Structure Extraction in Printed Documents Using Neural Approaches

This paper addresses the problem of layout and logical structure extraction from image documents. Two classes of approaches are first studied and discussed in general terms: data-driven and model-driven. In the latter, some specific approaches like rule-based or formal grammar are usually studied on very stereotyped documents providing honest results, while in the former artificial neural networks are often considered for small patterns with good results. Our understanding of these techniques let us to believe that a hybrid model is a more appropriate solution for structure extraction. Based on this standpoint, we proposed a Perceptive Neural Network based approach using a static topology that possesses the characteristics of a dynamic neural network. Thanks to its transparency, it allows a better representation of the model elements and the relationships between the logical and the physical components. Furthermore, it possesses perceptive cycles providing some capacities in data refinement and correction. Tested on several kinds of documents, the results are better than those of a static Multilayer Perceptron

Measuring Energy Expenditure in Sub-Adult and Hatchling Sea Turtles via Accelerometry

Measuring the metabolic of sea turtles is fundamental to understanding their ecology yet the presently available methods are limited. Accelerometry is a relatively new technique for estimating metabolic rate that has shown promise with a number of species but its utility with air-breathing divers is not yet established. The present study undertakes laboratory experiments to investigate whether rate of oxygen uptake (o2) at the surface in active sub-adult green turtles Chelonia mydas and hatchling loggerhead turtles Caretta caretta correlates with overall dynamic body acceleration (ODBA), a derivative of acceleration used as a proxy for metabolic rate. Six green turtles (25–44 kg) and two loggerhead turtles (20 g) were instrumented with tri-axial acceleration logging devices and placed singly into a respirometry chamber. The green turtles were able to submerge freely within a 1.5 m deep tank and the loggerhead turtles were tethered in water 16 cm deep so that they swam at the surface. A significant prediction equation for mean o2 over an hour in a green turtle from measures of ODBA and mean flipper length (R2 = 0.56) returned a mean estimate error across turtles of 8.0%. The range of temperatures used in the green turtle experiments (22–30°C) had only a small effect on o2. A o2-ODBA equation for the loggerhead hatchling data was also significant (R2 = 0.67). Together these data indicate the potential of the accelerometry technique for estimating energy expenditure in sea turtles, which may have important applications in sea turtle diving ecology, and also in conservation such as assessing turtle survival times when trapped underwater in fishing nets

Near-Real-Time Acoustic Monitoring of Beaked Whales and Other Cetaceans Using a Seaglider™

In most areas, estimating the presence and distribution of cryptic marine mammal species, such as beaked whales, is extremely difficult using traditional observational techniques such as ship-based visual line transect surveys. Because acoustic methods permit detection of animals underwater, at night, and in poor weather conditions, passive acoustic observation has been used increasingly often over the last decade to study marine mammal distribution, abundance, and movements, as well as for mitigation of potentially harmful anthropogenic effects. However, there is demand for new, cost-effective tools that allow scientists to monitor areas of interest autonomously with high temporal and spatial resolution in near-real time. Here we describe an autonomous underwater vehicle – a glider – equipped with an acoustic sensor and onboard data processing capabilities to passively scan an area for marine mammals in near-real time. The glider was tested extensively off the west coast of the Island of Hawai'i, USA. The instrument covered approximately 390 km during three weeks at sea and collected a total of 194 h of acoustic data. Detections of beaked whales were successfully reported to shore in near-real time. Manual analysis of the recorded data revealed a high number of vocalizations of delphinids and sperm whales. Furthermore, the glider collected vocalizations of unknown origin very similar to those made by known species of beaked whales. The instrument developed here can be used to cost-effectively screen areas of interest for marine mammals for several months at a time. The near-real-time detection and reporting capabilities of the glider can help to protect marine mammals during potentially harmful anthropogenic activities such as seismic exploration for sub-sea fossil fuels or naval sonar exercises. Furthermore, the glider is capable of under-ice operation, allowing investigation of otherwise inaccessible polar environments that are critical habitats for many endangered marine mammal species

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem