future Physics teachers indicate the highest score of 4.8 (96%) on the indicator of schedule making, while the lowest score was 2.9 (58%) on the indicators of formulating the product benefits, preparing the feasibility analysis and analyzing the environmental impact. The results show that future Physics teachers got the highest score of 4.6 (93%) on data adjustment for experimental purposes indicator, and the lowest score of 2.8 (55%) on designing tools and materials indicator. Based on the Physics props trial, it was obtained the highest score of 4.5 (89%) on the indicator of analyzing the baseline data, while the lowest score of 2.9 (58%) was on the variation test variables. Positive feedback about time-management in completing the project, the ability to identify the topic of the project, the ability to find ideas to support the completion of the project, the ability to test the tools, and the ability to make progress reports on project completion were derived from the interviews. The highest score of students’ responses to project completion was 4.8 (95%) on the second data collection indicator and the lowest was 3.2 (64%) on the instrument material design indicator.
Teachers’ Performance in Science Learning Management Integrated with Character Education
Authors: Isdaryanti B. , Rahman M. , Sukestiyarno Y. L. , Florentinus T. S. , Widodo W.
Published: July 2018

The research aims at revealing the performance of teachers in science learning management integrated with character education. This study employed qualitative research method. Based on the result of the t-test of the correlation coefficient, this study obtained t value 4,210 with significance 0,001. The significance value 0,001 < 0,05 showed that there is influence from teachers’ performance in arranging learning media integrated with character education to the teachers’ performance in science learning. The conclusion of research stated that the performance of science teachers in junior high schools in Semarang City in integrating character education is categorized into a very good category with average score 85,05.
A Robust Data Envelopment Analysis for Evaluating Technical Efficiency of Indonesian High Schools
Authors: Mahmudah U. , Suhartono S. , Rohayana A. D.
Published: July 2018

The main purpose of this study is to evaluate the technical efficiency of high school education in Indonesia by applying Data Envelopment Analysis (DEA), which is the most frequently used to measure the efficiency scores. However, this study uses a robust approach to face the complex problem of the traditional DEA, which may lead to biased results. Besides, it is a powerful approach to estimate technical efficiency when outliers contaminate the data set. Statistical data from general senior secondary schools in the period 2015/2016 is analyzed, using 34 provinces as decision-making units (DMUs), with eight input and six output variables. The results indicate that the average efficiency score of Indonesia’s major political subdivisions in managing high school education is 0.936. Furthermore, as many as 32.35 percents of provinces achieve efficient performances, with an efficiency score equal to one, while 17 provinces have above average efficiency scores.  The results also indicate that efficiency scores from robust data envelopment analysis provide better accuracy. Overall, application of robust data envelopment analysis (RDEA) is appropriate for measuring the efficiency of provincial performance in organizing secondary education.
The Influence of Inquiry-Based Science Issues Learning on Practical Skills of Junior High School Students in Environmental Pollution Topic
Authors: Hastuti P. W. , Tiarani V. A. , Nurita T.
Published: July 2018

Inquiry-based science issues approach facilitates the development of scientific skills through research activities on surrounding science issues. The purpose of this research was to understand the influence of Inquiry-based science issues on practical skills of Junior High School students. This research employed quasi-experimental research method, equipped with pre-test, post-test non-equivalent control group design. The sample of sixty junior high school students in Yogyakarta was picked for this study.  The instruments used were observation sheets on practical skills, practical skills test, and instructional learning sheets. The data collected were analyzed using N-Gain, percentage, and Analysis of Covariate (ANCOVA) at 0.05 significance level. The result revealed that Inquiry Science Issues was more effective in fostering students’ practical skills than the scientific approach. There was a significant effect of treatment on students’ practical skills while no interaction between approach and prior knowledge of students. Moreover, the data of practical skills through the observation sheets in the experimental class had higher percentage compared to the control class, 80.36%, and 70.61% respectively.
The Reconstruction of Disaster Knowledge through Thematic Learning of Science, Environment, Technology, and Society Integrated with Local Wisdom
Authors: Atmojo S. E. , Rusilowati A. , Dwiningrum S. I. A. , Skotnicka M.
Published: July 2018

This study aimed to improve the mastery of knowledge, attitudes, and skills of students in disaster mitigation in prone areas of volcanic eruptions. The subjects of this research were fifth (V) graders in three elementary schools situated in prone areas of Merapi volcano eruption in Srumbung subdistrict, Magelang regency, Central Java. The data collected in this study consisted of disaster knowledge, attitude, and mitigation practice. The data of disaster knowledge were obtained through a test and analyzed using t-test and N-gain. The attitude data were collected using questionnaire with an improvement analysis using N-gain. The mitigation practice data were collected through observation and analyzed by descriptive percentage for attitudes and skills. The research results showed that the implementation of thematic learning of Science, Environment, Technology, and Society (SETS) integrated with local wisdom was able to reconstruct and increase the disaster management knowledge. This can be seen from the N-gain score obtained by each elementary school which the highest Gain Score was 0.46 and included in the medium category. There was an enhancement attitude toward disaster having the highest Gain Score was 0.77. All disaster mitigation indicators were successfully mastered by more than 70 percent of the students, who were in the good category (70≥X≤85) in all three elementary schools implementing the thematic learning of SETS integrated with local wisdom.
TNF signaling impacts glucagon-like peptide-1 expression and secretion
Authors: Chen Sufang , Wei Wei , Chen Minjie , Qin Xiaobo , Qiu Lianglin , Zhang Li , Zhang Yuhao , Cao Qi , Ying Zhekang
Published: July 2018

Numerous studies have implicated tumor necrosis factor α (TNFα) in the pathogenesis of type 2 diabetes. However, the role of its primary receptor, TNF receptor 1 (TNFR1), in homeostatic regulation of glucose metabolism is still controversial. In addition to TNFα, lymphotoxin α (LTα) binds to and activates TNFR1. Thus, TNFα and LTα together are known as TNF. To delineate the role of TNF signaling in glucose homeostasis, the present study ascertained how TNF signaling deficiency affects major regulatory components of glucose homeostasis. To this end, normal diet-fed male TNFR1-deficient mice (TNFR1−/−), TNFα/LTα/LTβ triple-deficient mice (TNF/LT∆3) and their littermate controls were subjected to intraperitoneal glucose tolerance test, insulin tolerance test and oral glucose tolerance test. The present results showed that TNFR1−/− and TNF/LT∆3 mice vs their controls had comparable body weight, tolerance to intraperitoneal glucose and sensitivity to insulin. However, their tolerance to oral glucose was significantly increased. Additionally, glucose-induced insulin secretion assessments revealed that TNFR1 or TNF/LT deficiency significantly increased oral but not intraperitoneal glucose-induced insulin secretion. Consistently, qPCR and immunohistochemistry analyses showed that TNFR1−/− and TNF/LT∆3 mice vs their controls had significantly increased ileal expression of glucagon-like peptide-1 (GLP-1), one of the primary incretins. Their oral glucose-induced secretion of GLP-1 was also significantly increased. These data collectively suggest that physiological TNF signaling regulates glucose metabolism primarily through effects on GLP-1 expression and secretion and subsequently insulin secretion.
Rapid Low-Cost Assembly of the Drosophila melanogaster Reference Genome Using Low-Coverage, Long-Read Sequencing
Authors: Solares Edwin A , Chakraborty Mahul , Miller Danny E , Kalsow Shannon , Hall Kate , Perera Anoja G , Emerson J J , Hawley R Scott
Published: July 2018

Accurate and comprehensive characterization of genetic variation is essential for deciphering the genetic basis of diseases and other phenotypes. A vast amount of genetic variation stems from large-scale sequence changes arising from the duplication, deletion, inversion, and translocation of sequences. In the past 10 years, high-throughput short reads have greatly expanded our ability to assay sequence variation due to single nucleotide polymorphisms. However, a recent de novo assembly of a second Drosophila melanogaster reference genome has revealed that short read genotyping methods miss hundreds of structural variants, including those affecting phenotypes. While genomes assembled using high-coverage long reads can achieve high levels of contiguity and completeness, concerns about cost, errors, and low yield have limited widespread adoption of such sequencing approaches. Here we resequenced the reference strain of D. melanogaster (ISO1) on a single Oxford Nanopore MinION flow cell run for 24 hr. Using only reads longer than 1 kb or with at least 30x coverage, we assembled a highly contiguous de novo genome. The addition of inexpensive paired reads and subsequent scaffolding using an optical map technology achieved an assembly with completeness and contiguity comparable to the D. melanogaster reference assembly. Comparison of our assembly to the reference assembly of ISO1 uncovered a number of structural variants (SVs), including novel LTR transposable element insertions and duplications affecting genes with developmental, behavioral, and metabolic functions. Collectively, these SVs provide a snapshot of the dynamics of genome evolution. Furthermore, our assembly and comparison to the D. melanogaster reference genome demonstrates that high-quality de novo assembly of reference genomes and comprehensive variant discovery using such assemblies are now possible by a single lab for under $1,000 (USD).
Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data
Authors: Newman Jeremy R B , Concannon Patrick , Tardaguila Manuel , Conesa Ana , McIntyre Lauren M
Published: July 2018

Alternative splicing leverages genomic content by allowing the synthesis of multiple transcripts and, by implication, protein isoforms, from a single gene. However, estimating the abundance of transcripts produced in a given tissue from short sequencing reads is difficult and can result in both the construction of transcripts that do not exist, and the failure to identify true transcripts. An alternative approach is to catalog the events that make up isoforms (splice junctions and exons). We present here the Event Analysis (EA) approach, where we project transcripts onto the genome and identify overlapping/unique regions and junctions. In addition, all possible logical junctions are assembled into a catalog. Transcripts are filtered before quantitation based on simple measures: the proportion of the events detected, and the coverage. We find that mapping to a junction catalog is more efficient at detecting novel junctions than mapping in a splice aware manner. We identify 99.8% of true transcripts while iReckon identifies 82% of the true transcripts and creates more transcripts not included in the simulation than were initially used in the simulation. Using PacBio Iso-seq data from a mouse neural progenitor cell model, EA detects 60% of the novel junctions that are combinations of existing exons while only 43% are detected by STAR. EA further detects ∼5,000 annotated junctions missed by STAR. Filtering transcripts based on the proportion of the transcript detected and the number of reads on average supporting that transcript captures 95% of the PacBio transcriptome. Filtering the reference transcriptome before quantitation, results in is a more stable estimate of isoform abundance, with improved correlation between replicates. This was particularly evident when EA is applied to an RNA-seq study of type 1 diabetes (T1D), where the coefficient of variation among subjects (n = 81) in the transcript abundance estimates was substantially reduced compared to the estimation using the full reference. EA focuses on individual transcriptional events. These events can be quantitate and analyzed directly or used to identify the probable set of expressed transcripts. Simple rules based on detected events and coverage used in filtering result in a dramatic improvement in isoform estimation without the use of ancillary data (e.g., ChIP, long reads) that may not be available for many studies.
Elaboración de recubrimientos cerámicos sobre sustratos refractarios utilizando proyección térmica oxiacetilénica a partir de materias primas no convencionales
Authors: Cadavid Edwin , Vargas-Galvis Fabio , López-Gómez María Esperanza , Mesa Carlos Mario , Vargas Fabio Alonso , Latorre Guillermo , Peña Gabriel
Published: July 2018

Se seleccionaron, caracterizaron, procesaron y depositaron sobre ladrillos refractarios, dos residuos industriales provenientes de procesos de la industria petroquímica y termoeléctrica, así como un mineral de cromita. La composición química y las fases cristalográficas de cada uno de estos polvos fueron determinadas mediante Fluorescencia y Difracción de Rayos X, mientras que la morfología y tamaño de partícula fueron analizados mediante Microscopía Electrónica de Barrido. Estos polvos fueron depositados mediante proyección térmica oxiacetilénica sobre ladrillos refractarios silico-aluminosos y concretos de alta alúmina de bajo cemento, cuyas referencias comerciales de la empresa Erecos S.A.S. son ER-40 y CBC-50 respectivamente. Los parámetros utilizados para la elaboración de los recubrimientos fueron optimizados a partir de recubrimientos elaborados previamente y de los resultados de simulaciones realizadas con el software Jets&poudres. De los recubrimientos obtenidos el que presentó mayor homogeneidad en su estructura fue el elaborado con el mineral de cromita, los demás evidenciaron gran cantidad de partículas sin fundir y mayor porosidad. Sin embargo, por la composición química, su buena adherencia y las fases presentes en los recubrimientos todos son potenciales candidatos para ser usados como medio protector contra el deterioro de refractarios expuestos a altas temperaturas.
Evaluación del efecto genotóxico y mutagénico en linfocitos humanos expuestos a nanotubos de carbono modificados
Authors: Espinosa Claudia , Hoyos-Palacio Lina Marcela , López-López Lucelly , Gallego-González Daniel , Aranzazu-Ceballos Andrés David , Marín-Cárdenas Juan Sebastián , Quintero-Moreno Dayana Andrea , Ortiz-Trujillo Isabel Cristina
Published: July 2018

Objetivo: evaluar efecto genotóxico y mutagénico en linfocitos humanos expuestos a nanotubos de carbono (NTC) prístinos y dopados con Nitrógeno. Métodos: linfocitos humanos fueron expuestos a NTC dopados y prístinos (0.08, 0.09, 0.1mg/mL), se evaluó alteraciones cromosómicas e intercambio de cromátidas hermanas (ICH). Resultados: se presentaron rupturas cromatídicas y cromosómicas en linfocitos expuestos a 0.1mg/mL y 0.08mg/mL de NTC prístinos. NTC dopados, indujeron cromosomas dicéntricos y anillos cromosómicos. Se presentó diferencia significativa en el porcentaje de ICH de células tratadas con NTC dopados versus control negativo y NTC prístinos (p<0.0001). Conclusión: la concentración más alta de NTC prístinos indujo daños cromatídicos y cromosómicos; aunque el porcentaje de la población con daño fue inferior al 10%, se consideran perjudiciales para las células. Linfocitos tratados con NTC dopados presentaron menores porcentajes de AC y altos valores en ICH, lo que muestra mayor reparación de material genético con estos compuestos.
Research skills in medical education
Authors: Watson Helen R , Burr Steven
Published: July 2018

This article was migrated. The article was marked as recommended. The ability to find, interpret and use information is a key skill in any clinician's arsenal. During medical training, we aim to equip our students with the ability to navigate the potentially baffling amount of information available online, and come to valid conclusions. This article reflects on the nature of research skills and how they are delivered in medical education. It also explores whether these are the most efficient methods for enabling students to become able researchers. Comparisons with other types of university degrees are made, and consideration given to how research skills should best be integrated into our teaching.
Constructing a logical model in school health: experience on the Lower Amazon region
Authors: Souza Elaine Fernanda Dornelas de , Soares Maria do Carmo Souza , Santos Sueyla Ferreira da Silva dos , Paulo Thais Reis Silva de , Brandão Maria Verinize Santos , Freitas Júnior Ismael Forte
Published: July 2018

ABSTRACT Objective: This experience report presents the construction of the theoretical and logic model for intervention to promote physical activity and healthy eating habits of the public schools students in the city of Parintins, Amazonas, Brazil. Method: We carried out a theoretical research and also a documentary research on the School Health Program (PSE - Programa Saúde na Escola) on the national and local level, in addition to meetings with the PSE management committee for strategic planning of the actions. Result: The municipality had no systematic structure of health practices at school. A theoretical and logical model that involved prerogatives of practice from the management of the municipality in PSE was developed. Actions to promote physical activity and healthy nutrition of primary and secondary school students were planned and promoted in schools of the PSE. Final considerations: Thus, the collective construction of this proposal contributed to the research of multi-sectoral practices related to the PSE principles and strategies, considering the particularities of the Lower Amazon context.
Toward Supervised Anomaly Detection
Authors: Goernitz N. , Kloft M. , Rieck K. , Brefeld U.
Published: July 2018

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies.
An approximative inference method for solving ∃∀SO satisfiability problems
Authors: Vlaeminck H. , Vennekens J. , Denecker M. , Bruynooghe M.
Published: July 2018

This paper considers the fragment ∃∀SO of second-order logic. Many interesting problems, such as conformant planning, can be naturally expressed as finite domain satisfiability problems of this logic. Such satisfiability problems are computationally hard (ΣP2) and many of these problems are often solved approximately. In this paper, we develop a general approximative method, i.e., a sound but incomplete method, for solving ∃∀SO satisfiability problems. We use a syntactic representation of a constraint propagation method for first-order logic to transform such an ∃∀SO satisfiability problem to an ∃SO(ID) satisfiability problem (second-order logic, extended with inductive definitions). The finite domain satisfiability problem for the latter language is in NP and can be handled by several existing solvers. Inductive definitions are a powerful knowledge representation tool, and this moti- vates us to also approximate ∃∀SO(ID) problems. In order to do this, we first show how to perform propagation on such inductive definitions. Next, we use this to approximate ∃∀SO(ID) satisfiability problems. All this provides a general theoretical framework for a number of approximative methods in the literature. Moreover, we also show how we can use this framework for solving practical useful problems, such as conformant planning, in an effective way.
Predicting Behavior in Unstructured Bargaining with a Probability Distribution
Authors: Wolpert D. H. , Bono J. W.
Published: July 2018

In experimental tests of human behavior in unstructured bargaining games, typically many joint utility outcomes are found to occur, not just one. This suggests we predict the outcome of such a game as a probability distribution. This is in contrast to what is conventionally done (e.g, in the Nash bargaining solution), which is predict a single outcome. We show how to translate Nash's bargaining axioms to provide a distribution over outcomes rather than a single outcome. We then prove that a subset of those axioms forces the distribution over utility outcomes to be a power-law distribution. Unlike Nash's original result, our result holds even if the feasible set is finite. When the feasible set is convex and comprehensive, the mode of the power law distribution is the Harsanyi bargaining solution, and if we require symmetry it is the Nash bargaining solution. However, in general these modes of the joint utility distribution are not the experimentalist's Bayes-optimal predictions for the joint utility. Nor are the bargains corresponding to the modes of those joint utility distributions the modes of the distribution over bargains in general, since more than one bargain may result in the same joint utility. After introducing distributional bargaining solution concepts, we show how an external regulator can use them to optimally design an unstructured bargaining scenario. Throughout we demonstrate our analysis in computational experiments involving flight rerouting negotiations in the National Airspace System. We emphasize that while our results are formulated for unstructured bargaining, they can also be used to make predictions for noncooperative games where the modeler knows the utility functions of the players over possible outcomes of the game, but does not know the move spaces the players use to determine those outcomes.
Tractable Set Constraints
Authors: Bodirsky M. , Hils M.
Published: July 2018

Many fundamental problems in artificial intelligence, knowledge representation, and verification involve reasoning about sets and relations between sets and can be modeled as set constraint satisfaction problems (set CSPs). Such problems are frequently intractable, but there are several important set CSPs that are known to be polynomial-time tractable. We introduce a large class of set CSPs that can be solved in quadratic time. Our class, which we call EI, contains all previously known tractable set CSPs, but also some new ones that are of crucial importance for example in description logics. The class of EI set constraints has an elegant universal-algebraic characterization, which we use to show that every set constraint language that properly contains all EI set constraints already has a finite sublanguage with an NP-hard constraint satisfaction problem.
A Survey on Latent Tree Models and Applications
Authors: Mourad R. , Sinoquet C. , Zhang N. L. , Liu T. , Leray P.
Published: July 2018

In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field.
The Arcade Learning Environment: An Evaluation Platform for General Agents
Authors: Bellemare M. G. , Naddaf Y. , Veness J. , Bowling M.
Published: July 2018

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
Acyclicity Notions for Existential Rules and Their Application to Query Answering in Ontologies
Authors: Cuenca Grau B. , Horrocks I. , Krötzsch M. , Kupke C. , Magka D. , Motik B. , Wang Z.
Published: July 2018

Answering conjunctive queries (CQs) over a set of facts extended with existential rules is a prominent problem in knowledge representation and databases. This problem can be solved using the chase algorithm, which extends the given set of facts with fresh facts in order to satisfy the rules. If the chase terminates, then CQs can be evaluated directly in the resulting set of facts. The chase, however, does not terminate necessarily, and checking whether the chase terminates on a given set of rules and facts is undecidable. Numerous acyclicity notions were proposed as sufficient conditions for chase termination. In this paper, we present two new acyclicity notions called model-faithful acyclicity (MFA) and model-summarising acyclicity (MSA). Furthermore, we investigate the landscape of the known acyclicity notions and establish a complete taxonomy of all notions known to us. Finally, we show that MFA and MSA generalise most of these notions.

Existential rules are closely related to the Horn fragments of the OWL 2 ontology language; furthermore, several prominent OWL 2 reasoners implement CQ answering by using the chase to materialise all relevant facts. In order to avoid termination problems, many of these systems handle only the OWL 2 RL profile of OWL 2; furthermore, some systems go beyond OWL 2 RL, but without any termination guarantees. In this paper we also investigate whether various acyclicity notions can provide a principled and practical solution to these problems. On the theoretical side, we show that query answering for acyclic ontologies is of lower complexity than for general ontologies. On the practical side, we show that many of the commonly used OWL 2 ontologies are MSA, and that the number of facts obtained by materialisation is not too large. Our results thus suggest that principled development of materialisation-based OWL 2 reasoners is practically feasible.
Unsupervised Sub-tree Alignment for Tree-to-Tree Translation
Authors: Xiao T. , Zhu J.
Published: July 2018

This article presents a probabilistic sub-tree alignment model and its application to tree-to-tree machine translation. Unlike previous work, we do not resort to surface heuristics or expensive annotated data, but instead derive an unsupervised model to infer the syntactic correspondence between two languages. More importantly, the developed model is syntactically-motivated and does not rely on word alignments. As a by-product, our model outputs a sub-tree alignment matrix encoding a large number of diverse alignments between syntactic structures, from which machine translation systems can efficiently extract translation rules that are often filtered out due to the errors in 1-best alignment. Experimental results show that the proposed approach outperforms three state-of-the-art baseline approaches in both alignment accuracy and grammar quality. When applied to machine translation, our approach yields a +1.0 BLEU improvement and a -0.9 TER reduction on the NIST machine translation evaluation corpora. With tree binarization and fuzzy decoding, it even outperforms a state-of-the-art hierarchical phrase-based system.
Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search
Authors: Guez A. , Silver D. , Dayan P.
Published: July 2018

Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, planning optimally in the face of uncertainty is notoriously taxing, since the search space is enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach avoids expensive applications of Bayes rule within the search tree by sampling models from current beliefs, and furthermore performs this sampling in a lazy manner. This enables it to outperform previous Bayesian model-based reinforcement learning algorithms by a significant margin on several well-known benchmark problems. As we show, our approach can even work in problems with an infinite state space that lie qualitatively out of reach of almost all previous work in Bayesian exploration.
Towards Minimizing Disappointment in Repeated Games
Authors: Crandall J. W.
Published: July 2018

We consider the problem of learning in repeated games against arbitrary associates. Specifically, we study the ability of expert algorithms to quickly learn effective strategies in repeated games, towards the ultimate goal of learning near-optimal behavior against any arbitrary associate within only a handful of interactions. Our contribution is three-fold. First, we advocate a new metric, called disappointment, for evaluating expert algorithms in repeated games. Unlike minimizing traditional notions of regret, minimizing disappointment in repeated games is equivalent to maximizing payoffs. Unfortunately, eliminating disappointment is impossible to guarantee in general. However, it is possible for an expert algorithm to quickly achieve low disappointment against many known classes of algorithms in many games. Second, we show that popular existing expert algorithms often fail to achieve low disappointment against a variety of associates, particularly in early rounds of the game. Finally, we describe a new meta-algorithm that can be applied to existing expert algorithms to substantially reduce disappointment in many two-player repeated games when associates follow various static, reinforcement learning, and expert algorithms.
Game-Theoretic Patrolling with Dynamic Execution Uncertainty and a Case Study on a Real Transit System
Authors: Delle Fave F.M. , Jiang A.X. , Yin Z. , Zhang C. , Tambe M. , Kraus S. , Sullivan J. P.
Published: July 2018

Attacker-Defender Stackelberg security games (SSGs) have emerged as an important research area in multi-agent systems. However, existing SSGs models yield fixed, static, schedules which fail in dynamic domains where defenders face execution uncertainty, i.e., in domains where defenders may face unanticipated disruptions of their schedules. A concrete example is an application involving checking fares on trains, where a defender's schedule is frequently interrupted by fare evaders, making static schedules useless.

To address this shortcoming, this paper provides four main contributions. First, we present a novel general Bayesian Stackelberg game model for security resource allocation in dynamic uncertain domains. In this new model, execution uncertainty is handled by using a Markov decision process (MDP) for generating defender policies. Second, we study the problem of computing a Stackelberg equilibrium for this game and exploit problem structure to reduce it to a polynomial-sized optimization problem. Shifting to evaluation, our third contribution shows, in simulation, that our MDP-based policies overcome the failures of previous SSG algorithms. In so doing, we can now build a complete system, that enables handling of schedule interruptions and, consequently, to conduct some of the first controlled experiments on SSGs in the field. Hence, as our final contribution, we present results from a real-world experiment on Metro trains in Los Angeles validating our MDP-based model, and most importantly, concretely measuring the benefits of SSGs for security resource allocation.
Optimal Scheduling of Contract Algorithms for Anytime Problem-Solving
Authors: Lopez-Ortiz A. , Angelopoulos S. , Hamel A. M.
Published: July 2018

A contract algorithm is an algorithm which is given, as part of the input, a specified amount of allowable computation time. The algorithm must then complete its execution within the allotted time. An interruptible algorithm, in contrast, can be interrupted at an arbitrary point in time, at which point it must report its currently best solution. It is known that contract algorithms can simulate interruptible algorithms using iterative deepening techniques. This simulation is done at a penalty in the performance of the solution, as measured by the so-called acceleration ratio.

In this paper we give matching (i.e., optimal) upper and lower bounds for the acceleration ratio under such a simulation. We assume the most general setting in which n problem instances must be solved by means of scheduling executions of contract algorithms in $m$ identical parallel processors. This resolves an open conjecture of Bernstein, Filkenstein, and Zilberstein who gave an optimal schedule under the restricted setting of round robin and length-increasing schedules, but whose optimality in the general unrestricted case remained open.

Lastly, we show how to evaluate the average acceleration ratio of the class of exponential strategies in the setting of n problem instances and m parallel processors. This is a broad class of schedules that tend to be either optimal or near-optimal, for several variants of the basic problem.
Tutorial on Structured Continuous-Time Markov Processes
Authors: Shelton C. R. , Ciardo G.
Published: July 2018

A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that the distribution over a future variable is independent of past variables given the state at the present time. We introduce continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, assuming no prior knowledge of continuous-time processes but some basic knowledge of probability and statistics. We begin by describing "flat" or unstructured Markov processes and then move to structured Markov processes (those arising from state spaces consisting of assignments to variables) including Kronecker, decision-diagram, and continuous-time Bayesian network representations. We provide the first connection between decision-diagrams and continuous-time Bayesian networks.
Simple Regret Optimization in Online Planning for Markov Decision Processes
Authors: Feldman Z. , Domshlak C.
Published: July 2018

We consider online planning in Markov decision processes (MDPs). In online planning, the agent focuses on its current state only, deliberates about the set of possible policies from that state onwards and, when interrupted, uses the outcome of that exploratory deliberation to choose what action to perform next. Formally, the performance of algorithms for online planning is assessed in terms of simple regret, the agent's expected performance loss when the chosen action, rather than an optimal one, is followed.

To date, state-of-the-art algorithms for online planning in general MDPs are either best effort, or guarantee only polynomial-rate reduction of simple regret over time. Here we introduce a new Monte-Carlo tree search algorithm, BRUE, that guarantees exponential-rate and smooth reduction of simple regret. At a high level, BRUE is based on a simple yet non-standard state-space sampling scheme, MCTS2e, in which different parts of each sample are dedicated to different exploratory objectives. We further extend BRUE with a variant of ``learning by forgetting.'' The resulting parametrized algorithm, BRUE(alpha), exhibits even more attractive formal guarantees than BRUE. Our empirical evaluation shows that both BRUE and its generalization, BRUE(alpha), are also very effective in practice and compare favorably to the state-of-the-art.
On the Testability of BDI Agent Systems
Authors: Winikoff M. , Cranefield S.
Published: July 2018

Before deploying a software system we need to assure ourselves (and stakeholders) that the system will behave correctly. This assurance is usually done by testing the system. However, it is intuitively obvious that adaptive systems, including agent-based systems, can exhibit complex behaviour, and are thus harder to test. In this paper we examine this "obvious intuition" in the case of Belief-Desire-Intention (BDI) agents. We analyse the size of the behaviour space of BDI agents and show that although the intuition is correct, the factors that influence the size are not what we expected them to be. Specifically, we found that the introduction of failure handling had a much larger effect on the size of the behaviour space than we expected. We also discuss the implications of these findings on the testability of BDI agents.
Approximate Value Iteration with Temporally Extended Actions
Authors: Mann Timothy A. , Mannor Shie , Precup Doina
Published: July 2018

Temporally extended actions have proven useful for reinforcement learning, but their duration also makes them valuable for efficient planning. The options framework provides a concrete way to implement and reason about temporally extended actions. Existing literature has demonstrated the value of planning with options empirically, but there is a lack of theoretical analysis formalizing when planning with options is more efficient than planning with primitive actions. We provide a general analysis of the convergence rate of a popular Approximate Value Iteration (AVI) algorithm called Fitted Value Iteration (FVI) with options. Our analysis reveals that longer duration options and a pessimistic estimate of the value function both lead to faster convergence. Furthermore, options can improve convergence even when they are suboptimal and sparsely distributed throughout the state-space. Next we consider the problem of generating useful options for planning based on a subset of landmark states. This suggests a new algorithm, Landmark-based AVI (LAVI), that represents the value function only at the landmark states. We analyze both FVI and LAVI using the proposed landmark-based options and compare the two algorithms. Our experimental results in three different domains demonstrate the key properties from the analysis. Our theoretical and experimental results demonstrate that options can play an important role in AVI by decreasing approximation error and inducing fast convergence.
Satsisfiability and Systematicity
Authors: Ginsberg Matthew L
Published: July 2018

We introduce a new notion of systematicity for satisfiability algorithms with restarts, saying that an algorithm is strongly systematic if it is systematic independent of restart policy but weakly systematic if it is systematic for some restart policies but not others. We show that existing satisfiability engines are generally only weakly systematic, and describe FLEX, a strongly systematic algorithm that uses an amount of memory polynomial in the size of the problem. On large number factoring problems, FLEX appears to outperform weakly systematic approaches.
PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner
Authors: Zhou Yujiao , Cuenca Grau Bernardo , Nenov Yavor , Kaminski Mark , Horrocks Ian
Published: July 2018

Answering conjunctive queries over ontology-enriched datasets is a core reasoning task for many applications. Query answering is, however, computationally very expensive, which has led to the development of query answering procedures that sacrifice either expressive power of the ontology language, or the completeness of query answers in order to improve scalability. In this paper, we describe a hybrid approach to query answering over OWL 2 ontologies that combines a datalog reasoner with a fully-fledged OWL 2 reasoner in order to provide scalable `pay-as-you-go' performance. The key feature of our approach is that it delegates the bulk of the computation to the datalog reasoner and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. Furthermore, although our main goal is to efficiently answer queries over OWL 2 ontologies and data, our technical results are very general and our approach is applicable to first-order knowledge representation languages that can be captured by rules allowing for existential quantification and disjunction in the head; our only assumption is the availability of a datalog reasoner and a fully-fledged reasoner for the language of interest, both of which are used as `black boxes'. We have implemented our techniques in the PAGOdA system, which combines the datalog reasoner RDFox and the OWL 2 reasoner HermiT. Our extensive evaluation shows that PAGOdA succeeds in providing scalable pay-as-you-go query answering for a wide range of OWL 2 ontologies, datasets and queries.
Solving #SAT and MAXSAT by Dynamic Programming
Authors: Sæther Sigve Hortemo , Telle Jan Arne , Vatshelle Martin
Published: July 2018

We look at dynamic programming algorithms for propositional model counting, also called #SAT, and MaxSAT. Tools from graph structure theory, in particular treewidth, have been used to successfully identify tractable cases in many subfields of AI, including SAT, Constraint Satisfaction Problems (CSP), Bayesian reasoning, and planning. In this paper we attack #SAT and MaxSAT using similar, but more modern, graph structure tools. The tractable cases will include formulas whose class of incidence graphs have not only unbounded treewidth but also unbounded clique-width. We show that our algorithms extend all previous results for MaxSAT and #SAT achieved by dynamic programming along structural decompositions of the incidence graph of the input formula. We present some limited experimental results, comparing implementations of our algorithms to state-of-the-art #SAT and MaxSAT solvers, as a proof of concept that warrants further research.
Weighted Regret-Based Likelihood: A New Approach to Describing Uncertainty
Authors: Halpern Joseph Y.
Published: July 2018

Recently, Halpern and Leung suggested representing uncertainty by a set of weighted probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event E'. In this paper, a notion of comparative likelihood when uncertainty is represented by a set of weighted probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given.