Shape Space Research Articles

The Pedagogy of Gear Touchers: Unearthing Modes of Teaching Within and Through DIY Venues

Background: Within the body of literature on do-it-yourself (DIY) music scenes, researchers have routinely placed an emphasis on the role of material space in shaping the sociocultural and musical practices of punk music and other related genres. Scholars have also examined the teaching and learning processes of these musical subcultures under the banner of “punk pedagogy” scholarship. However, investigations into the intersection between these two strands of research need to occur because theories of punk pedagogy have largely overlooked the role of physical space within the educative practices of DIY music. Research into the thematically related space of the maker movement amplifies this need, because maker education scholars have repeatedly shown the multiple ways that materials and space shape how individuals learn through DIY production. Research Questions: I use this paper to attend to the following questions: How do DIY music venues shape the pedagogical practices of DIY music scenes? And in what ways do those pedagogies align with the ideological and ethical aims of these communities? By focusing on learning within DIY venues, I consider multiple forms of musical production outside of the context of a specific genre (i.e., punk). This study therefore provides insight into the mechanisms through which individuals learn and how those mechanisms relate to the physical affordances of these spaces. Research Design: To address these questions, I conducted a year-long comparative case study into two intertwined music series centered on noise music (an experimental subgenre within DIY music’s broad umbrella) and located in two separate DIY venues. Although each of the 13 events in this series included both a workshop and a concert, I focus my analysis on the concert portion of the series to explore a common site of interaction within DIY scenes. Through open and iterative qualitative analyses of field notes generated from observations of concerts in the series and interviews with featured artists and audience members, I provide a nuanced understanding of learning within DIY music venues and the role that both material space and technologies play in shaping that process. Conclusions: Drawing on this analysis, I contend that the stageless design of DIY venues provides a physical affordance that allows “gear toucher conversations” to occur. These conversations involve audience members engaging performers in discussions about the music technologies they use mere seconds after they finish performing, thus linking this pedagogical moment to the material attributes of the venue. However, these conversations reinscribe masculine notions of technology and undermine DIY music’s egalitarian politics, a finding that mirrors critical research into maker education. This work therefore calls on both researchers and practitioners to contend with the pedagogies of place and the educative processes that emerge out of situated technologies to further the liberatory praxes that DIY production can produce.

Response of Prolyl 4 Hydroxylases, Arabinogalactan Proteins and Homogalacturonans in Four Olive Cultivars under Long-Term Salinity Stress in Relation to Physiological and Morphological Changes.

Olive (Olea europeae L.) salinity stress induces responses at morphological, physiological and molecular levels, affecting plant productivity. Four olive cultivars with differential tolerance to salt were grown under saline conditions in long barrels for regular root growth to mimic field conditions. Arvanitolia and Lefkolia were previously reported as tolerant to salinity, and Koroneiki and Gaidourelia were characterized as sensitive, exhibiting a decrease in leaf length and leaf area index after 90 days of salinity. Prolyl 4-hydroxylases (P4Hs) hydroxylate cell wall glycoproteins such as arabinogalactan proteins (AGPs). The expression patterns of P4Hs and AGPs under saline conditions showed cultivar-dependent differences in leaves and roots. In the tolerant cultivars, no changes in OeP4H and OeAGP mRNAs were observed, while in the sensitive cultivars, the majority of OeP4Hs and OeAGPs were upregulated in leaves. Immunodetection showed that the AGP signal intensity and the cortical cell size, shape and intercellular spaces under saline conditions were similar to the control in Arvanitolia, while in Koroneiki, a weak AGP signal was associated with irregular cells and intercellular spaces, leading to aerenchyma formation after 45 days of NaCl treatment. Moreover, the acceleration of endodermal development and the formation of exodermal and cortical cells with thickened cell walls were observed, and an overall decrease in the abundance of cell wall homogalacturonans was detected in salt-treated roots. In conclusion, Arvanitolia and Lefkolia exhibited the highest adaptive capacity to salinity, indicating that their use as rootstocks might provide increased tolerance to irrigation with saline water.

Shape correspondence for cel animation based on a shape association graph and spectral matching

We present an effective spectral matching method based on a shape association graph for finding region correspondences between two cel animation keyframes. We formulate the correspondence problem as an adapted quadratic assignment problem, which comprehensively considers both the intrinsic geometric and topology of regions to find the globally optimal correspondence. To simultaneously represent the geometric and topological similarities between regions, we propose a shape association graph (SAG), whose node attributes indicate the geometric distance between regions, and whose edge attributes indicate the topological distance between combined region pairs. We convert topological distance to geometric distance between geometric objects with topological features of the pairs, and introduce Kendall shape space to calculate the intrinsic geometric distance. By utilizing the spectral properties of the affinity matrix induced by the SAG, our approach can efficiently extract globally optimal region correspondences, even if shapes have inconsistent topology and severe deformation. It is also robust to shapes undergoing similarity transformations, and compatible with parallel computing techniques.

BARC: Breed-Augmented Regression Using Classification for 3D Dog Reconstruction from Images

The goal of this work is to reconstruct 3D dogs from monocular images. We take a model-based approach, where we estimate the shape and pose parameters of a 3D articulated shape model for dogs. We consider dogs as they constitute a challenging problem, given they are highly articulated and come in a variety of shapes and appearances. Recent work has considered a similar task using the multi-animal SMAL model, with additional limb scale parameters, obtaining reconstructions that are limited in terms of realism. Like previous work, we observe that the original SMAL model is not expressive enough to represent dogs of many different breeds. Moreover, we make the hypothesis that the supervision signal used to train the network, that is 2D keypoints and silhouettes, is not sufficient to learn a regressor that can distinguish between the large variety of dog breeds. We therefore go beyond previous work in two important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. Second, we formulate novel losses that exploit information about dog breeds. In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss, consisting of two parts: One term is a triplet loss, that encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one is a breed classification loss. With our approach we obtain 3D dogs that, compared to previous work, are quantitatively better in terms of 2D reconstruction, and significantly better according to subjective and quantitative 3D evaluations. Our work shows that a-priori side information about similarity of shape and appearance, as provided by breed labels, can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. We call our method BARC (Breed-Augmented Regression using Classification). Our code is publicly available for research purposes at https://barc.is.tue.mpg.de/.

The role of social networks in academic discourse socialization: insights from degree-seeking multilingual international students in China

Abstract This study adopted a social network perspective to explore the academic discourse socialization experiences of eight degree-seeking multilingual international students at a university in eastern China. Based on a triangulation of ethnographic interviews, social network questionnaires, and supplementary sources (e.g., voluntarily submitted recordings, texts about academic exchanges), the study revealed five patterns of students’ social networks, including heterogeneous-sparse network, heterogeneous-dense network, homogeneous-sparse network, homogeneous-dense network, and balanced network. This resulting network typology was utilized to interpret the role of social networks in individuals’ socialization trajectories, which were observed to include affecting capacities to negotiate academic norms, structuring channels to build and transform expertise, and shaping space for multicompetence development. While different network connections demonstrated different roles, networks with similar characteristics could exert divergent impacts, highlighting the mediation of a range of individual and sociocultural dynamics. Based on the findings, the study contributes to critical multilingual studies by offering theoretical implications for socialization research on community and competence, and providing practical suggestions for research, education, and program administration in international education.

Effects of large-scale disturbance on animal space use: Functional responses by greater sage-grouse after megafire.

Global change has altered the nature of disturbance regimes, and megafire events are increasingly common. Megafires result in immediate changes to habitat available to terrestrial wildlife over broad landscapes, yet we know surprisingly little about how such changes shape space use of sensitive species in habitat that remains. Functional responses provide a framework for understanding and predicting changes in space use following habitat alteration, but no previous studies have assessed functional responses as a consequence of megafire. We studied space use and tested for functional responses in habitat use by breeding greater sage-grouse (Centrocercus urophasianus) before and after landscape-level changes induced by a >40,000 ha, high-intensity megafire that burned sagebrush steppe in eastern Idaho, USA. We also incorporated functional responses into predictive resource selection functions (RSFs) to map breeding habitat before and after the fire. Megafire had strong effects on the distribution of available resources and resulted in context-dependent habitat use that was heterogeneous across different components of habitat. We observed functional responses in the use and selection of a variety of resources (shrubs and herbaceous vegetation) for both nesting and brood rearing. Functional responses in the use of nesting habitat were influenced by the overarching effect of megafire on vegetation, whereas responses during brood rearing appeared to be driven by individual variation in available resources that were conditional on nest locations. Importantly, RSFs built using data collected prior to the burn also had poor transferability for predicting space use in a post-megafire landscape. These results have strong implications for understanding and predicting how animals respond to a rapidly changing environment, given that increased severity, frequency, and extent of wildfire are consequences of global change with the capacity to reshape ecosystems. We therefore demonstrate a conceptual framework to better understand space use and aid habitat conservation for wildlife in a rapidly changing world.

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos.

Caricature is a kind of artistic style of human faces that attracts considerable attention in entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model that generates high-quality 3D caricature directly from a simple normal face photo. The most challenging issue in our system is that the source domain of face photos (characterized by 2D normal faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and texture). To address this challenge, we (1) build a large dataset of 6,100 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space, (2) reconstruct a 3D normal full head from the input face photo and use its PCA representation in the 3D caricature shape space to set up correspondence between the input photo and 3D caricature shape, and (3) propose a novel character loss and a novel caricature loss based on previous psychological studies on caricatures. Experiments including a novel two-level user study show that our system can generate high-quality 3D caricatures directly from normal face photos.

Experience Grammar: Creative Space Planning with Generative Graph and Shape for Early Design Stage

This paper presents a method to synthesise functional relationships and spatial configuration simultaneously using shape and graph computation from shape grammar and space syntax theories. The study revisits seminal works and summarises the compatibilities between shape and graph computation as a set of rules. The rule computation is demonstrated in two cases from hospitality and retail, where current applications, opportunities, and limitations are discussed. The results from the study show that incorporating graph and shape rules allows sequences of functions and spatial arrangements to be developed in parallel. The method could help the designer anticipate the impact on the users’ flow of activities more explicitly during the early design process and could also assist in generating new functional configurations to provide alternative spatial strategies in broader applications.

De novo design of small beta barrel proteins

Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.

Semiparametric Multinomial Logistic Regression Model to Classify‎ ‎Shape Data

This article introduces a semiparametric multinomial logistic regression model to classify labeled configurations. In the regression model, the explanatory variable is the kernel function obtained using the power-divergence criterion. Also, the response variable was categorical and showed the class of each configuration. This semiparametric regression model is introduced based on distances defined in the shape space, and for this reason, the correct classification of shapes using this method has been improved compared to previous methods. ‎The performance of this model has been investigated in the comprehensive simulation study‎. ‎Two real datasets were analyzed using this article's method as an application‎. ‎Finally‎, ‎the method presented in this article was compared with the techniques introduced in the literature‎, ‎which shows the proper performance of this method in classifying configurations‎.

통계적 신체 외형 데이터베이스를 활용한 실루엣으로부터의 3차원 인체 외형 예측

In this paper, we present a method for estimating full 3D shapes from given 2D silhouette images of human bodies. Because the silhouette only consists of the partial information on the true shape, it is an ill-posed problem. To address the problem, we use the statistical human shape space obtained from the existing large 3D human shape database. The method consists of three steps. First, we extract the boundary pixels and their appropriate normal vectors from the input silhouette images. Then, we initialize the correspondences of each pixel to the vertex of the statistically-deformable 3D human model. Finally, we numerically optimize the parameters of the statistical model to fit best to the given silhouettes. The viability and the robustness of the method is demonstrated with various experiments.

Vamos a caminar: utilizing a Chicana/Latina feminist walking plática methodology to honor the brown body and facultad

Following the tradition of Chicana/Latina feminist nuevas teorias and methodologies, I offer walking pláticas as a qualitative research methodology that honors the brown body and facultad to examine our relationship to the spaces we traverse, live within and mutually shape. Walking pláticas is a reclamation of research methodologies that dismantle the white colonial gaze through a recentering of the brown body as a critical corporeal, political, and geographic site of analysis. In this article, I propose to move beyond the understandings of space as the neutral and apolitical backdrop on which life occurs. The strengths of walking pláticas include the consideration of spatiality as key to exploring how people make sense of, are influenced by, and shape space—a great methodological tool for critical educational researchers seeking to understand the role of geography and the physical and social places and spaces on Communities of Color.

“You don't go there”: Spatial Strategies of Stigma Negotiation in a Post‐Industrial Town

AbstractThis paper draws on a multimethod ethnographic study, conducted between 2016 and 2017 in Shirebrook, Derbyshire, England—a small and relatively isolated deindustrialising colliery town—examining how residents negotiate living in stigmatised territory. In doing so, microspatial strategies of distancing, avoidance, and deflection are illustrated, revealing how residents reassign and deepen stigma in particular locations within a stigmatised territory. This highlights the relationship between social and physical space, and while spatial strategies of negotiation do not mitigate stigma, they do (re)produce internal social hierarchies within a place that is homogenised from the outside through disparaging narratives. A key contribution reveals the significance of the racialised production of space in shaping how territorial stigma is negotiated within this distinct socio‐spatial location. Residents use strategies to redirect the stigma toward those seen as out of place and draw attention away from sticky sites of racialised urban stigma towards symbols of unspoilt rural Englishness.

Extracting statistical information about shapes in the visual environment

It is well known that observers can use so-called summary statistics of visual ensembles to simplify perceptual processing. The assumption has been that instead of representing feature distributions in detail the visual system extracts the mean and variance of visual ensembles. But recent evidence from implicit testing using a method called feature distribution learning showed that far more detail of the distributions is retained than the summary statistic literature indicates. Observers also encode higher-order statistics such as the kurtosis of feature distributions of orientation and color. But this sort of learning has not been shown for more intricate aspects of visual information. Here we tested the learning of distractor ensembles for shape, using the feature distribution learning method. Using a linearized circular shape space, we found that learning of detailed distributions of shape does not occur for this shape space while observers were able to learn the mean and range of the distributions. Previous demonstrations of feature distribution learning involved simpler feature dimensions than the more complex shape space tested here, and our findings may therefore reveal important boundary conditions of feature distribution learning.

Analysis and Control of Acoustic Modes in Cylindrical Cavities with application to Direct Field Acoustic Noise (DFAN) Testing.

It is well known that acoustic cavities have frequencies at which certain free-response 'modes' of propagation respond especially strongly. In the absence of significant damping, these cause peaks of high SPL in the frequency response as well as spatial non-uniformity and temporal ringing. The spatial non-uniformity is especially problematic since it means the room cannot be 'EQ'd' to compensate, since the SPL is different in different positions. This phenomenon has been studied extensively in the room acoustics literature and various strategies for mitigation proposed. Many of these make use of the theoretical mode shapes for a cuboid with a rigid boundary condition, since this is a common shape of room and a reasonable approximation for a solidly constructed wall. But modes exist for other shape spaces too. Of particular interest is the cylindrical cavity that is formed when large enclosing arrays of loudspeakers are used to perform high-intensity acoustic tests on space hardware. These possess problematic modes that can cause over-testing in some positions and under-testing in others. In this work, it is investigated how a simple FEM simulation can compute Q-factors for these modes and identify which will be problematic. How this might inform control system design is discussed.

BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs

We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies differs substantially from that of adults, baby-specific facial reconstruction systems are needed. BabyNet consists of two stages: 1) a 3D graph convolutional autoencoder learns a latent space of the baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D latent space based on representative features extracted using transfer learning. In this way, using the pre-trained 3D decoder, we can recover a 3D face from 2D images. We evaluate BabyNet and show that 1) methods based on adult datasets cannot model the 3D facial geometry of babies, which proves the need for a baby-specific method, and 2) BabyNet outperforms classical model-fitting methods even when a baby-specific 3D morphable model, such as BabyFM, is used.

4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data.

We propose a novel framework to learn the spatiotemporal variability in longitudinal 3D shape data sets, which contain observations of objects that evolve and deform over time. This problem is challenging since surfaces come with arbitrary parameterizations and thus, they need to be spatially registered. Also, different deforming objects, hereinafter referred to as 4D surfaces, evolve at different speeds and thus they need to be temporally aligned. We solve this spatiotemporal registration problem using a Riemannian approach. We treat a 3D surface as a point in a shape space equipped with an elastic Riemannian metric that measures the amount of bending and stretching that the surfaces undergo. A 4D surface can then be seen as a trajectory in this space. With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold. However, performing the spatiotemporal registration, and subsequently computing statistics, on such nonlinear spaces is not straightforward as they rely on complex nonlinear optimizations. Our core contribution is the mapping of the surfaces to the space of Square-Root Normal Fields (SRNF) where the [Formula: see text] metric is equivalent to the partial elastic metric in the space of surfaces. Thus, by solving the spatial registration in the SRNF space, the problem of analyzing 4D surfaces becomes the problem of analyzing trajectories embedded in the SRNF space, which has a euclidean structure. In this paper, we develop the building blocks that enable such analysis. These include: (1) the spatiotemporal registration of arbitrarily parameterized 4D surfaces even in the presence of large elastic deformations and large variations in their execution rates; (2) the computation of geodesics between 4D surfaces; (3) the computation of statistical summaries, such as means and modes of variation, of collections of 4D surfaces; and (4) the synthesis of random 4D surfaces. We demonstrate the performance of the proposed framework using 4D facial surfaces and 4D human body shapes.

Comparison of 3D quantitative osteoarthritis imaging biomarkers from paired CT and MR images: data from the IMI-APPROACH study

IntroductionMRI bone surface area and femoral bone shape (B-score) measures have been employed as quantitative endpoints in DMOAD clinical trials. Computerized Tomography (CT) imaging is more commonly used for 3D visualization of bony anatomy due to its high bone-soft tissue contrast. We aimed to compare CT and MRI assessments of 3D imaging biomarkers.MethodsWe used baseline and 24-month image data from the IMI-APPROACH 2-year prospective cohort study. Femur and tibia were automatically segmented using active appearance models, a machine-learning method, to measure 3D bone shape, area and 3D joint space width (3DJSW). Linear regression was used to test for correlation between measures. Limits of agreement and bias were tested using Bland-Altman analysis.ResultsCT-MR pairs of the same knee were available from 434 participants (78% female). B-scores from CT and MR were strongly correlated (CCC = 0.967) with minimal bias of 0.1 (SDD = 0.227). Area measures were also correlated but showed a consistent bias (MR smaller). 3DJSW showed different biases (MR larger) in both lateral and medial compartments.DiscussionThe strong correlation and small B-score bias suggests that B-score may be measured reliably using either modality. It is likely that the bone surface identified using MR and CT will be at slightly different positions within the bone/cartilage boundary. The negative bone area bias suggests the MR bone boundary is inside the CT boundary producing smaller areas for MR, consistent with the positive 3DJSW bias. The lateral-medial 3DJSW difference is possibly due to a difference in knee pose during acquisition (extended for CT, flexed for MR).Trial registrationNCT03883568

Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics.

The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit.

Significance of Sandwich Panel’s Core and Design on Its Impact Resistance under Blast Load

Extensive research is conducted on enhancing the blast mitigation performance of the sandwich panels by examining different design parameters, and core geometries. Nevertheless, there is no direct comparison between those alternatives to evaluate their contribution to maximizing energy absorption. In this research, three core designs honeycomb, mushroom, and tubular were compared to determine the influence of core shape on the panel’s impact resistance against blast load. In addition to varying core shapes, the effect of plate thickness and the spacing between the core shapes are also examined. Finite element analysis was used to evaluate the performance of these designs. Twenty-seven numerical experiments were performed and then analyzed using regression analysis. Results reveal that the tubular sandwich panel exhibited minimum deformation, and least damage and contributed to the highest kinetic energy dissipation. On the other hand, honeycomb core structures recorded the highest internal energy dissipation, largest deformation, and damage. Despite those differences, core shape and core spacing were not as influential in resisting blast load compared to plate thickness. Facade plate thickness was the most significant factor. Results suggest that more research needs to be targeted toward enhancing façade plate stiffness for better mitigation of blast load.