Diffusion of Context and Credit Information in Markovian Models

This paper studies the problem of ergodicity of transition probability matrices in Markovian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from Markov chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm.Comment: See http://www.jair.org/ for any accompanying file

Learning and Interpreting Multi-Multi-Instance Learning Networks

We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as text and image classification, but also supervised learning over graphs. As a further advantage, multi-multi instance learning enables a particular way of interpreting predictions and the decision function. Our approach is based on a special neural network layer, called bag-layer, whose units aggregate bags of inputs of arbitrary size. We prove theoretically that the associated class of functions contains all Boolean functions over sets of sets of instances and we provide empirical evidence that functions of this kind can be actually learned on semi-synthetic datasets. We finally present experiments on text classification, on citation graphs, and social graph data, which show that our model obtains competitive results with respect to accuracy when compared to other approaches such as convolutional networks on graphs, while at the same time it supports a general approach to interpret the learnt model, as well as explain individual predictions.Comment: JML

Shift Aggregate Extract Networks

We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested "part-of-part" relations. Unlike recursive neural networks, which unroll a template on input graphs directly, we unroll a neural network template over the decomposition hierarchy, allowing us to deal with the high degree variability that typically characterize social network graphs. Deep hierarchical decompositions are also amenable to domain compression, a technique that reduces both space and time complexity by exploiting symmetries. We show empirically that our approach is competitive with current state-of-the-art graph classification methods, particularly when dealing with social network datasets

Photo-responsive graphene and carbon nanotubes to control and tackle biological systems

Photo-responsive multifunctional nanomaterials are receiving considerable attention for biological applications because of their unique properties. The functionalization of the surface of carbon nanotubes (CNTs) and graphene, among other carbon based nanomaterials, with molecular switches that exhibit reversible transformations between two or more isomers in response to different kind of external stimuli, such as electromagnetic radiation, temperature and pH, has allowed the control of the optical and electrical properties of the nanomaterial. Light-controlled molecular switches, such as azobenzene and spiropyran, have attracted a lot of attention for nanomaterial's functionalization because of the remote modulation of their physicochemical properties using light stimulus. The enhanced properties of the hybrid materials obtained from the coupling of carbon based nanomaterials with light-responsive switches has enabled the fabrication of smart devices for various biological applications, including drug delivery, bioimaging and nanobiosensors. In this review, we highlight the properties of photo-responsive carbon nanomaterials obtained by the conjugation of CNTs and graphene with azobenzenes and spiropyrans molecules to investigate biological systems, devising possible future directions in the field

A functional perspective on ant biodiversity along environmental gradients in Mediterranean woodlands

As zonas áridas estão a sofrer um aumento no grau de aridez e de pressões antropogénicas, que têm consequências socioeconómicas e ecológicas. As zonas áridas do Mediterrâneo parecem ser particularmente vulneráveis a aumentos de aridez, os quais têm colocado a sua biodiversidade e funcionamento em perigo. Para o estudo e monitorização das mudanças na biodiversidade em resposta a pressões ambientais, climaticas e antropogénicas, é essencial identificar indicadores ecológicos e as melhores métricas que refletem a mudança na diversidade. As formigas são importantes engenheiros do ecossistema e indicadores ecológicos; assim, entender as suas respostas às mudanças ambientais, a par da identificação das melhores métricas que refletem a mudança na diversidade, pode ajudar a prever o funcionamento dos ecossistemas, especialmente no contexto dos hotspots de biodiversidade, como é o caso do ecossistema Mediterrânico. O objetivo desta tese de doutoramento é i) avaliar quais são as variáveis ambientais que atuam em diferentes escalas espaciais sobre a biodiversidade das formigas, tanto ao nível da espécie como das características funcionais e ii) identificar quais são as métricas baseadas na espécie e nas suas características funcionais que são mais adequadas para seguir os efeitos das alterações ambientais em pequena e larga escala na biodiversidade das formigas. Para isso, selecionámos diferentes gradientes no ecossitema Mediterrânico, que incluem um gradiente de micro-escala floresta-prado, um gradiente de aridez espaço-por-tempo e uma sucessão pós-pastoreio. De um modo geral, constatámos que a estrutura local do habitat, as variáveis regionais climaticas e as pressões antropogénicas atuam sobre a biodiversidade das formigas a diferentes escalas, no espaço e no tempo. Em particular, os diferentes gradientes evidenciaram a forte associação entre as comunidades de plantas e de formigas e respetivas diversidades, em diferentes escalas espaciais. Os resultados foram concordantes sobre os impactos negativos de uma espécie invasora na biodiversidade de formigas e num processo do ecossistema, sugerindo que a restauração do ecossistema é necessária para promover a recuperação da biodiversidade de formigas e das funções do ecossistema mediados por este grupo-chave. Em relação às métricas utilizadas, os resultados enfatizam a importância de incluir diferentes abordagens (baseadas em espécies e características funcionais) para avaliar a resposta das formigas às mudanças ambientais. No entanto, os índices baseados em caraterísticas funcionais apresentaram melhor desempenho do que os índices taxonómicos, sugerindo a sua importância como indicadores ecológicos para monitorizar os efeitos das alterações ambientais em pequena e larga escala nas zonas áridas do Mediterrâneo.Drylands are experiencing an increase in aridity and in anthropogenic pressure, which will have socio-economic and ecological consequences. Mediterranean drylands seem to be particularly vulnerable to increases in aridity, which put their functioning and biodiversity at risk. To study and monitor changes in biodiversity in response to these environmental, climatic and anthropogenic pressures, it is essential to identify ecological indicators and the best Biodiversity Change Metrics. Ants are important ecosystem engineers and ecological indicators; thus, understanding their responses to environmental changes while identifying the best Biodiversity Change Metrics may help to forecast ecosystem functioning, especially in the context of biodiversity hotspots, such as the Mediterranean ecosystem. The aim of this PhD thesis is to i) identify which environmental variables act at different scales on ant biodiversity, both at the species and functional trait level and ii) assess if and which species- and trait-based metrics best track the effects of environmental changes at small and large scale on ant biodiversity. To do so, different gradients, which included a micro-scale woodland-grassland gradient, a space-for-time aridity gradient and a post-grazing succession, are selected within the Mediterranean ecosystem. Overall, local habitat structure, regional climatic variables and anthropogenic pressures function as drivers on ant biodiversity at different spatial scales. Findings agree on the negative impacts of an invasive species on ant biodiversity and on an ecosystem process, suggesting that ecosystem restoration is needed to promote recovery of ant biodiversity and ecosystem functions mediated by this key group. With regards to the metrics used, results emphasize the importance to include different approaches (species- and trait-based) to assess the response of ant to environmental changes. In particular, trait-based indices perform better (than species-based indices) suggesting their suitability as ecological indicators to track effects of environmental changes acting at different spatial scales in Mediterranean ecosystems