Shape Space Research Articles

Procedural Roof Generation From a Single Satellite Image

AbstractUrban procedural modeling has benefited from recent advances in deep learning and computer graphics. However, few, if any, approaches have automatically produced procedural building roof models from a single overhead satellite image. Large‐scale roof modeling is important for a variety of applications in urban content creation and in urban planning (e.g., solar panel planning, heating/cooling/rainfall modeling). While the allure of modeling only from satellite images is clear, unfortunately structures obtained from the satellite images are often in low‐resolution, noisy and heavily occluded, thus getting a clean and complete view of urban structures is difficult. In this paper, we present a framework that exploits the inherent structure present in man‐made buildings and roofs by explicitly identifying the compact space of potential building shapes and roof structures. Then, we utilize this relatively compact space with a two‐component solution combining procedural modeling and deep learning. Specifically, we use a building decomposition component to separate the building into roof parts and predict regularized building footprints in a procedural format, and use a roof ridge detection component to refine the individual roof parts by estimating the procedural roof ridge parameters. Our qualitative and quantitative assessments over multiple satellite datasets show that our method outperforms various state‐of‐the‐art methods.

T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration.

T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding ‘stereotyped’ motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.

How does geometry affect quantum gases?

In this work, we study the thermodynamic functions of quantum gases confined to spaces of various shapes, namely, a sphere, a cylinder and an ellipsoid. We start with the simplest situation, namely, a spinless gas treated within the canonical ensemble framework. As a next step, we consider noninteracting gases (fermions and bosons) with the usage of the grand canonical ensemble description. For this case, the calculations are performed numerically. We also observe that our results may possibly be applied to Bose–Einstein condensate and to helium dimer. Moreover, the bosonic sector, independently of the geometry, acquires entropy and internal energy greater than for the fermionic case. Finally, we also devise a model allowing us to perform analytically the calculations in the case of interacting quantum gases, and, afterwards, we apply it to a cubical box.

Research on mass transport characteristics and net power performance under different flow channel streamlined imitated water-drop block arrangements for proton exchange membrane fuel cell

The flow channel structure affects the fluid flow, gas diffusion, and electrochemical reactions for proton exchange membrane fuel cell (PEMFC). In this work, the flow channel structure is modified to improve the performance of PEMFC by adding streamlined blocks. First, four different imitated water-drop (IWD) blocks installed in the flow channel are designed based on the low resistance and high speed of streamline shape. Then, the different optimal imitated water-drop block arrangement spacing in the flow channel is investigated for PEMFC. Finally, the net power of the flow channel with optimal imitated water-drop shape and arrangement spacing is compared with the basic straight channel and trapezoid channel. Results show that the fourth imitated water-drop block has the highest net power when installed in the flow channel. Furthermore, the mass transport performance is further improved with the block spacing decreasing. The net power of the optimal imitated water-drop block channel with 2 mm block spacing is higher than that of the basic straight channel and the trapezoid channel. Therefore, the streamlined imitated water-drop blocks installed in the flow channel for PEMFC can strengthen the gas transfer capability, accelerate the electrochemical reaction, and obtain the better net power.

Positionality: The Interplay of Space, Context and Identity

This article considers the way in which positionality shifts and is formed during a cross-cultural study to reveal the complexity of the insider-outsider status. As a researcher in a male-dominated game setting, I reflect on the research process and my interactions with participants to show the interplay of space, context, and identity in shaping a researcher’s status. I discuss the process of gaining access to the research site and participants, and data collection in relation to space, context, and identity. The interaction of my identities with space, and context informed my status at various moments. This interplay constructs a complex status of an “in-out-sider”. These findings prepare researchers to pay close attention to the role space, context, and identity play in shaping their positionality. This study serves as a welcome addition to the emerging literature on positionality, and to the situatedness of a researcher status.

Concise and Effective Network for 3D Human Modeling From Orthogonal Silhouettes

Abstract In this article, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i.e., front and side views). Different from our previous work (Wang et al. (2003, “Virtual Human Modeling From Photographs for Garment Industry,” Comput. Aided Des., 35, pp. 577–589).), a supervised learning approach based on the convolutional neural network (CNN) is investigated to solve the problem by establishing a mapping function that can effectively extract features from two silhouettes and fuse them into coefficients in the shape space of human bodies. A new CNN structure is proposed in our work to extract not only the discriminative features of front and side views but also their mixed features for the mapping function. 3D human models with high accuracy are synthesized from coefficients generated by the mapping function. Existing CNN approaches for 3D human modeling usually learn a large number of parameters (from 8.5 M to 355.4 M) from two binary images. Differently, we investigate a new network architecture and conduct the samples on silhouettes as the input. As a consequence, more accurate models can be generated by our network with only 2.4 M coefficients. The training of our network is conducted on samples obtained by augmenting a publicly accessible dataset. Learning transfer by using datasets with a smaller number of scanned models is applied to our network to enable the function of generating results with gender-oriented (or geographical) patterns.

A Hierarchical Geodesic Model for Longitudinal Analysis on Manifolds

In many applications, geodesic hierarchical models are adequate for the study of temporal observations. We employ such a model derived for manifold-valued data to Kendall’s shape space. In particular, instead of the Sasaki metric, we adapt a functional-based metric, which increases the computational efficiency and does not require the implementation of the curvature tensor. We propose the corresponding variational time discretization of geodesics and employ the approach for longitudinal analysis of 2D rat skulls shapes as well as 3D shapes derived from an imaging study on osteoarthritis. Particularly, we perform hypothesis test and estimate the mean trends.

The N-Link Swimmer in Three Dimensions: Controllability and Optimality Results

The controllability of a fully three-dimensional N-link swimmer is studied. After deriving the equations of motion in a low Reynolds number fluid by means of Resistive Force Theory, the controllability of the minimal 2-link swimmer is tackled using techniques from Geometric Control Theory. The shape of the 2-link swimmer is described by two angle parameters. It is shown that the associated vector fields that govern the dynamics generate, via taking their Lie brackets, all eight linearly independent directions in the combined configuration and shape space, leading to controllability; the swimmer can move from any starting configuration and shape to any target configuration and shape by operating on the two shape variables. The result is subsequently extended to the N-link swimmer. Finally, the minimal time optimal control problem and the minimization of the power expended are addressed and a qualitative description of the optimal strategies is provided.

Ecomorphology of the tarsometatarsus of waterfowl (Anseriformes) based on geometric morphometrics and its application to fossils.

Anseriformes is a diverse group of birds that comprises screamers, the Magpie Goose, and swans ducks and geese, with a relatively rich fossil record. Waterfowl live in close relation to water bodies, but show a diversity of locomotory habits, being typically categorized as walkers, dabblers, and divers. Owing to its functional significance and high preservation potential, the tarsometatarsus has been considered to be a "key" element upon which to base ecomorphological inferences in fossil waterfowl. For instance, based on features of the tarsometatarsus the Miocene flightless duck Cayaoa bruneti and the Oligocene-Miocene large waterfowl Paranyroca have been inferred as divers. Herein, we use a geometric morphometric approach and comparative methods to assess the phylogenetic and ecomorphological signals in the shape and size of waterfowl tarsometatarsi in relation to their locomotory habits. We also apply phylogenetic flexible discriminant analysis (pFDA) to test the inferred diving habits in the extinct waterfowl Cayaoa and Paranyroca. Extant waterfowl species are largely distributed according to their locomotory habit along the main axis of variation in the shape space, a pattern mirrored by the phylogenetic generalized least squares model, which shows that a third of the shape variation is significantly explained by the habit. The pFDA reclassifies correctly almost all extant species and classified with high posterior probabilities the fossil Cayaoa and Paranyroca as a diver and as a dabbler, respectively. Our quantitative multivariate approach confirms the tarsometatarsus as a useful source of data upon which reliably assesses locomotory habits of fossil waterfowl.

Sex determination accuracy using the human sacrum in a Central European sample.

The sacrum is often used for sex estimation of human remains. However, reported sexual classification accuracies based on the sacrum produce highly discrepant estimates ranging from 54% to 98% depending on the method and reference sample. Here, we therefore aim to evaluate the sacrum's efficacy in differentiating males from females by comparing three different approaches in a homogeneous Central European sample (n = 58, 27 males and 31 females) obtained from the 19th century Weisbach collection. Specifically, we investigated the sacrum by 1) a qualitative visual inspection, 2) traditional linear metrics, namely, sacral width, corpus width, and the associated corporo-basal index, and 3) geometric morphometrics (GM) using a 3D configuration of 100 landmarks and semilandmarks. Classification accuracies for the qualitative approach ranged from 69% to 81%. The investigated quantitative methods based on linear dimensions led to comparable classification accuracies of 62% for sacral width and 78% for the corporo-basal index. However, absolute corpus width had a stronger discriminative power (86%), similar to the PC scores from shape space (87%). Unexpectedly, the GM approach, which considered the shape of the entire sacrum, did not classify better than linear variables. This was exceeded only when sacrum shape was combined with corpus width or another measure of size, like the natural logarithm of the centroid size, yielding a classification accuracy of 95%. The male and female group means differed mainly in the general height-to-width relationship and corpus-to-alae proportions, corroborating patterns previously described in the literature. Our results suggest that the sacrum is markedly less effective than the hipbone for sex determination, which is at odds with its central position within the pelvic girdle and thus its supposed obstetric relevance.

Influence of Circulation Patterns on The Visitor Distribution in A Shopping Mall Case Study Palembang Icon Shopping Center, Palembang City

Physiological needs are the most basic human needs. Shopping is one of the measures to fulfill physiological needs, such as clothing, foods, and drinks. Most people prefer to shop in shopping centers as it is more convenient and secure due to its regular spatial pattern, forming a good circulation pattern for their visitors. Palembang Icon is a shopping center in Palembang, located on Jl. POM IX, Lorok Pakjo, Ilir Barat I District, Palembang City, South Sumatra which considered has a good circulation pattern. Palembang Icon has a mall circulation pattern, namely a linear circulation pattern, one lane intended so that all retailers get a strategic place and the circulation orientation becomes clear. This research discusses the Palembang Icon shopping center's circulation pattern and analyzes its influence on the distribution of visitors by using the space syntax method with simulation using the DepthMapX application. The result shows that the circulation space's shape is closed, and the configuration of the circulation path is entirely a linear circulation pattern.

Modeling how antibody responses may determine the efficacy of COVID-19 vaccines.

Predicting the efficacy of COVID-19 vaccines would aid vaccine development and usage strategies, which is of importance given their limited supplies. Here we develop a multiscale mathematical model that proposes mechanistic links between COVID-19 vaccine efficacies and the neutralizing antibody (NAb) responses they elicit. We hypothesized that the collection of all NAbs would constitute a shape space and that responses of individuals are random samples from this space. We constructed the shape space by analyzing reported in vitro dose-response curves of ~80 NAbs. Sampling NAb subsets from the space, we recapitulated the responses of convalescent patients. We assumed that vaccination would elicit similar NAb responses. We developed a model of within-host SARS-CoV-2 dynamics, applied it to virtual patient populations and, invoking the NAb responses above, predicted vaccine efficacies. Our predictions quantitatively captured the efficacies from clinical trials. Our study thus suggests plausible mechanistic underpinnings of COVID-19 vaccines and generates testable hypotheses for establishing them.

Performance of variously shaped glass-fibre-reinforced polymer bars in concrete columns

An investigation was carried out into the structural performance of concrete columns reinforced with various shapes of glass-fibre-reinforced polymer bars and stainless steel stirrups under concentric loading at ultimate limit state. Six square-section columns were cast to investigate the effects of different reinforcement types. The results showed failure modes depended on reinforcement material, shape and stirrup spacing. Across all specimens, steel-reinforced columns had higher loading capacity and better ductile performance, followed by L-shape and then round polymer bars. Smaller spiral spacing increased confinement efficiency and ductility and provided sufficient restraint against longitudinal polymer bar buckling. Finite-element models were also calibrated, and the results were in close agreement with experimental measurements. Based on the calibrated models, numerical parameters were studied to understand further the behaviour of composite columns reinforced with glass-fibre-reinforced polymer.

Hiding the Rabbit: Using a genetic algorithm to investigate shape guidance in visual search.

During visual search, attention is guided by specific features, including shape. Our understanding of shape guidance is limited to specific attributes (closures and line terminations) that do not fully explain the richness of preattentive shape processing. We used a novel genetic algorithm method to explore shape space and to stimulate hypotheses about shape guidance. Initially, observers searched for targets among 12 random distractors defined, in radial frequency space, by the amplitude and phase of 10 radial frequencies. Reaction time (RT) was the measure of “fitness.” To evolve toward an easier search task, distractors with faster RTs survived to the next generation, “mated,” and produced offspring (new distractors for the next generation of search). To evolve a harder search, surviving distractors were those yielding longer RTs. Within eight generations of evolution, the method succeeds in producing visual searches either harder or easier than the starting search. In radial frequency space, easy distractors evolve amplitude × frequency spectra that are dissimilar to the target, whereas hard distractors evolve spectra that are more similar to the target. This method also works with naturally shaped targets (e.g., rabbit silhouettes). Interestingly, the most inefficient distractors featured a combination of a body and ear distractors that did not resemble the rabbit (visually or in spectrum). Adding extra ears to these distractors did not impact the search spectrally and instead made it easier to confirm a rabbit, once it was found. In general, these experiments show that shapes that are clearly distinct when attended are similar to each other preattentively.

Ciliary beating patterns map onto a low-dimensional behavioural space

Biological systems are robust to perturbations at both the genetic and environmental levels, although these same perturbations can elicit variation in behaviour. The interplay between functional robustness and behavioural variability is exemplified at the organellar level by the beating of cilia and flagella. Cilia are motile despite wide genetic diversity between and within species, differences in intracellular concentrations of ATP and calcium, and considerable environment fluctuations in temperature and viscosity. At the same time, these perturbations result in a variety of spatio-temporal patterns that span a rich behavioural space. To investigate this behavioural space we analysed the dynamics of isolated cilia from the unicellular algae Chlamydomonas reinhardtii under many different environmental and genetic conditions. We found that, despite large changes in beat frequency and amplitude, the space of waveform shapes is low-dimensional in the sense that two features account for 80% of the observed variation. The geometry of this behavioural space accords with the predictions of a simple mechanochemical model in the low-viscosity regime. This allowed us to associate waveform shape variability with changes in only the curvature response coefficients of the dynein motors.

Workspaces of Mediation: How Digital Platforms Shape Practices, Spaces and Places of Creative Work

ABSTRACTThis paper investigates how online platforms co‐construct spatialities of fashion design. Advancing the notion of workspaces of mediation, the article interlinks research on the geographies of creative work with debates on digital geography. It demonstrates that humans, online platforms, and spatiality are interrelated and come together in the constitution of everyday spatial encounters and experiences. Applying methods from digital ethnography, in‐depth interviews, on/offline participant observations, and an analysis of Instagram accounts were conducted. The article demonstrates that Instagram has become a powerful non‐human actor that reconfigures (i) practices, (ii) spaces, and (iii) places of work. With the notion of workspaces of mediation, the paper argues that spaces of work must go beyond concepts of hybrid space and beyond the geotag. Instead, mediated workspaces are constituted as a complex entanglement of online and offline socio‐spatial relations and practices.

Nonparametric k-Sample Test on Shape Spaces with Applications to Mitochondrial Shape Analysis

Abstract An important hypothesis in animal cell biology is that an animal’s acute exercise regimen affects some subcellular structures, for example mitochondrial morphology, in its muscle tissue. This paper investigates that hypothesis using a nonparametric metric-based energy test for comparing mitochondrial populations. It explores two shape spaces—the elastic shape space and Kendall shape space—and five corresponding shape metrics on these spaces. The results overwhelmingly point to the statistical significance of the effect of an acute exercise regimen on the shape of SS-type mitochondria. Although past studies based on specific morphological features derived from mitochondria failed to detect this significance. In this analysis, a potentially significant factor is cell membership and a k-sample generalization of the energy test—the DISCO test shows that the cell effect is indeed significant. The energy test cannot be applied directly due to the hierarchical structure of the distance matrix. We propose a compression method to remove the significant cell effect while testing for the exercise effect. With this compression, only the elastic scaled metric shows statistical significance of the exercise factor in this more complicated scenario. This result is because the elastic-scaled metric is more sensitive to subtle changes in mitochondrial shapes caused by acute exercise.

Polytopes in all dimensional loop quantum gravity

The Lasserre’s reconstruction algorithm is extended to the D-polytopes with the construction of their shape space. Thus, the areas of d-skeletons (1le dle D) can be expressed as functions of the areas and normal bi-vectors of the (D-1)-faces of D-polytopes. As weak solutions of the simplicity constraints in all dimensional loop quantum gravity, the simple coherent intertwiners are employed to describe semiclassical D-polytopes. New general geometric operators based on D-polytopes are proposed by using the Lasserre’s reconstruction algorithm and the coherent intertwiners. Such kind of geometric operators have expected semiclassical property by the definition. The consistent semiclassical limit with respect to the semiclassical D-polytopes can be obtained for the usual D-volume operator in all dimensional loop quantum gravity by fixing its undetermined regularization factor case by case.

The role of geometric features in a germinal center.

The germinal center (GC) is a self-organizing structure produced in the lymphoid follicle during the T-dependent immune response and is an important component of the humoral immune system. However, the impact of the special structure of GC on antibody production is not clear. According to the latest biological experiments, we establish a spatiotemporal stochastic model to simulate the whole self-organization process of the GC including the appearance of two specific zones: the dark zone (DZ) and the light zone (LZ), the development of which serves to maintain an effective competition among different cells and promote affinity maturation. A phase transition is discovered in this process, which determines the critical GC volume for a successful growth in both the stochastic and the deterministic model. Further increase of the volume does not make much improvement on the performance. It is found that the critical volume is determined by the distance between the activated B cell receptor (BCR) and the target epitope of the antigen in the shape space. The observation is confirmed in both 2D and 3D simulations and explains partly the variability of the observed GC size.

From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach.

Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existing techniques for predicting shape with aleatoric (data-dependent) uncertainty utilize a principal component analysis (PCA) based shape representation computed in isolation of the model training. This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i.e., shape) space. In this paper, we propose a principled framework based on the variational information bottleneck theory to relax these assumptions while predicting probabilistic shapes of anatomy directly from images without supervised encoding of shape descriptors. Here, the latent representation is learned in the context of the learning task, resulting in a more scalable, flexible model that better captures data non-linearity. Additionally, this model is self-regularized and generalizes better given limited training data. Our experiments demonstrate that the proposed method provides an accuracy improvement and better calibrated aleatoric uncertainty estimates than state-of-the-art methods.