Learning relational dynamics of stochastic domains for planning

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft

Learning relational dynamics of stochastic domains for planning

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft

Auxiliary variables for the mapping of the drainage network: spatial correlation between relieve units, lithotypes and springs in Benevente River basin-ES

Processos de mapeamento da rede de drenagem têm limitações metodológicas que resultam em imprecisões e restringem seu uso em estudos ambientais. Tais problemas demandam extensos levantamentos de campo e a busca de variáveis auxiliares que otimizem esses trabalhos, permitindo a análise da acurácia dos ma- pas produzidos. Esta pesquisa mediu a correlação entre nascentes, litotipos e uni- dades de revelo, caracterizadas pelo índice de concentração da rugosidade (ICR) na bacia hidrográfica do rio Benevente-ES, concentrando-se nas operações de álgebra de mapa e na aplicação de técnicas de estatística espacial. Os proce- dimentos adotados identificaram as classes de ICR e litotipos que apresentam maior e menor correlação com a distribuição espacial das nascentes, indicando seu potencial de uso como variáveis auxiliares para a verificação da acurácia das bases cartográficas.Process of the drainage network mapping present methodological limitations re- sulting in inaccurate maps, restricting their use in environmental studies. Such problems demand the realization of long field surveys to verify the error and the search for auxiliary variables to optimize this works and turn possible the analysis of map accuracy. This research aims at the measurement of the correlation be- tween springs, lithotypes and relieve units, characterized by Roughness Concentration Index (RCI) in River Basin Benevente-ES, focusing on the operations of map algebra and the use of spatial statistical techniques. These procedures have identified classes of RCI and lithotypes that present the highest and the lowest correlation with the spatial distribution of springs, indicating its potential use as auxiliary variables to verify the map accuracy

Symbolic AI for XAI: Evaluating LFIT inductive programming for explaining biases in machine learning

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicityThis work was supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN(H2020-MSCA-ITN-2019-860813), IDEA-FAST (IMI2-2018-15-853981), BIBECA(RTI2018-101248-B-I00MINECO/FEDER), RTI2018-095232-B-C22MINECO, PLeNTaS project PID2019-111430RBI00MINECO; and also by Pays de la Loire Region through RFI Atlanstic 202

Comparison of Parent Questionnaires, Examiner-Led Assessment and Parents' Concerns at 14 Months of Age as Indicators of Later Diagnosis of Autism.

Parents participating in a prospective longitudinal study of infants with older siblings with autism completed an autism screening questionnaire and were asked about any concerns relating to their child's development, and children were administered an interactive assessment conducted by a researcher at 14 months. Scores on the parent questionnaire were highest for children later diagnosed with autism. Parental concerns and scores from the examiner-led assessment distinguished children with later developmental difficulties (both autism and other developmental atypicalities) from those who were developing typically. Children about whom parents expressed concern scored higher on both the questionnaire and the interactive assessment than those without concerns. There were no significant associations between total or individual item scores from the questionnaire and interactive assessment

Antibiotic free selection for the high level biosynthesis of a silk-elastin-like protein

Silk-elastin-like proteins (SELPs) are a family of genetically engineered recombinant protein polymers exhibiting mechanical and biological properties suited for a wide range of applications in the biomedicine and materials fields. They are being explored as the next generation of biomaterials but low productivities and use of antibiotics during production undermine their economic viability and safety. We have developed an industrially relevant, scalable, fed-batch process for the high level production of a novel SELP in E. coli in which the commonly used antibiotic selection marker of the expression vector is exchanged for a post segregational suicide system, the separate-component-stabilisation system (SCS). SCS significantly augments SELP productivity but also enhances the product safety profile and reduces process costs by eliminating the use of antibiotics. Plasmid content increased following induction but no significant differences in plasmid levels were discerned when using SCS or the antibiotic selection markers under the controlled fed-batch conditions employed. It is suggested that the absence of competing plasmid-free cells improves host cell viability and enables increased productivity with SCS. With the process developed, 12.8 g L(-1) purified SELP was obtained, this is the highest SELP productivity reported to date and clearly demonstrates the commercial viability of these promising polymers.This work was financed by the European Commission via the 7th Framework Programme Project EcoPlast (FP7-NMP-2009-SME-3), by national funds from the FCT through EXPL/BBB-BIO/1772/2013-FCOMP-010124-FEDER-041595, the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) and a fellowship to SRC (SFRH/BPD/89980/2012), as well as from ERDF through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI). T.C. is supported by the FCT, the European Social Fund, the Programa Operacional Potencial Humano and the Investigador FCT Programme (IF/01635/2014). All the technical staff at the CBMA are thanked for their skilful technical assistance.info:eu-repo/semantics/publishedVersio

Silk-based biomaterials functionalized with fibronectin type II promotes cell adhesion

The objective of this work was to exploit the fibronectin type II (FNII) module from human matrix metalloproteinase-2 as a functional domain for the development of silk-based biopolymer blends that display enhanced cell adhesion properties. The DNA sequence of spider dragline silk protein (6mer) was genetically fused with the FNII coding sequence and expressed in Escherichia coli. The chimeric protein 6mer + FNII was purified by non-chromatographic methods. Films prepared from 6mer + FNII by solvent casting promoted only limited cell adhesion of human skin fibroblasts. However, the performance of the material in terms of cell adhesion was significantly improved when 6mer + FNII was combined with a silk-elastin-like protein in a concentration-dependent behavior. With this work we describe a novel class of biopolymer that promote cell adhesion and potentially useful as biomaterials for tissue engineering and regenerative medicine. Statement of Significance This work reports the development of biocompatible silk-based composites with enhanced cell adhesion properties suitable for biomedical applications in regenerative medicine. The biocomposites were produced by combining a genetically engineered silk-elastin-like protein with a genetically engineered spider-silk-based polypeptide carrying the three domains of the fibronectin type II module from human metalloproteinase-2. These composites were processed into free-standing films by solvent casting and characterized for their biological behavior. To our knowledge this is the first report of the exploitation of all three FNII domains as a functional domain for the development of bioinspired materials with improved biological performance. The present study highlights the potential of using genetically engineered protein-based composites as a platform for the development of new bioinspired biomaterials.This work was supported by Fundação para a Ciência e Tecnologia (FCT – Portugal) Funded Project “Chimera” (PTDC/EBB-EBI/109093/2008), by FCT/MEC through Portuguese funds (PIDDAC) – PEst-OE/BIA/UI4050/2014, by the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) funded by national funds through the FCT I.P. and by the ERDF through COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI). TC is thankful to the FCT, ESF and POPH for its support through the Investigador FCT Programme (IF/01635/2014). ARibeiro thanks FCT for the SFRH\BPD\98388\2013 grant. AMPereira, RMachado and AdaCosta acknowledge FCT for PD/BD/113811/2015, SFRH-BPD/86470/2012 and SFRH/BD/75882/2011 grants, respectively

Synthetic Protein Biotechnology approaches for the creation of antimicrobial biopolymers

[Excerpt] The spread of antimicrobials resistant microorganisms has triggered the search for new ways to treat infections. In the present work we explored the ABP-CM4 peptide properties from Bombyx mori for the creation of biopolymers with broad antimicrobial activity. An antimicrobial recombinant protein-based polymer (rPBP) was designed by cloning the DNA sequence coding for ABP-CM4 in frame with the N-terminus of the elastin-like recombinamer consisting of 200 repetitions of the pentamer VPAVG, here named A200. [...]This work was supported by FEDER through POFC – COMPETE by FCT through the project PEst-OE/BIA/UI4050/2014. By the Spanish Minister of Economy and Competitiveness (MAT2012-38043-C02-01) and Junta de Castilla y León-JCyL (VA152A12-2 and VA155A12-2), Spain. AC and RM, acknowledge FCT for SFRH/BD/75882/2011 and SFRH-BPD/86470/2012 grants, respectively

Aspectos micro-analíticos da crise econômica de 2008/2009: evidências para os municípios brasileiros

O estudo examina a reação das atividades produtivas dos municípios brasileiros frente à crise do subprime que assolou a economia mundial. Através das informações socioeconômicas destas unidades, identificaram-se os que seguiram a tendência macroeconômica nacional de crise de modo imediato, em 2008, e/ou sua repercussão, em 2009. Por meio do modelo de regressão logit ordenado foram encontradas as probabilidades dos municípios sentirem os efeitos da crise em cada ano. Os resultados apontam que os municípios com maior PIB, população e proporção das atividades de serviços, apresentam maiores chances de sentir o que chamamos de efeito imediato. Já os municípios menores, tanto populacional quanto economicamente, e com maior participação da agropecuária e da administração pública, apresentaram maior probabilidade de sentir o efeito repercussão da crise

Relational reinforcement learning for planning with exogenous effects

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way.Peer ReviewedPostprint (published version