Fewer epistemological challenges for connectionism

Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolensky’s paper 'On the proper treatment of connectionism'. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field

Connectionist modal logic: Representing modalities in neural networks

AbstractModal logics are amongst the most successful applied logical systems. Neural networks were proved to be effective learning systems. In this paper, we propose to combine the strengths of modal logics and neural networks by introducing Connectionist Modal Logics (CML). CML belongs to the domain of neural-symbolic integration, which concerns the application of problem-specific symbolic knowledge within the neurocomputing paradigm. In CML, one may represent, reason or learn modal logics using a neural network. This is achieved by a Modalities Algorithm that translates modal logic programs into neural network ensembles. We show that the translation is sound, i.e. the network ensemble computes a fixed-point meaning of the original modal program, acting as a distributed computational model for modal logic. We also show that the fixed-point computation terminates whenever the modal program is well-behaved. Finally, we validate CML as a computational model for integrated knowledge representation and learning by applying it to a well-known testbed for distributed knowledge representation. This paves the way for a range of applications on integrated knowledge representation and learning, from practical reasoning to evolving multi-agent systems

Argument-based agreements in agent societies

In this paper, we present an abstract argumentation framework for the support of agreement processes in agent societies. It takes into account arguments, attacks among them, and the social context of the agents that put forward arguments. Then, we de¿ne the semantics of the framework, providing a mechanism to evaluate arguments in view of other arguments posed in the argumentation process. We also provide a translation of the framework into a neural network that computes the set of acceptable arguments and can be tuned to give more or less importance to argument attacks. Finally, the framework is illustrated with an example in a real domain of a water-rights transfer market. & 2011 Elsevier B.V. All rights reservedThis work is supported by the Spanish government Grants CONSOLIDER INGENIO 2010 CSD2007-00022, TIN2008-04446 and TIN2009-13839-C03-01 and by the GVA project PROMETEO 2008/051.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2012). Argument-based agreements in agent societies. Neurocomputing. 75(1):156-162. doi:10.1016/j.neucom.2011.02.022S15616275

Accountability in AI: From principles to industry-specific accreditation

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings

Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper

Encoder-decoder architectures are prominent building blocks of state-of-the-art solutions for tasks across multiple fields where deep learning (DL) or foundation models play a key role. Although there is a growing community working on the provision of interpretation for DL models as well as considerable work in the neuro-symbolic community seeking to integrate symbolic representations and DL, many open questions remain around the need for better tools for visualization of the inner workings of DL architectures. In particular, encoder-decoder models offer an exciting opportunity for visualization and editing by humans of the knowledge implicitly represented in model weights. In this work, we explore ways to create an abstraction for segments of the network as a two-way graph-based representation. Changes to this graph structure should be reflected directly in the underlying tensor representations. Such two-way graph representation enables new neuro-symbolic systems by leveraging the pattern recognition capabilities of the encoder-decoder along with symbolic reasoning carried out on the graphs. The approach is expected to produce new ways of interacting with DL models but also to improve performance as a result of the combination of learning and reasoning capabilities

MECANISMOS MOLECULARES NO HIRSUTISMO: EXPRESSÃO GÊNICA DE ENZIMAS DO METABOLISMO ANDROGÊNICO NO FOLÍCULO PILOSO E ASSOCIAÇÃO COM O DIAGNÓSTICO ETIOLÓGICO

Androgens are the main hormonal regulators of human hair growth and they are related to clinical conditions such as hirsutism. The aim of this study was to analyze the gene expression of androgen receptor (AR), type 1 and type 2 5a-reductase isoenzymes (5a R1 and 2) and type 2 17b hydroxysteroid dehydrogenase (17b-HSD 2) in plucked scalp hairs from hirsute patients and normal subjects. We studied 33 women with hirsutism [20 with polycystic ovary syndrome (PCOS), 13 with idiopathic hirsutism (IH)]; 15 control women; and 10 control men. Hirsutism was assessed by a modified Ferriman-Gallwey method. Hormonal status was assessed between days 2 and 10 of the menstrual cycle or on any day when the patients were amenorrheic. AR and enzymes mRNA levels were estimated by reverse transcriptionpolymerase chain reaction (RT-PCR). AR expression was similar in all groups. The gene expression of 5a R2 was not detected in any hair samples analyzed in this study. No differences were found on 5a R1 mRNA levels between men and normal women (0.78 ± 0.05 vs. 0.74 ± 0.06, respectively). 5a R1 gene expression in the plucked hair cells from scalp of normal women (0.85 ± 0.04), PCOS (0.78 ± 0.05) and IH (0.80 ± 0.06) was also similar. 17b-HSD2 gene expression in hirsute patients was lower (2.2±0.13 and 2.0±0.15, for PCOS and IH, respectively) than in normal women (3.1±0.17, p<0.05), and similar to men (1.8±0.22). In conclusion, these results indicate that there are no changes on 5a R1 gene expression in the plucked hair cells from scalp, related to gender or hirsutism. The lower expression of 17b-HSD2 mRNA in scalp hairs of hirsute patients suggests androgen metabolism disturbances with predominance of more potent androgens, as occurs in men.Androgens are the main hormonal regulators of human hair growth and they are related to clinical conditions such as hirsutism. The aim of this study was to analyze the gene expression of androgen receptor (AR), type 1 and type 2 5a-reductase isoenzymes (5a R1 and 2) and type 2 17b hydroxysteroid dehydrogenase (17b-HSD 2) in plucked scalp hairs from hirsute patients and normal subjects. We studied 33 women with hirsutism [20 with polycystic ovary syndrome (PCOS), 13 with idiopathic hirsutism (IH)]; 15 control women; and 10 control men. Hirsutism was assessed by a modified Ferriman-Gallwey method. Hormonal status was assessed between days 2 and 10 of the menstrual cycle or on any day when the patients were amenorrheic. AR and enzymes mRNA levels were estimated by reverse transcriptionpolymerase chain reaction (RT-PCR). AR expression was similar in all groups. The gene expression of 5a R2 was not detected in any hair samples analyzed in this study. No differences were found on 5a R1 mRNA levels between men and normal women (0.78 ± 0.05 vs. 0.74 ± 0.06, respectively). 5a R1 gene expression in the plucked hair cells from scalp of normal women (0.85 ± 0.04), PCOS (0.78 ± 0.05) and IH (0.80 ± 0.06) was also similar. 17b-HSD2 gene expression in hirsute patients was lower (2.2±0.13 and 2.0±0.15, for PCOS and IH, respectively) than in normal women (3.1±0.17, p<0.05), and similar to men (1.8±0.22). In conclusion, these results indicate that there are no changes on 5a R1 gene expression in the plucked hair cells from scalp, related to gender or hirsutism. The lower expression of 17b-HSD2 mRNA in scalp hairs of hirsute patients suggests androgen metabolism disturbances with predominance of more potent androgens, as occurs in men

Identificação do Estado do Trânsito Através da Análise de Aglomerados Utilizando um Classificador Fuzzy

O aumento da frota de veículos tem incentivado o desenvolvimento de sistemas que auxiliem na gestão do tráfego, destacando-se aqueles que utilizam informações inteligentes obtidas através de visão computacional. As propostas atuais apresentam falhas ao utilizarem ou reconhecimento e contagem individual de veículos ou classificadores que erraram em predições quando o estado do trânsito estava em transição. Este artigo apresenta uma proposta de classificação fuzzy do tráfego de veículos utilizando para a estimativa do fluxo a abordagem macroscópica da análise de aglomerados. O classificador apresentou uma acurácia de 89,9%, classificando corretamente vias que apresentaram oclusões e variações climáticas.Palavras-chave: Estado do Trânsito, Aglomerados, Classificador Fuzzy

Quintais Orgânicos de Frutas: diversificação da matriz produtiva e geração de renda familiar.

Quintais Orgânicos de Frutas é uma iniciativa de transferência de tecnologia desenvolvida pela Embrapa Clima Temperado que leva, a públicos em situação de vulnerabilidade social, econômica e alimentar (agricultores familiares, assentados da reforma agrária, comunidades indígenas, quilombolas, alunos de escolas rurais e instituições assistencialistas), as últimas soluções tecnológicas desenvolvidas e validadas pela Embrapa, buscando a sustentabilidade; as práticas compreendem desde o preparo do solo até o pós-colheita

Identificação do Estado do Trânsito Através da Análise de Aglomerados Utilizando um Classificador Fuzzy

O aumento da frota de veículos tem incentivado o desenvolvimento de sistemas que auxiliem na gestão do tráfego, destacando-se aqueles que utilizam informações inteligentes obtidas através de visão computacional. As propostas atuais apresentam falhas ao utilizarem ou reconhecimento e contagem individual de veículos ou classificadores que erraram em predições quando o estado do trânsito estava em transição. Este artigo apresenta uma proposta de classificação fuzzy do tráfego de veículos utilizando para a estimativa do fluxo a abordagem macroscópica da análise de aglomerados. O classificador apresentou uma acurácia de 89,9%, classificando corretamente vias que apresentaram oclusões e variações climáticas.Palavras-chave: Estado do Trânsito, Aglomerados, Classificador Fuzzy

A Genome-Wide Screening and SNPs-to-Genes Approach to Identify Novel Genetic Risk Factors Associated with Frontotemporal Dementia

Frontotemporal dementia (FTD) is the second most prevalent form of early onset dementia after Alzheimer’s disease (AD). We performed a case-control association study in an Italian FTD cohort (n = 530) followed by the novel SNPs-to-genes approach and functional annotation analysis. We identified two novel potential loci for FTD. Suggestive SNPs reached p-values ~10-7 and OR > 2.5 (2p16.3) and 1.5 (17q25.3). Suggestive alleles at 17q25.3 identified a disease-associated haplotype causing decreased expression of -cis genes such as RFNG and AATK involved in neuronal genesis and differentiation, and axon outgrowth, respectively. We replicated this locus through the SNPs-to-genes approach. Our functional annotation analysis indicated significant enrichment for functions of the brain (neuronal genesis, differentiation and maturation), the synapse (neurotransmission and synapse plasticity), and elements of the immune system, the latter supporting our recent international FTD-GWAS. This is the largest genome-wide study in Italian FTD to date. Although our results are not conclusive, we set the basis for future replication studies and identification of susceptible molecular mechanisms involved in FTD pathogenesis