Representation Of Structure Research Articles

X-ray micro-computed tomography for mechanical behaviour analysis of Automated Fiber Placement (AFP) laminates with integrated gaps and overlaps

Automated Fiber Placement (AFP) is a manufacturing technique widely used for the serial production of aerospace parts. A deep understanding of the effect of lay-up defects is crucial for part and lay-up design. Currently, numerical models for structural simulation lack a precise representation of the internal structure of AFP laminates, which is crucial for understanding the impact of defects on mechanical properties. This paper presents a novel approach based on high-resolution micro-computed tomography (micro-CT) scans from specimens manufactured with AFP, which automatically creates a mesoscale numerical model incorporating as-fabricated defect morphologies. The hexahedral mesh, generated from the segmented plies of the micro-CT volume, accounts for ply thickness and out-of-plane fiber orientation. This approach is verified with mechanical testing and digital image correlation (DIC) under tensile loading. The simulation results align closely with experimental testing and accurately illustrate the influence of fiber waviness in various defect configurations, such as gaps and overlaps. The study shows that lay-up defects can lead to knockdown factors of up to 12% in tensile properties, with each defect creating a distinct pattern in the local strain. This model can serve as a benchmark for further numerical simulations and surrogate models of defect configurations under varying loading conditions.

Notice of Retraction — Lax representation and bi-Hamiltonian structure of nonlinear Qiao model

Notice of Retraction — Lax representation and bi-Hamiltonian structure of nonlinear Qiao model

SCAE: Structural Contrastive Auto-Encoder for Incomplete Multi-View Representation Learning

Describing an object from multiple perspectives often leads to incomplete data representation. Consequently, learning consistent representations for missing data from multiple views has emerged as a key focus in the realm of Incomplete Multi-view Representation Learning (IMRL). In recent years, various strategies, such as subspace learning, matrix decomposition, and deep learning, have been harnessed to develop numerous IMRL methods. In this article, our primary research revolves around IMRL, with a particular emphasis on addressing two main challenges. Firstly, we delve into the effective integration of intra-view similarity and contextual structure into a unified framework. Secondly, we explore the effective facilitation of information exchange and fusion across multiple views. To tackle these issues, we propose a deep learning approach known as Structural Contrastive Auto-Encoder (SCAE) to solve the challenges of IMRL. SCAE comprises two major components: intra-view structural representation learning and inter-view contrastive representation learning. The former involves capturing intra-view similarity by minimizing the Dirichlet energy of the feature matrix, while also applying spatial dispersion regularization to capture intra-view contextual structure. The latter encourages maximizing the mutual information of inter-view representations, facilitating information exchange and fusion across views. Experimental results demonstrate the efficacy of our approach in significantly enhancing model accuracy and robustly addressing IMRL problems. The code is available at https://github.com/limengran98/SCAE .

Structural neuroanatomy of human facial behaviors.

The human face plays a central role in emotions and social communication. The emotional and somatic motor networks generate facial behaviors, but whether facial behaviors have representations in the structural anatomy of the human brain is unknown. We coded 16 facial behaviors in 55 healthy older adults who viewed five videos that elicited emotions and examined whether individual differences in facial behavior were related to regional variation in gray matter volume. Voxel-based morphometry analyses revealed that greater emotional facial behavior during the disgust trial (i.e. greater brow furrowing and eye tightening as well as nose wrinkling and upper lip raising) and the amusement trial (i.e. greater smiling and eye tightening) was associated with larger gray matter volume in midcingulate cortex, supplementary motor area, and precentral gyrus, areas spanning both the emotional and somatic motor networks. When measured across trials, however, these facial behaviors (and others) only related to gray matter volume in the precentral gyrus, a somatic motor network hub. These findings suggest that the emotional and somatic motor networks store structural representations of facial behavior and that the midcingulate cortex is critical for generating the predictable movements in the face that arise during emotions.

Towards a taxonomy of team workflow structures

Team workflow represents interactions between individuals and specific actions or tasks. Individuals’ interactions have important effects on fellow teammates’ actions by expanding or constraining actions available to them. For example, teammates may avoid performing the same action to avoid duplication of effort or they may perform their actions sequentially if one task’s completion is a prerequisite for another task. Complex dependencies embedded in these interactions suggest the need to understand team workflows from a relational perspective. As workflow structures are shaped by elements of organizational design, cognitive factors, and features of the task environment, no single workflow structure is optimal for all teams, and team workflows may manifest in countless distinct configurations. Through a systematic, network-based representation of team workflows, this paper uses a sample of 139,500 teams on GitHub to identify common patterns of team workflows. Each team is represented as a two-mode network where individuals form ties to up to fifteen distinct actions capturing productivity, discussion, and team management. Several node-level and graph-level centrality indices highlight patterns of differentiation across team workflows, and a k-means clustering algorithm detects three distinct clusters of team workflow structures: small teams of highly active generalists, small teams with a moderately active mix of focused and generalist members, and large, segmented teams of focused individuals collectively engaging in a few extremely popular actions. These results demonstrate how a structural representation of team workflows provides unique insight into team behavior and highlights distinctions that may otherwise be lost when examining team activity in aggregate.

Predicting metabolic responses in genetic disorders via structural representation in machine learning

Predicting metabolic responses in genetic disorders via structural representation in machine learning

Automatic Method for Extracting Tree Branching Structures from a Single RGB Image

Creating automated methods for detecting branches in images is crucial for applications like harvesting robots and forest monitoring. However, the tree images encountered in real-world scenarios present significant challenges for branch detection techniques due to issues such as background interference, occlusion, and varying environmental lighting. While there has been notable progress in extracting tree trunks for specific species, research on identifying lateral branches remains limited. The primary challenges include establishing a unified mathematical representation for multi-level branch structures, conducting quantitative analyses, and the absence of suitable datasets to facilitate the development of effective models. This study addresses these challenges by creating a dataset encompassing various tree species, developing annotation tools for multi-level branch structure labeling, designing branch vector representations and quantitative metrics. Building on this foundation, the study introduces an automatic extraction model for multi-level branch structures that utilizes ResNet and a self-attention mechanism, along with a tailored loss function for branch extraction tasks. The study evaluated several model variants through both qualitative and quantitative experiments. Results from different tree images demonstrate that the final model can accurately identify the trunk structure and effectively extract detailed lateral branch structures, offering a valuable tool for applications in this area.

Structural mechanism of glass transition uncovered by unsupervised machine learning

Uncovering the structural origins of the ubiquitous dynamic arrest phenomenon at the glass transition has long been a challenge due to the difficulty in identifying a rational structural representation from a disordered medium. To address this challenge, we propose a novel approach based on unsupervised learning to define a set of structural fingerprints. In this approach, complex local atomic environments, ranging from short to medium range, are captured by the discretized radial distribution function and projected onto a simple two-dimensional space using a neural network-based autoencoder. This two-dimensional space is characterized by two static structural indicators, P1 and P2, providing a comprehensive and user-friendly representation of the mysterious “glassy structure”. By employing Gaussian mixture modeling, the structural space is autonomously divided into three sections, each representing a unique cluster with similar environments. These indicators not only elucidate the glass transition but also allow for the quantitative prediction of activation barriers for local structural excitations. Furthermore, the unsupervised clustering technique can distinguish between the structural features of “hard zones” and “soft zones”, as well as recently proposed superfast “liquid-like” atoms in glass. This unsupervised machine learning approach demonstrates the utility of seemingly agnostic local structure in amorphous materials, offering insights into the long-sought structural origins of the glass transition.

Figurative Narratives in Political Blogging: Linguocognitive Perspective

Introduction. Recent years have seen a significant shift towards the pervasive use of figurative narratives in mediated political communication. Narrative practices, previously analyzed exclusively within the framework of literary theory, have become a kind of «language games» of modern politicians in their communication with potential voters and opponents, debates and intricate negotiation processes. The article analyzes figurative narratives of the English- and German-speaking political blogs as a powerful linguistic-cognitive tool for mediated political communication. The purpose of the paper is deep semantic analysis of figurative narratives of political blogs, which, as the authors claim, is a promising direction of research due to the narrative nature of human reasoning. The paper argues that the skillful use of figurative narrative practices based on archetypical plots and cultural references makes it possible to manipulate the consciousness of the electorate to achieve political goals.Methodology and sources. Methodologically, the study is based on the cognitive-discursive approach to narrative analysis and the basic principles of the neural theory of language and metaphor.Results and discussion. Pointing to the manipulative effect of metaphorical framing in narrative, the authors attempt to present a holistic view of narrative as a tool for the formation of political views. The study is based on the premise that narrative employs the rich structure of human representations of events and actions, and the type of metaphor promoted in the narrative affects human understanding of events and the adoption of certain positions on an important public issue by readers of political blogs. The set of metaphors, politicians use, forms a special type of cultural narrative, which the authors interpret as an extended metaphorical narrative.Conclusion. Summarizing the effects of using metaphorically framed narratives in politics, the authors conclude that the narrative communication strategy in political blogging is a targeted impact on a communication partner by referring to various narrative plots; the modification or transformation of the potential and diversity of political narratives, as well as their effectiveness, speaks in favor of their use in domestic and foreign policy communication to contribute to a symmetrical communication space aimed at successful cooperation rather than confrontation.

Decomposition and graphical correspondence analysis of checkerboard copulas

Abstract We analyze optimal low-rank approximations and correspondence analysis of the dependence structure given by arbitrary bivariate checkerboard copulas. Methodologically, we make use of the truncation of singular value decompositions of doubly stochastic matrices representing the copulas. The resulting (truncated) representations of the dependence structures are sparse, in particular, compared to the number of squares on the checkerboard. The additive structure of the decomposition carries through to statistical functionals of the copula, such as Kendall’s τ \tau or Spearman’s ρ \rho , and also motivates similarity measures for checkerboard copulas. We link our analysis to continuous decompositions of copula densities and copula-generating algorithms and discuss further general properties of the decomposition and its truncation. For example, truncated series might lack nonnegativity, and approximation errors increase for monotonicity-like copulas. We provide algorithms and extensions that account for and counteract these properties. The low-rank representation is illustrated for various copula examples, and some analytical results are derived. The resulting correspondence analysis profile plots are analyzed, providing graphical insights into the dependence structure implied by the copula. An illustration is provided with an empirical data set on fuel injector spray characteristics in jet engines.

Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.

The high-dimensional structure of natural image representations varies systematically across visual cortex

The high-dimensional structure of natural image representations varies systematically across visual cortex

Comparing representational structures for simple and complex stimuli in visual working memory

Comparing representational structures for simple and complex stimuli in visual working memory

Partial density of states representation for accurate deep neural network predictions of X-ray spectra.

The performance of a machine learning (ML) algorithm for chemistry is highly contingent upon the architect's choice of input representation. This work introduces the partial density of states (p-DOS) descriptor: a novel, quantum-inspired structural representation which encodes relevant electronic information for machine learning models seeking to simulate X-ray spectroscopy. p-DOS uses a minimal basis set in conjunction with a guess (non-optimised) electronic configuration to extract and then discretise the density of states (DOS) of the absorbing atom to form the input vector. We demonstrate that while the electronically-focused p-DOS performs well in isolation, optimal performance is achieved when supplemented with nuclear structural information imparted via a geometric representation. p-DOS provides a description of the key electronic properties of a system which is not only concise and computationally efficient, but also independent of molecular size or choice of basis set. It can be rapidly generated, facilitating its application with large training sets. Its performance is demonstrated using a wide variety of examples at the sulphur K-edge, including the prediction of ultrafast X-ray spectroscopic signal associated with photoexcited 2(5H)-thiophenone. These results highlight the potential for ML models developed using p-DOS to contribute to the interpretation and prediction of experimental results e.g. in operando measurements of batteries and/or catalysts and femtosecond time-resolved studies, especially those made possible by emergent cutting-edge technologies, especially X-ray free electron lasers.

PROFiT-Net: Property-Networking Deep Learning Model for Materials.

There is a growing need to develop artificial intelligence technologies capable of accurately predicting the properties of materials. This necessitates the expansion of material databases beyond the scope of density functional theory, and also the development of deep learning (DL) models that can be effectively trained with a limited amount of high-fidelity data. We developed a DL model utilizing a crystal structure representation based on the orbital field matrix (OFM), which was modified to incorporate information on elemental properties and valence electron configurations. This model, effectively capturing the interrelation between the elemental properties in the crystal, was coined the PRoperty-networking Orbital Field maTrix-convolutional neural Network (PROFiT-Net). Remarkably, PROFiT-Net demonstrated high accuracy in predicting the dielectric constant, experimental band gaps, and formation enthalpies compared with other leading DL models. Moreover, our model accurately identifies physical patterns, such as avoiding the prediction of unphysical negative band gaps and exhibiting a Penn-model-like trend while maintaining the scalability. We envision that PROFiT-Net will accelerate the development of functional materials.

SEMbap: Bow-free covariance search and data de-correlation.

Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the first stage, an exhaustive search of missing edges with significant covariance is performed via Shipley d-separation tests; then, in the second stage, a Constrained Gaussian Graphical Model (CGGM) is fitted or a low dimensional representation of bow-free edges structure is obtained via Graph Laplacian Principal Component Analysis (gLPCA). We compare four popular deconfounding methods to BAP search approach with applications on simulated and observed expression data. In the former, different structures of the hidden covariance matrix have been replicated. Compared to existing methods, BAP search algorithm is able to correctly identify hidden confounding whilst controlling false positive rate and achieving good fitting and perturbation metrics.

Is the Term "Food Consistency" Used Consistently in Consumer Science? An Exploratory Study of Consumer Association and Conceptualization.

The post-pandemic context has changed the modes for collecting data in sensory and consumer science. The objectives of this research were to analyze consumers' associations of food consistency and to study two virtual modes of the Free Word Association test (FWA). This test was administered to 209 consumers (180 women, 29 men, 18-45 years old) asynchronously (i.e. self-administered) and synchronously (i.e. face to face interviews). The Cognitive Salience Index (CSI) was calculated, and the structure of the social representation was analyzed. Correspondence analysis showed that food consistency was a mixture of concepts related to structure, hardness and several aspects of auditory (e.g., Crunchy, Crispy), tactile (e.g. Smooth, Spreadable) and oral texture (e.g., Creamy, Gummy). Slightly consistent food was associated with something soft, liquid or semisolid, and very consistent food to something hard and resistant. Consistent food was more related to "very" than to "slightly consistent." The CSI depended on the stimulus presented (p < 0.05). Regarding the social representation structure, the central core had the highest CSI for all stimuli (CSI ≥ 0.13, p < 0.05). Consumers defined "very consistent, consistent and slightly consistent food" by naming more foods in the synchronous mode than in the asynchronous one. In the asynchronous mode, consumers took more time to complete the test. The virtual FWA test (asynchronous or synchronous) showed some differences in the associations of term consistency, due to the lack of spontaneity in the first minute. It is important to adjust the methodologies to standardize the times in both modes.

A New Approach Using the Lindley Distribution in Stochastic Frontier Analysis

Abstract Stochastic Frontier Analysis plays a crucial role in assessing technical efficiency and modelling production processes across various disciplines. Traditionally, SFA assumes specific error distributions, such as the normal distribution for random effects and the half-normal distribution for technical efficiency. However, the choice of error distributions can significantly impact model estimation and interpretation. This study proposes a novel approach by incorporating the Lindley distribution as a flexible error distribution in SFA, termed Lindley Stochastic Frontier Analysis (L-SFA). This extension offers a more detailed representation of the error structure, potentially enhancing the accuracy of efficiency estimates. The derivation and solution of maximum likelihood estimators for the theoretical foundations of L-SFA are provided. Furthermore, a simulation study demonstrates the advantages of L-SFA over traditional SFA. The findings underscore the importance of flexible error distributions in capturing the complexities of production processes with this new SFA extension. Keywords: Efficiency, Error distribution, Lindley stochastic frontier analysis, Maximum likelihood estimation, Simulation

Factorizers for distributed sparse block codes

Distributed sparse block codes (SBCs) exhibit compact representations for encoding and manipulating symbolic data structures using fixed-width vectors. One major challenge however is to disentangle, or factorize, the distributed representation of data structures into their constituent elements without having to search through all possible combinations. This factorization becomes more challenging when SBCs vectors are noisy due to perceptual uncertainty and approximations made by modern neural networks to generate the query SBCs vectors. To address these challenges, we first propose a fast and highly accurate method for factorizing a more flexible and hence generalized form of SBCs, dubbed GSBCs. Our iterative factorizer introduces a threshold-based nonlinear activation, conditional random sampling, and an ℓ ∞ -based similarity metric. Its random sampling mechanism, in combination with the search in superposition, allows us to analytically determine the expected number of decoding iterations, which matches the empirical observations up to the GSBC’s bundling capacity. Secondly, the proposed factorizer maintains a high accuracy when queried by noisy product vectors generated using deep convolutional neural networks (CNNs). This facilitates its application in replacing the large fully connected layer (FCL) in CNNs, whereby C trainable class vectors, or attribute combinations, can be implicitly represented by our factorizer having F-factor codebooks, each with C F fixed codevectors. We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function. With this integration, the convolutional layers can generate a noisy product vector that our factorizer can still decode, whereby the decoded factors can have different interpretations based on downstream tasks. We demonstrate the feasibility of our method on four deep CNN architectures over CIFAR-100, ImageNet-1K, and RAVEN datasets. In all use cases, the number of parameters and operations are notably reduced compared to the FCL.

Serial metamorphic manipulator dynamics formulation implementing screw theory tools

Abstract In this work, a framework is proposed for the analytical formulation of dynamic equations for serial metamorphic manipulators using screw theory tools. The integration of structure reconfiguration is achieved in a compact and comprehensive manner. Based on the previously proposed Modular Structure Representation, additional statements are provided to strictly define the metamorphic structure assembly. To validate the precision of the extracted dynamics, two simulations are performed for paths defined in cartesian space. The generated trajectory is evaluated by comparing the dynamic matrices produced by the proposed method with those from an established dynamic modeling algorithm. Additionally, the trajectory is simulated in an open-source simulator, and the torques extracted from system dynamics are compared with the corresponding simulator torques under the same kinematic constraints.