Archetypal Analysis and DEA Model, Their Application on Financial Data and Visualization with PHATE

One of the goals of macroeconomic analysis is to rank and segment enterprises described by many financial indicators. The segmentation can be used for investment strategies or risk evaluation. The aim of this research was to distinguish groups of similar objects and visualize the results in a low dimensional space. In order to obtain clusters of similar objects, the authors applied a DEA BCC model and archetypal analysis for a set of companies described by financial indicators and listed on the Warsaw Stock Exchange. The authors showed that both methods give consistent results. To get a better insight into the data structure as well as a visualization of the similarities between objects, the authors used a new approach called the PHATE algorithm. It allowed the results of DEA and archetypal analysis to be visualized in a low dimensional space.

The Moldovan film Balada haiducească (Outlaw Ballad): the Phenomenon of Innovation through Tradition

The polemical character of the paper is explained by the need to reconsider the generative factors in the configuration of the new cinematic wave of the “thaw” era. Unlike the popular postulate, the author launches the novel conception: the change in the face of Soviet cinema is entirely due to the ideational-aesthetic performances of the most ideologized genre of historical film, the historical-revolutionary one. Namely in the films, dedicated to the Russian revolution, there happened a reversal of values of the existential equations: “man – history”, “individual – collective”, “general – human-soviet”. Appealing to the generous virtualities of the mythical-archetypal analysis, we specified the change of the vector of cinematographic knowledge in the Balada haiducească/Outlaw Ballad by updating the patterns of the mythical-folkloric complex as the millennial censorship of the nation. Assigning the inspired cinematographic work both to the context of the “thaw” era and to that of the exponential Balkan genre – the film with outlaws, the singularity of the local discourse inscribed in the identity grid of the neo-romantic type was stated

Archetypal Analysis for Population Genetics

The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse, large-scale cohorts. However, existing methods are computationally-intensive, prohibitively so in the case of nationwide biobanks. Here we explore Archetypal Analysis as an efficient, unsupervised approach for identifying genetic clusters and for associating individuals with them. Such unsupervised approaches help avoid conflating socially constructed ethnic labels with genetic clusters by eliminating the need for exogenous training labels. We show that Archetypal Analysis yields similar cluster structure to existing unsupervised methods such as ADMIXTURE and provides interpretative advantages. More importantly, we show that since Archetypal Analysis can be used with lower-dimensional representations of genetic data, significant reductions in computational time and memory requirements are possible. When Archetypal Analysis is run in this fashion, it takes several orders of magnitude less compute time than the current standard, ADMIXTURE. Finally, we demonstrate uses ranging across datasets from humans to canids.

100 years of the Mari fine arts: ethnographic realism (1920–1930)

Introduction. The article is the first in a series of publications dedicated to the 100th anniversary of the Mari autonomy and the fact of the emergence of professional visual arts among the Mari people. The author regards it as a systemic element of national-ethnic culture, which performed the function of ethno-cultural reflection by artistic means throughout the entire century, in which four major stages and corresponding stylistic forms can be traced. The article describes the initial stage of the Mari fine arts of the 1920s – 1930s. Materials and Methods. The main material was the collection of art and ethnographic works of the 1920s–1930 found in the funds of the National Museum of the Republic of Mari El. The author used various methods: historical research, art history analysis of works of art, as well as the author’s own method of structural and archetypal analysis. Results and Discussion. The first art institutions appeared in the mountainous Mari region at the turn of the 1910–1920 thanks to the artist A.V. Grigoriev, who together with his associates later founded the Association of Artists of Revolutionary Russia in Moscow. The systematic institutionalization of the Mari fine arts began in the second half of the 1920, which was facilitated by the activities of the Mari Regional Society of Local History and the Central Mari Museum in the town of Krasnokokshaisk. The founders of the Mari fine arts were the invited artists from Kazan, namely P. A. Radimov, G. A. Medvedev, V. K. Timofeev, M. M. Vasilyeva, the first Mari artists K. F. Egorov and E. D. Atlashkina, and P. G. Gorbuntsov. With the beginning of the “Great Terror” period, the first stage of the Mari art was interrupted, and socialist realism replaced ethnographic realism. Conclusion. The development of the fine art of the Mari at the initial stage was stimulated by the Mari Regional Society of Local History and the Central Mari Museum represented by V. A. Mukhin (Savi), V. M. Vasiliev, T. E. Evseev. Their educational interests, combined with the documentary-oriented program of the Association of Artists of Revolutionary Russia, contributed to the formation of such a stylistic form as ethnographic realism, which became the first artistic form of ethnocultural reflection by the means of fine arts.

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages

Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged.

Non-linear Archetypal Analysis of Single-cell RNA-seq Data by Deep Autoencoders

Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.

The meaning of life and death in the eyes of Frankl: Archetypal and terror management perspectives

This article aims to uncover the meaning of life and death across the lifespan of the extraordinary person, Viktor E. Frankl (1905–1997). Frankl was purposively sampled due to his international acclaim as an Austrian neurologist and psychiatrist, who later became famous as a holocaust survivor and the founder of logotherapy. Through his approach of “healing through meaning,” he became the founder of the meaning-centred school of psychotherapy and published many books on existential and humanistic psychology. The study describes the meaning of life and death through two theoretical approaches: the archetypal analysis based on C.G. Jung’s and C.S. Pearson’s work and a terror management approach based on the melancholic existentialist work of Ernest Becker. The methodology of psychobiography is used to conduct the psycho-historical analysis of the interplay of archetypes and death annihilation anxiety throughout Frankl’s lifespan. The article evaluates how archetypes and death anxiety interacts and how they built meaning in different stages of Frankl’s lifespan. The theories are discussed and illustrated in the light of Viktor E. Frankl’s life.

Estimating Intraclonal Heterogeneity and Subpopulation Changes from Perturbational Bulk Gene Expression Profiles in LINCS L1000 CMap by Premnas

The connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases. However, due to the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections. In this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of searching for drug cocktails and drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined. The proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology. The executable and source code of Premnas is freely available at https://github.com/jhhung/Premnas.