Meaningful Structure Research Articles

A game-theoretic driver steering model with individual risk perception field generation

Driver-automation shared steering control (SSC) has emerged as a promising technology for enhancing vehicle safety, but desire to achieve seamless collaboration between the driver and automation requires an in-depth understanding of driver steering behavior in interaction with automation. In this paper, we introduce a game-theoretic driver steering model with individual risk perception field generation. Firstly, a driver risk perception field is developed based on a novel concept of potential injury risk (PIR) to provide a quantitative estimation of the driver’s perceived driving risk. This approach offers an explicit and physically meaningful structure for simulating the driver’s risk perception process and elucidating the reasons for discrepancies in risk perception. Then, this driver risk perception field is integrated into the framework of non-cooperative Nash game to model the steering interaction between the driver and automation, and the analytical expression of Nash equilibrium is derived in detail. The resulting combined driver model effectively captures the driver adaptation at both the control and planning levels. Next, the key parameters of the combined driver model and its comparators are identified using measured driver steering behavior data from thirty subjects in a series of driving simulator experiments. Finally, the effectiveness and superiority of the combined driver model is validated through a comprehensive comparative analysis. The results demonstrate that the combined driver model achieves the lowest prediction errors compared to its comparators and effectively captures the individual differences in risk perception and steering behavior.

Kajian Terhadap Unsur Kalimat Subjek, Objek, Predikat, dan Keterangan

This study aims to examine the elements in a sentence, various basic patterns that form a sentence and identify basic patterns in a sentence. The method used in this study is descriptive analysis with a qualitative approach. The results of the study indicate that the subject, predicate, object, and description are elements of a sentence that are basic components in forming a coherent and meaningful syntactic structure. The subject performs the action or the main topic of discussion, the predicate as an explanation of the action or condition, the object as the recipient of the action or condition, and the adverb as a complement that provides additional information. All elements play a crucial role in forming clear and precise sentences.

Bayesian Clustering via Fusing of Localized Densities

Bayesian clustering typically relies on mixture models, with each component interpreted as a different cluster. After defining a prior for the component parameters and weights, Markov chain Monte Carlo (MCMC) algorithms are commonly used to produce samples from the posterior distribution of the component labels. The data are then clustered by minimizing the expectation of a clustering loss function that favors similarity to the component labels. Unfortunately, although these approaches are routinely implemented, clustering results are highly sensitive to kernel misspecification. For example, if Gaussian kernels are used but the true density of data within a cluster is even slightly non-Gaussian, then clusters will be broken into multiple Gaussian components. To address this problem, we develop Fusing of Localized Densities (FOLD), a novel clustering method that melds components together using the posterior of the kernels. FOLD has a fully Bayesian decision theoretic justification, naturally leads to uncertainty quantification, can be easily implemented as an add-on to MCMC algorithms for mixtures, and favors a small number of distinct clusters. We provide theoretical support for FOLD including clustering optimality under kernel misspecification. In simulated experiments and real data, FOLD outperforms competitors by minimizing the number of clusters while inferring meaningful group structure.

Mining of primary raw materials as the critical foundation of 'sustainable' metals: a wicked problem for technology innovation clusters.

A transition to a more sustainable human-nature system is inextricably linked to raw materials production, if economic growth is to be maintained or increased by the emergence of new, energy- and metal-hungry technology innovation clusters. The dependence on mined raw materials is a wicked problem for societies vulnerable to negative ecological impacts and for global power bases wanting to secure access to an increasing array of feedstocks. We interrogate the issue of what constitutes a sustainable metal from a triple perspective: (i) the characteristics of ore deposits and the primary extractive operations that supply critical raw materials; (ii) the impediments for complex and interacting supply chains to maintain critical (and other) metals in use; and (iii) the lack of transparency in supply chains that makes it challenging for customers to avoid resources that have been produced by unsustainable and poor practices. We examine existing and emerging structures for resource management to explain the limits to the circular economy and what constitutes a meaningful systemic structure for primary production by responsible mining. We call for the inclusion of a standardized statement of the 'natural capital' embodied in R&D for technological materials as a means to create transparency about what constitutes a sustainable metal.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering.

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

EGST: Enhanced Geometric Structure Transformer for Point Cloud Registration.

We explore the effect of geometric structure descriptors on extracting reliable correspondences and obtaining accurate registration for point cloud registration. The point cloud registration task involves the estimation of rigid transformation motion in unorganized point cloud, hence it is crucial to capture the contextual features of the geometric structure in point cloud. Recent coordinates-only methods ignore numerous geometric information in the point cloud which weaken ability to express the global context. We propose Enhanced Geometric Structure Transformer to learn enhanced contextual features of the geometric structure in point cloud and model the structure consistency between point clouds for extracting reliable correspondences, which encodes three explicit enhanced geometric structures and provides significant cues for point cloud registration. More importantly, we report empirical results that Enhanced Geometric Structure Transformer can learn meaningful geometric structure features using none of the following: (i) explicit positional embeddings, (ii) additional feature exchange module such as cross-attention, which can simplify network structure compared with plain Transformer. Extensive experiments on the synthetic dataset and real-world datasets illustrate that our method can achieve competitive results.

ArtiSAN: navigating the complexity of material structures with deep reinforcement learning

Abstract Finding low-energy atomic ordering in compositionally complex materials is one of the hardest problems in materials discovery, the solution of which can lead to breakthroughs in functional materials—from alloys to ceramics. In this work, we present the Artificial Structure Arranging Net (ArtiSAN)—a reinforcement learning agent utilizing graph representation that is trained to find low-energy atomic configurations of multicomponent systems through a series of atomic switch operations. ArtiSAN is trained on small alloy supercells ranging from binary to septenary. Strikingly, ArtiSAN generalizes to much larger systems of more than a thousand atoms, which are inaccessible with state-of-the-art methods due to the combinatorially larger search space. The performance of the current ArtiSAN agent is tested and deployed on several compositions that can be correlated with known experimental and high-fidelity computational structures. ArtiSAN demonstrates transfer across size and composition and finds physically meaningful structures using no energy evaluation calls once fully trained. While ArtiSAN will require further modifications to capture all variability in structure search, it is a remarkable step towards solving the structural part of the problem of disordered materials discovery.

Future acceptance of automated insulin delivery systems in youths with type 1 diabetes: validation of the Italian artificial pancreas-acceptance measure.

The purpose of this study was to develop a questionnaire to examine the future acceptance of Automatic insulin delivery systems (AIDs), their perceived usefulness, ease of use, and trust in the device in subjects with type 1 diabetes (T1D). A questionnaire in Italian, based on the Technology Acceptance Model, was developed to examine intention to use AIDs, considered as a measure of future acceptance, and its determinants to use the system. A total of 43 questions for children and 46 for parents were included, and a 5-point Likert scale was used. 239 subjects with T1D using multiple daily injections (MDI) or sensor-augmented pump (SAP) and their parents completed the questionnaire. The completion rate was excellent, with almost 100% of items answered. The overall Cronbach's coefficient for children and adolescents was 0.92 and 0.93 for parents, indicating excellent internal consistency in both groups. Parent-youth agreement was 0.699 (95% confidence interval: 0.689-0.709), indicating a good agreement between the two evaluations. Factor analysis identified measurement factors for the "artificial pancreas (AP)-acceptance labeled benefits and hassles of AIDs, and the internal consistency of the total scale was alpha = 0.94 for subjects with T1D and 0.95 for parents. The level of AP acceptance was more than neutral: 3.91 ± 0.47 and 3.99 ± 0.43 (p = 0.07) for youths and parents, respectively (possible score range 1 to 5, neutral score is 3.0). Parents reported higher scores in the benefit items than children-adolescents (p = 0.04). We developed a new questionnaire based on the items available in the literature, and we demonstrated that the "AP-acceptance" reveals a meaningful factor structure, good internal reliability, and agreement between parent-young people evaluations. This measure could be a valuable resource for clinicians and researchers to assess AP acceptance in pediatric patients with T1D and their parents. This patient profiling approach could help to enroll candidates for AIDs with proper expectations and who most likely will benefit from the system.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution.

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace

Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.

A Multi-view Molecular Pre-training with Generative Contrastive Learning.

Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previous approaches to learning molecular representations in an end-to-end manner potentially suffered information loss while neglecting the utilization of molecular generative representations. To obtain rich molecular feature information, the pre-training molecular representation model utilized different molecular representations to reduce information loss caused by a single molecular representation. Therefore, we provide the MVGC, a unique multi-view generative contrastive learning pre-training model. Our pre-training framework specifically acquires knowledge of three fundamental feature representations of molecules and effectively integrates them to predict molecular properties on benchmark datasets. Comprehensive experiments on seven classification tasks and three regression tasks demonstrate that our proposed MVGC model surpasses the majority of state-of-the-art approaches. Moreover, we explore the potential of the MVGC model to learn the representation of molecules with chemical significance.

Discovering the hierarchical structure of variables describing adult attachment: insight from a joint analysis of seven questionnaires

Our work aimed to discover the structure of variables developed to conceptualize attachment dimensions in seven main approaches and measurement instruments. A group of 435 participants (49.7% female, 46.2% male, 4.1% non-binary/other identities) between the ages of 16 and 65 (M = 24.8, SD = 8.1) completed a series of seven questionnaires commonly used to measure attachment. A series of exploratory factor analyses was performed on the scales and items. In the analysis on the scales, the two-factor structure of Anxiety and Avoidance was reproduced. In the analysis on the items, a meaningful hierarchical six-level structure was discovered. Dimensions of Anxiety and Avoidance are quite similar across models, but Avoidance is much more complex than Anxiety. Anxiety remains in principle the same across all levels of the hierarchy, while Avoidance divides into several lower-order traits. The lower-order traits of Avoidance need reconceptualization, and the current study takes a step in this direction.

Adapting a Financial Incentives Intervention for Smoking Cessation With Alaska Native Families: Phase 1 Qualitative Research to Inform the Aniqsaaq (To Breathe) Study.

Alaska Native and American Indian (ANAI) peoples in Alaska currently experience a disproportionate burden of morbidity and mortality from tobacco cigarette use. Financial incentives for smoking cessation are evidence-based, but a family-level incentive structure has not been evaluated. We used a community-based participatory research and qualitative approach to culturally adapt a smoking cessation intervention with ANAI families. We conducted individual, semistructured telephone interviews with 12 ANAI adults who smoke, 12 adult family members, and 13 Alaska Tribal Health System stakeholders statewide between November 2022 and March 2023. Through content analysis, we explored intervention receptivity, incentive preferences, culturally aligned recruitment and intervention messaging, and future implementation needs. Participants were receptive to the intervention. Involving a family member was viewed as novel and aligned with ANAI cultural values of commitment to community and familial interdependence. Major themes included choosing a family member who is supportive and understanding, keeping materials positive and encouraging, and offering cash and noncash incentives for family members to choose (eg, fuel, groceries, activities). Participants indicated that messaging should emphasize family collaboration and that cessation resources and support tips should be provided. Stakeholders also reinforced that program materials should encourage the use of other existing evidence-based cessation therapies (eg, nicotine replacement, counseling). Adaptations, grounded in ANAI cultural strengths, were made to the intervention and recruitment materials based on participant feedback. Next steps include a beta-test for feasibility and a randomized controlled trial for efficacy. This is the first study to design and adapt a financial incentives intervention promoting smoking cessation among ANAI peoples and the first to involve the family system. Feedback from this formative work was used to develop a meaningful family-level incentive structure with ANAI people who smoke and family members and ensure intervention messaging is supportive and culturally aligned. The results provide qualitative knowledge that can inform future family-based interventions with ANAI communities, including our planned randomized controlled trial of the intervention.

Large Language Models Enable Few-Shot Clustering

Abstract Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the user’s intent. Existing approaches to semi-supervised clustering require a significant amount of feedback from an expert to improve the clusters. In this paper, we ask whether a large language model (LLM) can amplify an expert’s guidance to enable query-efficient, few-shot semi-supervised text clustering. We show that LLMs are surprisingly effective at improving clustering. We explore three stages where LLMs can be incorporated into clustering: before clustering (improving input features), during clustering (by providing constraints to the clusterer), and after clustering (using LLMs post-correction). We find that incorporating LLMs in the first two stages routinely provides significant improvements in cluster quality, and that LLMs enable a user to make trade-offs between cost and accuracy to produce desired clusters. We release our code and LLM prompts for the public to use.1

Through the compression glass: language complexity and the linguistic structure of compressed strings

Abstract Against the backdrop of the sociolinguistic-typological complexity debate which is all about measuring, comparing and explaining language complexity, this article investigates how Kolmogorov-based information theoretic complexity relates to linguistic structures. Specifically, the linguistic structure of text which has been compressed with the text compression algorithm gzip will be analysed. One implementation of Kolmogorov-based language complexity is the compression technique (Ehret, Katharina. 2021. An information-theoretic view on language complexity and register variation: Compressing naturalistic corpus data. Corpus Linguistics and Linguistic Theory (2). 383–410) which employs gzip to measure language complexity in naturalistic text samples. In order to determine what type of structures compression algorithms like gzip capture, and how these compressed strings relate to linguistically meaningful structures, gzip’s lexicon output is retrieved and subjected to an in-depth analysis. As a case study, the compression technique is applied to the English version of Lewis Carroll’s Alice’s Adventures in Wonderland and its lexicon output is extracted. The results show that gzip-like algorithms sometimes capture linguistically meaningful structures which coincide, for instance, with lexical words or suffixes. However, many compressed sequences are linguistically unintelligible or simply do not coincide with any linguistically meaningful structures. Compression algorithms like gzip thus crucially capture purely formal structural regularities. As a consequence, information theoretic complexity, in this context, is a linguistically agnostic, purely structural measure of regularity and redundancy in texts.

A generalized hypothesis test for community structure in networks

AbstractResearchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in terms of a general and model-agnostic community structure parameter. Leveraging this parameter, we propose a simple and interpretable test statistic used to formulate two separate hypothesis testing frameworks. The first is an asymptotic test against a baseline value of the parameter while the second tests against a baseline model using bootstrap-based thresholds. We prove theoretical properties of these tests and demonstrate how the proposed method yields rich insights into real-world datasets.

From local to global gene co-expression estimation using single-cell RNA-seq data.

In genomics studies, the investigation of gene relationships often brings important biological insights. Currently, the large heterogeneous datasets impose new challenges for statisticians because gene relationships are often local. They change from one sample point to another, may only exist in a subset of the sample, and can be nonlinear or even nonmonotone. Most previous dependence measures do not specifically target local dependence relationships, and the ones that do are computationally costly. In this paper, we explore a state-of-the-art network estimation technique that characterizes gene relationships at the single cell level, under the name of cell-specific gene networks. We first show that averaging the cell-specific gene relationship over a population gives a novel univariate dependence measure, the averaged Local Density Gap (aLDG), that accumulates local dependence and can detect any nonlinear, nonmonotone relationship. Together with a consistent nonparametric estimator, we establish its robustness on both the population and empirical levels. Then, we show that averaging the cell-specific gene relationship over mini-batches determined by some external structure information (eg, spatial or temporal factor) better highlights meaningful local structure change points. We explore the application of aLDG and its minibatch variant in many scenarios, including pairwise gene relationship estimation, bifurcating point detection in cell trajectory, and spatial transcriptomics structure visualization. Both simulations and real data analysis show that aLDG outperforms existing ones.

Associations between mind wandering, viewer interactions, and the meaningful structure of educational videos

Over the last years, and especially in the context of the COVID-19 pandemic, online videos have become an integral part of education. Therefore, it is essential to understand how to design such videos to provide an efficient and engaging learning experience for the viewers. Regarding learning success, the occurrence of mind wandering is of particular relevance. Thus, this preregistered research investigates the perception of online videos by analyzing the relationship between viewer behavior (such as pause button presses), mind wandering, and event segmentation by employing logfiles of a large German online video platform for educational videos combined with data collected in two online studies. Contrary to our expectations, we found a significant positive correlation between pausing, seek and resume viewer behavior, and mind wandering. Furthermore, replicating previous research, we identified a significant positive correlation between pausing behavior and meaningful event boundaries in the video. Additionally, a significant positive correlation between perceived event boundaries and mind wandering was shown. The triangulation of three data sources suggests that mind-wandering co-occurs with perceived event boundaries and observable, logged behavior (e.g., pressing the pause button). Given that the overall mind-wandering occurrence was also positively related to self-reported learning, a possible interpretation of the data is that event boundaries may trigger task-related mind-wandering that might even be conducive to learning.

Structure-Aware Simplification for Hypergraph Visualization.

Hypergraphs provide a natural way to represent polyadic relationships in network data. For large hypergraphs, it is often difficult to visually detect structures within the data. Recently, a scalable polygon-based visualization approach was developed allowing hypergraphs with thousands of hyperedges to be simplified and examined at different levels of detail. However, this approach is not guaranteed to eliminate all of the visual clutter caused by unavoidable overlaps. Furthermore, meaningful structures can be lost at simplified scales, making their interpretation unreliable. In this paper, we define hypergraph structures using the bipartite graph representation, allowing us to decompose the hypergraph into a union of structures including topological blocks, bridges, and branches, and to identify exactly where unavoidable overlaps must occur. We also introduce a set of topology preserving and topology altering atomic operations, enabling the preservation of important structures while reducing unavoidable overlaps to improve visual clarity and interpretability in simplified scales. We demonstrate our approach in several real-world applications.

Transformer-Based High-Fidelity Facial Displacement Completion for Detailed 3D Face Reconstruction

In this paper, we tackle a special face completion task, facial displacement completion, which can offer a key component for many single image 3D face reconstruction systems. To produce a detailed 3D face with ear-to-ear complete displacement UV map, we propose a novel Displacement Completion method based on Transformer (DCT). Current transformer based image inpainting methods usually follow a two-stage scheme, which firstly recovers the masked pixels in low resolution with transformer, and then replenishes the inpainting result in high resolution with GAN. Although these methods have achieved great success, they suffer from information loss from two aspects when applied in face completion: 1) The downsampling operation makes transformer only produce a coarse appearance prior for GAN, incurring middle and low level information loss. 2) Some meaningful facial semantics can be well captured by transformer and further benefit the completion, but it's has not yet been explored. Motivated by the above considerations, we come up with three key designs in the proposed DCT: PCA tokenization, BERT-style learning, and style modulation. Firstly, we use PCA tokenization to replace the downsampling in transformer to preserve more meaningful structures. Secondly, we make transformer simulate the two tasks in BERT, Masked Language Model (MLM) and Next Sentence Prediction (NSP), for both masked pixels and facial attributes recovery. Thirdly, we encode the outcome of transformer as the latent code to guide an image translation network in the StyleGAN2 modulation way. Experments on both FaceScape dataset and in-the-wild data demonstrate DCT's better performance compared with other transformer based or GAN based completion methods.