2,300 research outputs found
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 nÃvel da espécie como das caracterÃsticas funcionais e ii) identificar quais são as métricas baseadas na espécie e nas suas caracterÃsticas 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 à s métricas utilizadas, os resultados enfatizam a importância de incluir diferentes abordagens (baseadas em espécies e caracterÃsticas funcionais) para avaliar a resposta das formigas à s mudanças ambientais. No entanto, os Ãndices baseados em caraterÃsticas funcionais apresentaram melhor desempenho do que os Ãndices 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
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