Unsupervised Learning of Artistic Styles with Archetypal Style Analysis

In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings. Our method is based on archetypal analysis, which is an unsupervised learning technique akin to sparse coding with a geometric interpretation. When applied to deep image representations from a collection of artworks, it learns a dictionary of archetypal styles, which can be easily visualized. After training the model, the style of a new image, which is characterized by local statistics of deep visual features, is approximated by a sparse convex combination of archetypes. This enables us to interpret which archetypal styles are present in the input image, and in which proportion. Finally, our approach allows us to manipulate the coefficients of the latent archetypal decomposition, and achieve various special effects such as style enhancement, transfer, and interpolation between multiple archetypes.Comment: Accepted at NIPS 2018, Montr\'eal, Canad

An application of machine learning to the organization of institutional software repositories

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application

Are fossil groups a challenge of the Cold Dark Matter paradigm?

We study six groups and clusters of galaxies suggested in the literature to be `fossil' systems (i.e. to have luminous diffuse X-ray emission and a magnitude gap of at least 2 mag-R between the first and the second ranked member within half of the virial radius), each having good quality X-ray data and SDSS spectroscopic or photometric coverage out to the virial radius. The poor cluster AWM4 is clearly established as a fossil system, and we confirm the fossil nature of four other systems (RXJ1331.5+1108, RXJ1340.6+4018, RXJ1256.0+2556 and RXJ1416.4+2315), while the cluster RXJ1552.2+2013 is disqualified as fossil system. For all systems we present the luminosity functions within 0.5 and 1 virial radius that are consistent, within the uncertainties, with the universal luminosity function of clusters. For the five bona fide fossil systems, having a mass range 2x10^13-3x10^14 M_Sun, we compute accurate cumulative substructure distribution functions (CSDFs) and compare them with the CSDFs of observed and simulated groups/clusters available in the literature. We demonstrate that the CSDFs of fossil systems are consistent with those of normal observed clusters and do not lack any substructure with respect to simulated galaxy systems in the cosmological LambdaCDM framework. In particular, this holds for the archetype fossil group RXJ1340.6+4018 as well, contrary to earlier claims.Comment: Accepted for publication on MNRAS. Minor changes in sections 2.1 and 6. 13 pages, 4 eps figure

Knowledge-based zonal grid generation for computational fluid dynamics

Automation of flow field zoning in two dimensions is an important step towards reducing the difficulty of three-dimensional grid generation in computational fluid dynamics. Using a knowledge-based approach makes sense, but problems arise which are caused by aspects of zoning involving perception, lack of expert consensus, and design processes. These obstacles are overcome by means of a simple shape and configuration language, a tunable zoning archetype, and a method of assembling plans from selected, predefined subplans. A demonstration system for knowledge-based two-dimensional flow field zoning has been successfully implemented and tested on representative aerodynamic configurations. The results show that this approach can produce flow field zonings that are acceptable to experts with differing evaluation criteria

Automatic definition of engineer archetypes: A text mining approach

With the rapid and continuous advancements in technology, as well as the constantly evolving competences required in the field of engineering, there is a critical need for the harmonization and unification of engineering professional figures or archetypes. The current limitations in tymely defining and updating engineers' archetypes are attributed to the absence of a structured and automated approach for processing educational and occupational data sources that evolve over time. This study aims to enhance the definition of professional figures in engineering by automating archetype definitions through text mining and adopting a more objective and structured methodology based on topic modeling. This will expand the use of archetypes as a common language, bridging the gap between educational and occupational frameworks by providing a unified and up-to-date engineering professional figure tailored to a specific period, specialization type, and level. We validate the automatically defined industrial engineer archetype against our previously manually defined profile

Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data

We study ridge estimation of the precision matrix in the high-dimensional setting where the number of variables is large relative to the sample size. We first review two archetypal ridge estimators and note that their utilized penalties do not coincide with common ridge penalties. Subsequently, starting from a common ridge penalty, analytic expressions are derived for two alternative ridge estimators of the precision matrix. The alternative estimators are compared to the archetypes with regard to eigenvalue shrinkage and risk. The alternatives are also compared to the graphical lasso within the context of graphical modeling. The comparisons may give reason to prefer the proposed alternative estimators

Learning Extremal Representations with Deep Archetypal Analysis

Archetypes are typical population representatives in an extremal sense, where typicality is understood as the most extreme manifestation of a trait or feature. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. However, it might not always be possible to identify meaningful archetypes in a given feature space. Learning an appropriate feature space and identifying suitable archetypes simultaneously addresses this problem. This paper introduces a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a variational autoencoder, and an optimal representation with respect to the unknown archetypes can be learned end-to-end. The reformulation of linear Archetypal Analysis as deep variational information bottleneck, allows the incorporation of arbitrarily complex side information during training. Furthermore, an alternative prior, based on a modified Dirichlet distribution, is proposed. The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. In this experiment, it is demonstrated that exchanging the side information but keeping the same set of molecules, e. g. using as side information the heat capacity of each molecule instead of the band gap energy, will result in the identification of different archetypes. As an application, these learned representations of chemical space might reveal distinct starting points for de novo molecular design.Comment: Under review for publication at the International Journal of Computer Vision (IJCV). Extended version of our GCPR2019 paper "Deep Archetypal Analysis

An Investigation of Semantic Links to Archetypes in an External Clinical Terminology through the Construction of Terminological Shadows

The two-level model based specifications for electronic health record communication EHRcom (ISO 13606) and openEHR both support the embedding of terminological references in Archetypes. This terminological binding can be created manually by a health terminology expert during Archetype design, and the binding is assessed during Archetype evaluation. There has also been some recent work on using lexical queries to generate term sets to represent concepts in Archetypes. This work created an information construct which we call a Terminological Shadow that links Archetype nodes to sets of candidate concepts from a terminology system. The coding scheme used for this work is SNOMED-CT. The proposed Shadows can be used to facilitate the mapping between an Archetype information model and terminological systems. A framework, which also acts as an analysis tool, has been created to construct Shadows from Archetypes. The work also demonstrates how the framework can be used to evaluate different searching algorithms by comparing the search results to the existing bound SNOMED codes