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1. Mimicking Complexity of Structured Data Matrix’s Information Content: Categorical Exploratory Data Analysis

   https://doi.org/10.3390/e23050594  

   Hsieh, Fushing ; Chou, Elizabeth P. ; Chen, Ting-Li ( May 2021 , Entropy)

   null (Ed.)

   We develop Categorical Exploratory Data Analysis (CEDA) with mimicking to explore and exhibit the complexity of information content that is contained within any data matrix: categorical, discrete, or continuous. Such complexity is shown through visible and explainable serial multiscale structural dependency with heterogeneity. CEDA is developed upon all features’ categorical nature via histogram and it is guided by all features’ associative patterns (order-2 dependence) in a mutual conditional entropy matrix. Higher-order structural dependency of k(≥3) features is exhibited through block patterns within heatmaps that are constructed by permuting contingency-kD-lattices of counts. By growing k, the resultant heatmap series contains global and large scales of structural dependency that constitute the data matrix’s information content. When involving continuous features, the principal component analysis (PCA) extracts fine-scale information content from each block in the final heatmap. Our mimicking protocol coherently simulates this heatmap series by preserving global-to-fine scales structural dependency. Upon every step of mimicking process, each accepted simulated heatmap is subject to constraints with respect to all of the reliable observed categorical patterns. For reliability and robustness in sciences, CEDA with mimicking enhances data visualization by revealing deterministic and stochastic structures within each scale-specific structural dependency. For inferences in Machine Learning (ML) and Statistics, it clarifies, upon which scales, which covariate feature-groups have major-vs.-minor predictive powers on response features. For the social justice of Artificial Intelligence (AI) products, it checks whether a data matrix incompletely prescribes the targeted system. 

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2. Spatiotemporal patterns of lion ( Panthera leo ) space use in a human–wildlife system

   https://doi.org/10.1002/2688-8319.12276  

   Burak, Mary K. ; Broekhuis, Femke ; Dickman, Amy ; Ekwanga, Steven ; Elliot, Nicholas ; Frank, Laurence ; Oriol‐Cotterill, Alayne ; Williams, Terrie M. ; Wilmers, Christopher C. ; Schmitz, Oswald ( September 2023 , Ecological Solutions and Evidence)

   Conserving large carnivores requires protecting landscape spaces that encompass all spatiotemporal scales of their movement. Large carnivores normally roam widely, but habitat loss and fragmentation can constrain their movement in ways that restrict access to resources and increase encounters with humans and potential conflict. Facilitating carnivore population coexistence with humans across landscapes requires conservation plans informed by patterns of carnivore space use, particularly at the human–wildlife interface.

   We sought to understand lion space use in Laikipia, Kenya. We conducted a path‐selection function analysis using GPS collar data from 16 lions to assess patterns of space use across a range of spatial scales (sedentary to home range expanses; 0, 12.5, 25 and 50 km) and temporal scales (day, dusk, night and dawn). Path‐selection results were then incorporated into space use maps.

   We found that most landscape features influenced path‐selection at the broadest spatial scale (50 km), representative of home range‐wide movement, thereby demonstrating a landscape‐wide human impact on lion space use. We also detected sub‐diurnal variation in lion path‐selection which revealed limited space use during daylight hours and increased space use overnight.

   Our results highlight that optimal support for human–lion coexistence should be temporally adaptive at sub‐diurnal scales. Furthermore, spatial approaches to lion conservation may be better generalized at broad spatial scales so that land management plans can account for home range patterns in lion space use.

    

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3. Topological data analysis of zebrafish patterns

   https://doi.org/10.1073/pnas.1917763117  

   McGuirl, Melissa R. ; Volkening, Alexandria ; Sandstede, Björn ( March 2020 , Proceedings of the National Academy of Sciences)

   Self-organized pattern behavior is ubiquitous throughout nature, from fish schooling to collective cell dynamics during organism development. Qualitatively these patterns display impressive consistency, yet variability inevitably exists within pattern-forming systems on both microscopic and macroscopic scales. Quantifying variability and measuring pattern features can inform the underlying agent interactions and allow for predictive analyses. Nevertheless, current methods for analyzing patterns that arise from collective behavior capture only macroscopic features or rely on either manual inspection or smoothing algorithms that lose the underlying agent-based nature of the data. Here we introduce methods based on topological data analysis and interpretable machine learning for quantifying both agent-level features and global pattern attributes on a large scale. Because the zebrafish is a model organism for skin pattern formation, we focus specifically on analyzing its skin patterns as a means of illustrating our approach. Using a recent agent-based model, we simulate thousands of wild-type and mutant zebrafish patterns and apply our methodology to better understand pattern variability in zebrafish. Our methodology is able to quantify the differential impact of stochasticity in cell interactions on wild-type and mutant patterns, and we use our methods to predict stripe and spot statistics as a function of varying cellular communication. Our work provides an approach to automatically quantifying biological patterns and analyzing agent-based dynamics so that we can now answer critical questions in pattern formation at a much larger scale. 

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4. Wing pattern diversity in Eunica butterflies (Nymphalidae: Biblidinae): phylogenetic analysis implies decoupled adaptive trends in dorsal sexual dimorphism and ventral eyespot evolution

   https://doi.org/10.1111/cla.12556  

   Garzón‐Orduña, Ivonne J. ; Silva‐Brandão, Karina Lucas ; Willmott, Keith ; Freitas, André V. L. ; Wahlberg, Niklas ; Brower, Andrew V. Z. ( September 2023 , Cladistics)

   Butterfly eyespots are wing patterns reminiscent of vertebrate eyes, formed by concentric rings of contrastingly coloured scales. Eyespots are usually located close to the wing margin and often regarded as the single most conspicuous pattern element of butterfly wing colour displays. Recent efforts to understand the processes involved in the formation of eyespots have been driven mainly by evo‐devo approaches focused on model species. However, patterns of change implied by phylogenetic relationships can also inform hypotheses about the underlying developmental mechanisms associated with the formation or disappearance of eyespots, and the limits of phenotypic diversity occurring in nature. Here we present a combined evidence phylogenetic hypothesis for the genusEunica, a prominent member of diverse Neotropical butterfly communities, that features notable variation among species in eyespot patterns on the ventral hind wing surface. The data matrix consists of one mitochondrial gene region (COI), four nuclear gene regions (GAPDH, RPS5, EF1a and Wingless) and 68 morphological characters. A combined cladistic analysis with all the characters concatenated produced a single most parsimonious tree that, although fully resolved, includes many nodes with modest branch support. The phylogenetic hypothesis presented corroborates a previously proposed morphological trend leading to the loss of eyespots, together with an increase in the size of the conserved eyespots, relative to outgroup taxa. Furthermore, wing colour pattern dimorphism and the presence of androconia suggest that the most remarkable instances of sexual dimorphism are present in the species ofEunicawith the most derived eyespot patterns, and are in most cases accompanied by autapomorphic combinations of scent scales and “hair pencils”. We discuss natural and sexual selection as potential adaptive explanations for dorsal and ventral wing patterns.

    

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5. Multiresolution Quality Inspection of Layerwise Builds for Metal 3D Printer and Scanner

   https://doi.org/10.1115/1.4057013  

   Yang, Hui ; Reijonen, Joni ; Revuelta, Alejandro ( October 2023 , Journal of Manufacturing Science and Engineering)

   Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.

    

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