Finding a third archetypal technical system in architectural phenomenology

Within the scope of phenomenology and in order to understand architecture, the role of the technical system is as important as those of the purpose of the building or its form. Mass construction and skeletal construction relate to the architectural theory concepts stereotomy and tectonics respectively, which are suitable for describing the fundamental structural and constructive form of architecture. These two systems became established as man built his first shelters and, so far, represented opposite sides of the building industry’s possibilities. The development of new construction techniques and the relationship between research and technology have a great impact on architecture, although new processing methods and materials may not necessarily cause genuine tectonic changes. The technical dimension of architecture is analysed in this work describing how technical elements are built from materials, and then organised in systems. First, the paper examines the division of technical systems in two categories (massive systems and skeletal systems); then it studies timber’s modern production technologies and subsequently the paper critically analyses how these influence the architectural form. The paper concludes that a third archetypal technical system can be perceived with the assembly of surface elements, joining both the multifunctional aspect of the massive systems and the flexibility of the skeletal systems, this third category being fundamental in phenomenological terms

Weighted and Robust Archetypal Analysis

Archetypal analysis represents observations in a multivariate data set as convex combinations of a few extremal points lying on the boundary of the convex hull. Data points which vary from the majority have great influence on the solution; in fact one outlier can break down the archetype solution. This paper adapts the original algorithm to be a robust M-estimator and presents an iteratively reweighted least squares fitting algorithm. As required first step, the weighted archetypal problem is formulated and solved. The algorithm is demonstrated using both an artificial and a real world example

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

Probabilistic Archetypal Analysis

Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.Comment: 24 pages; added literature review and visualizatio

Does the Soul's sleep generate the Reason? The symbol's compensatory aspect at quantum-psychoid matrix with regard to the Reason's unilateralism. Excerpt by.

A Symbol doesn't explain, says Jung. In fact it is beyond the dichotomy of the binary logic, that wants the limiting and restrictive diktat of the tertium non datur to be perpetuated so as to be obliged to choose between two possibilities being anyway on the same nomological axis

A geometric approach to archetypal analysis and non-negative matrix factorization

Archetypal analysis and non-negative matrix factorization (NMF) are staples in a statisticians toolbox for dimension reduction and exploratory data analysis. We describe a geometric approach to both NMF and archetypal analysis by interpreting both problems as finding extreme points of the data cloud. We also develop and analyze an efficient approach to finding extreme points in high dimensions. For modern massive datasets that are too large to fit on a single machine and must be stored in a distributed setting, our approach makes only a small number of passes over the data. In fact, it is possible to obtain the NMF or perform archetypal analysis with just two passes over the data.Comment: 36 pages, 13 figure

Understanding the Heterogeneity of Contributors in Bug Bounty Programs

Background: While bug bounty programs are not new in software development, an increasing number of companies, as well as open source projects, rely on external parties to perform the security assessment of their software for reward. However, there is relatively little empirical knowledge about the characteristics of bug bounty program contributors. Aim: This paper aims to understand those contributors by highlighting the heterogeneity among them. Method: We analyzed the histories of 82 bug bounty programs and 2,504 distinct bug bounty contributors, and conducted a quantitative and qualitative survey. Results: We found that there are project-specific and non-specific contributors who have different motivations for contributing to the products and organizations. Conclusions: Our findings provide insights to make bug bounty programs better and for further studies of new software development roles.Comment: 6 pages, ESEM 201