Constrained Distance Based Clustering for Satellite Image Time-Series

International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018

Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents

Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised featurebased models as well as unsupervised models that use manually constructed declarative knowledge. Our results demonstrate that useful declarative knowledge can be learned from data with very limited human involvement.This is the final published version. It first appeared at https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/472

A physiologically based approach to consciousness

The nature of a scientific theory of consciousness is defined by comparison with scientific theories in the physical sciences. The differences between physical, algorithmic and functional complexity are highlighted, and the architecture of a functionally complex electronic system created to relate system operations to device operations is compared with a scientific theory. It is argued that there are two qualitatively different types of functional architecture, and that electronic systems have the instruction architecture based on exchange of unambiguous information between functional components, and biological brains have been constrained by natural selection pressures into the recommendation architecture based on exchange of ambiguous information. The mechanisms by which a recommendation architecture could heuristically define its own functionality are described, and compared with memory in biological brains. Dream sleep is interpreted as the mechanism for minimizing information exchange between functional components in a heuristically defined functional system. The functional role of consciousness of self is discussed, and the route by which the experience of that function described at the psychological level can be related to physiology through a functional architecture is outlined

Cost-optimal constrained correlation clustering via weighted partial Maximum Satisfiability

Peer reviewe

CLP-based protein fragment assembly

The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict the 3D conformation of a protein via fragments assembly. The fragments are extracted by a preprocessor-also developed for this work- from a database of known protein structures that clusters and classifies the fragments according to similarity and frequency. The problem of assembling fragments into a complete conformation is mapped to a constraint solving problem and solved using CLP. The constraint-based model uses a medium discretization degree Ca-side chain centroid protein model that offers efficiency and a good approximation for space filling. The approach adapts existing energy models to the protein representation used and applies a large neighboring search strategy. The results shows the feasibility and efficiency of the method. The declarative nature of the solution allows to include future extensions, e.g., different size fragments for better accuracy.Comment: special issue dedicated to ICLP 201