Hierarchical Constraints Research Articles

3D interactive garment parametric pattern-making and linkage editing based on constrained contour lines

PurposeThe purpose of this paper is to propose an interactive 2D–3D garment parametric pattern-making and linkage editing scheme that integrates clothing design, simulation and interaction to design 3D garments and 2D patterns. The proposed scheme has the potential to satisfy the individual needs of fashion industry, such as precise fit evaluation of the garment, interactive style editing with ease allowance and constrained contour lines in fashion design.Design/methodology/approachThe authors first construct a parametric pattern-making model for flat pattern design corresponding to the body dimensions. Then, the designing 2D patterns are stitched on a virtual 3D mannequin by performing a virtual try-on. If the customer is unsatisfied after the virtual try-on, the adjustable parameters (appearance parameters and fit parameters) can be adjusted using the 2D–3D linkage editing with hierarchical constrained contour lines, and the fit evaluation tool interactively provides the feedback.FindingsThe authors observed that the usability and efficiency of the existing garment pattern-making method simplifies the garment pattern-making process. The authors utilize an interactive garment parametric flat pattern-making model to generate an individualized garment flat pattern that effectively adjust and realize the local editing of the garment pattern-making. The 2D–3D linkage editing is then employed, which alters the size and shape of garment pattern for a precise human model fit of the 3D garment using hierarchical constrained contour lines. Various instances have validated the effectiveness of the proposed scheme, which can increase the reusability of the existing garment styles and improve the efficiency of fashion design.Research limitations/implicationsFirst, the authors do not consider the garment pattern-making design of sophisticated styles. Second, the authors do not directly consider complex garment shapes such as wrinkles, folds, multi-layer models and fabric physical properties.Originality/valueThe authors propose a pattern adjustment scheme that uses the 3D virtual try-on technology to avoid repetitions of reality-based fit tests and garment sample making in the designing process of clothing products. The proposed scheme provides interactive selections of garment patterns and sizes and renders modification tools for 3D garment designing and 2D garment pattern-making. The authors present the 2D–3D interactive linkage editing scheme for a custom-fit garment pattern based on the hierarchical constraint contour lines. The spatial relationship among the human body, pattern pieces and 3D garment model is adequately expressed, and the final design result of the garment pattern is obtained by constraint solving. Meanwhile, the tightness tension of different parts of the 3D garment is analyzed, and the fit and comfort of the garment are quantitatively evaluated.

코로나19와 여가제약: 위계적 여가제약 모델 검증

본 연구는 팬데믹 선언 이후 여가활동 참여 유형의 변화와 코로나19에 따른 여가제약에 주목하였다. 코로나19 발병에 따른 여가제약의 차이 변화를 살펴보고, 위계적 여가제약 이론을 통하여 여가제약 요인들이 어떻게 단계적으로 작용하는지 살펴보고 팬데믹 시대에 여가현상을 폭넓게 이해하는데 그 목적이 있다. 연구의 수행을 위해 설문지를 측정도구로 사용하였으며, 비확률 표집 방법 중 편의 추출법을 사용하여 총 317개의 자료를 수집한 후 분석하였다. 자료처리에서는 SPSS와 AMOS 프로그램을 사용하였다. 코로나 발병 전에는 신체적 활동, 사회적 활동, 자연적 활동, 문화적 활동, 지적활동의 순서로 나타났다. 하지만, 코로나 발병 이후에는 자연적 활동, 지적 활동, 신체적 활동, 사회적 활동, 문화적 활동의 순서로 나타났다. 둘째, 코로나19 발병에 따른 내재적, 대인적, 구조적 제약의 차이를 알아보기 위하여 전과 후로 나누어 분석한 결과 모두 통계적으로 유의한 차이가 나타났다. 마지막으로 코로나19 전과 후 모두 제약을 직면하는 순서는 내재적, 대인적, 구조적 순서로 나타났다.

Quantitative evaluation of industrial and commercial management’s promotion to economic growth based on hierarchical constraints

In order to improve the quantitative evaluation ability of industrial and commercial management on promoting economic growth, the design method of quantitative evaluation model of industrial and commercial management on promoting economic growth based on hierarchical constraints is proposed. the descriptive statistical analysis method to realize big data fusion mining for quantitative evaluation of business administration’s promotion to economic growth is used, and a hierarchical constraint parameter analysis model to realize node deployment model design of hierarchical constraint parameter analysis model is designed, fuzzy control and adaptive feature parameter identification are realized in the process of quantitative evaluation of industrial and commercial administration’s promotion to economic growth. Convergence control is realized in the process of quantitative evaluation based on pattern recognition and association distributed fusion. Fuzzy degree identification and optimization prediction of quantitative evaluation of industrial and commercial administration’s promotion to economic growth are realized in hierarchical constraint parameter analysis model. The simulation results show that the output accuracy and system stability of the quantitative evaluation of the promotion of business administration to economic growth are good.

Orientation Method of Distributed Measurement System Based on Hierarchical Geometric Constraints

Orientation Method of Distributed Measurement System Based on Hierarchical Geometric Constraints

Revisiting hierarchy: Deep learning with orthogonally constrained prior for classification

• A novel approach with hierarchical constraints over labels under deep learning framework. • Introduce weight decomposition to model parameter sharing along the root of the hierarchy to leafs. • Incorporate the orthogonal restrictions among adjacent nodes in the hierarchy. • Compared to competitive baselines, our approach yields significant better results when a few training data are available. Deep learning has attracted significant attention for its applications to a variety of classification problems, such as handwritten recognition, image classification and document categorization. One reason behind the success of deep learning can be attributed to its strong representation power with multiple layers of hidden variables. However, complex models are often encompassed with overfitting problems when limited training data is available. In this paper, we are interested in deep learning for classification with prior, where the set of labels are expressed in a hierarchy. In particular, we attempt to leverage knowledge transfer and parameter sharing among classes. We introduce the orthogonally constrained prior into deep learning, which exploits the following information among different classes: (1) introduce weight decomposition to model parameter sharing along the path from the root to leafs; (2) incorporate the orthogonal restrictions among adjacent nodes in the hierarchy. We test our method on challenge datasets in the case of a few training examples available, and show promising results compared to support vector machines and other deep learning methods .

Forecasting Swiss exports using Bayesian forecast reconciliation

This paper proposes a novel forecast reconciliation framework using Bayesian state-space methods. It allows for the joint reconciliation at all forecast horizons and uses predictive distributions rather than past variation of forecast errors. Informative priors are used to assign weights to specific predictions, which makes it possible to reconcile forecasts such that they accommodate specific judgmental predictions or managerial decisions. The reconciled forecasts adhere to hierarchical constraints, which facilitates communication and supports aligned decision-making at all levels of complex hierarchical structures. An extensive forecasting study is conducted on a large collection of 13,118 time series that measure Swiss merchandise exports, grouped hierarchically by export destination and product category. We find strong evidence that in addition to producing coherent forecasts, reconciliation also leads to substantial improvements in forecast accuracy. The use of state-space methods is particularly promising for optimal decision-making under conditions with increased model uncertainty and data volatility.

Senior's travel constraint, negotiation strategy and travel intention: Examining the role of social support

AbstractThis paper explores how travel constraints influence seniors' travel decision. By including social support for travel as a moderator in the hierarchical constraint model, we examined the effects of travel constraints, social support, and negotiation strategies on seniors' travel intentions. Face‐to‐face questionnaire interviews were conducted with Chinese seniors. The results were in accordance with the hierarchical constraint model in general, but also revealed some interesting findings: (a) for Chinese seniors, intrapersonal constraint (health constraint, habit constraint) and interpersonal constraint played a vital role in influencing travel intentions; (b) the negative effect of structural constraint was negotiable, but the negative impacts of intrapersonal and interpersonal constraints were hard to negotiate; (c) the effects of sub‐constraints at the same level were heterogeneous, for example, the effects of cost constraint were insignificant, while those of time and transportation constraints were significant; (d) social support for travel had a negative moderating role on the relationship between habit constraint and negotiation but a positive moderating role on the relationship between interpersonal constraint and negotiation. Both theoretical and practical implications are discussed.

Balzac Retranslated

Literary translation is tricky. Hardly ever do you hear a critic say that the translation of a book is “good”. In the best of cases, people pretend that, even though they have been reading a translation, they were in fact reading Balzac, or Dostoevsky, or any other author of universal renown. For those who are able to read the original text, the translation is more often than not rejected as “inaccurate”, “stylistically inadequate”, “loose”, “overly free”, “not doing justice to the original”, or simply “bad”. James Payn even claimed that Balzac “is not translatable, or when translated is not readable” (67). Yet Balzac was translated and retranslated many times in a variety of languages and in many ways. In this paper, the word retranslation will be used for the realisation of a new translation from the original source language into a target language in which a translation already exists, and relay translation for translations done from a translated source. As for the term translation, it will be extended, in the sense that Patrick O'Neill gives this term (6), to include adaptations such as movies, TV series, or even graphic novels, in any language, because adaptations, whatever the medium, are subjected to the same constraints as translations, creating effectively a new “language” to transfer the author’s story and message. Thus this paper will focus on how Balzac's novels have been extended when translated and/or adapted to other media, taking in consideration Roulet’s Discourse Analysis parameters (2001 44), that is, the hierarchical constraints related to the text structure; the linguistic constraints related to the syntactic or lexical norms of the language or linguistic variety that is being considered; and the situational constraints of the receiving culture. To do so, an analysis of the hierarchical constraints of translating, retranslating or adapting Balzac’s La Cousine Bette will be carried out, as well as of the linguistic constraints related to the translation of gender in Balzac’s short story Sarrasine, or to the translation of accents and other oral features in various novels; and finally of the situational constraints related to translating Balzac into English in the Victorian era, and into Chinese at the turn of the 20th century. From these parameters a new, prismatic view of Balzac’s creations will emerge, embodying the dialogue that translators and retranslators enable between cultures.

From livestock to families: taking pets to tourism activities in China

ABSTRACT This research aims to explore the phenomenon of taking pets to tourism activities in China based on the hierarchical leisure constraints model. In-depth interview with 23 participants were conducted. The results identified intrinsic and extrinsic motivations of taking pets to tourism activities, as well as the intrapersonal, interpersonal, and structural constraints. The analysis gave emphasis to the Chinese contextual characteristics, among which public acceptance and restrictions, and the strategy of sneaking pets into some tourism activities were highlighted. This paper provides insights into the salience of pet-human joint tourism behaviors in China and generates implications for pet owners and government.

Intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge

PurposeThe implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.Design/methodology/approachA spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.FindingsThe proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.Practical implicationsThe assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.Originality/valueThe spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

A deep neural network based hierarchical multi-label classification method.

With the accumulation of data generated by biological experimental instruments, using hierarchical multi-label classification (HMC) methods to process these data for gene function prediction has become very important. As the structure of the widely used Gene Ontology (GO) annotation is the directed acyclic graph (DAG), GO based gene function prediction can be changed to the HMC problem for the DAG of GO. Due to HMC, algorithms for tree ontology are not applicable to DAG, and the accuracy of these algorithms is low. Therefore, existing algorithms cannot satisfy the requirements of gene function prediction. To solve this problem, this paper proposes a DAG hierarchical multi-label classification algorithm, C2AE-DAGLabel algorithm. The C2AE-DAGLabel algorithm uses the Canonical Correlated AutoEncoder (C2AE) model as the classifier and designs a DAGLabel algorithm to solve the DAG hierarchical constraint problem. The DAGLabel algorithm can improve the classification accuracy by ensuring that the classification results meet the requirements of the hierarchical constraint. In the experiment, human gene data annotated with GO are used to evaluate the performance of the proposed algorithm. The experimental results show that compared with other state-of-the-art algorithms, the C2AE-DAGLabel algorithm has the best performance in solving the hierarchical multi-label classification problem for DAG.

Deep Reference Generation With Multi-Domain Hierarchical Constraints for Inter Prediction

Inter prediction is an important module in video coding for temporal redundancy removal, where similar reference blocks are searched from previously coded frames and employed to predict the block to be coded. Although traditional video codecs can estimate and compensate for block-level motions, their inter prediction performance is still heavily affected by the remaining inconsistent pixel-wise displacement caused by irregular rotation and deformation. In this paper, we address the problem by proposing a deep frame interpolation network to generate additional reference frames in coding scenarios. First, we summarize the previous adaptive convolutions used for frame interpolation and propose a factorized kernel convolutional network to improve the modeling capacity and simultaneously keep its compact form. Second, to better train this network, multi-domain hierarchical constraints are introduced to regularize the training of our factorized kernel convolutional network. For spatial domain, we use a gradually down-sampled and up-sampled auto-encoder to generate the factorized kernels for frame interpolation at different scales. For quality domain, considering the inconsistent quality of the input frames, the factorized kernel convolution is modulated with quality-related features to learn to exploit more information from high quality frames. For frequency domain, a sum of absolute transformed difference loss that performs frequency transformation is utilized to facilitate network optimization from the view of coding performance. With the well-designed frame interpolation network regularized by multi-domain hierarchical constraints, our method surpasses HEVC on average 6.1% BD-rate saving and up to 11.0% BD-rate saving for the luma component under the random access configuration.

Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

To fulfill the tasks of human-robot interaction (HRI), how to detect the specific human (SH) becomes paramount. In this paper, the deep learning approach by the integration of Single-Shot Detection, FaceNet, and Kernelized Correlation Filter (SSD-FN-KCF) is developed. From the outset, the SSD is employed to detect the human up to 8m using the RGB-D camera with 320 × 240 resolution. Afterward the omnidirectional mobile robot (ODMR) is driven to the neighborhood of 2.5~3.0 m such that the depth image can accurately estimate the detected human's pose. Subsequently, the ODMR is commanded to the vicinity of 1.0m and the orientation inside -60~60° with respect to the optical axis to identify whether he/she is the SH by the FaceNet. To reduce the computation time of the FaceNet and extend the SH's tracking, the KCF is employed to achieve the task of HRI (e.g., human following). Based on the image processing result, the required pose for searching or tracking (specific) human is accomplished by the image-based adaptive finite-time hierarchical constraint control. Finally, the experiment with the SH, who is far from and on the backside of the ODMR, validates the effectiveness and robustness of the proposed approach.

On general implicit hybrid iteration method for triple hierarchical variational inequalities with hierarchical variational inequality constraints

In this paper, we study the triple hierarchical variational inequality problem (THVIP, for short) with the hierarchical variational inequality constraint for finitely many nonexpansive mappings and an asymptotically nonexpansive mapping in a real Hilbert space. Based on Korpelevich's extragradient method, hybrid steepest-descent method, Mann's implicit iteration method and Halpern's iteration method, a general implicit hybrid iterative algorithm for solving this THVIP is proposed. Under mild conditions, the strong convergence of the iterative sequences generated by the algorithm is established. Our results improve and extend the corresponding results in the earlier and recent literature.

A qualitative examination of the perceived impact of bureaucratic managerialism on evidence-based practice implementation in Nigeria: a collective case study.

Evidence-based practice (EBP) is widely recognised as an essential aspect of contemporary healthcare delivery. However, the rise in cost containment and quest for profitability in healthcare management is found to be compromising implementation of evidence-based initiatives aimed at improving care quality. The aim of this work was to examine perspectives of nurses regarding the impact of bureaucratic managerialism on EBP implementation in the Nigerian acute care setting. A qualitative case study methodology was utilised to gather data from two large acute care settings. Drawing on semi-structured interviews, 12 staff nurses, 21 ward managers and 2 nurse managers were interviewed. Data were inductively analysed and themes generated. The managerial practice in this context is founded on bureaucratic managerialism, which in turn generated hierarchical constraints that denied nurses the opportunity to self-govern. Implementation of evidence-based initiatives was consequently opposed by the managerial desire to maximise throughput. There is need for nurse managers to have greater managerial influence, which would allow opportunities for implementing EBPs to be created. Managerial autonomy for nurse managers would allow them to create enabling environments capable of facilitating successful implementation.

Physician-assisted suicide travel constraints: thematic content analysis of online reviews

ABSTRACTConstraints to participating in leisure activities have been studied extensively. Yet given the sensitive nature of death and death-related topics, little effort has been invested in understanding travel constraints that limit people’s decisions to engage in physician-assisted suicide travel. As this form of travel is a relatively new area of interest in the tourism literature, this study investigates the public’s perceptions of physician-assisted suicide travel by extracting online comments from user-generated content. By applying thematic content analysis with a deductive approach, a pre-coding scheme was developed based on the hierarchical constraint model. Four travel constraints were identified: perceived incapability and lack of personal support (intrapersonal constraints), lack of suitable travel agencies and services (structural constraint), and complex travel decision making. The latter consisted of two sub-dimensions: individualistic decisions (intrapersonal constraint) and collectivistic decisions (interpersonal constraint). The findings of this study help to contextualise physician-assisted suicide practices and knowledge in a tourism context. Relevant practical implications are also discussed.

Negotiating Travel Constraints via Technology: A Study of Malay Muslim Women through a Hierarchical Constraint Model Perspective

Negotiating Travel Constraints via Technology: A Study of Malay Muslim Women through a Hierarchical Constraint Model Perspective

Robust semiparametric gene-environment interaction analysis using sparse boosting.

For the pathogenesis of complex diseases, gene-environment (G-E) interactions have been shown to have important implications. G-E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the "main effects, interactions" hierarchical constraint. Extensive methodological developments on G-E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long-tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables. To directly tackle these problems, a semiparametric model is assumed to accommodate nonlinear effects, and the Huber loss function and Qn estimator are adopted to accommodate long-tailed distributions/data contamination. A regression-based multiple imputation approach is developed to accommodate missingness in E variables. For model estimation and selection of relevant variables, we adopt an effective sparse boosting approach. The proposed approach is practically well motivated, has intuitive formulations, and can be effectively realized. In extensive simulations, it significantly outperforms multiple direct competitors. The analysis of The Cancer Genome Atlas data on stomach adenocarcinoma and cutaneous melanoma shows that the proposed approach makes sensible discoveries with satisfactory prediction and stability.

Chemical and Metabolic Controls on Dihydroxyacetone Metabolism Lead to Suboptimal Growth of Escherichia coli.

In this work, we shed light on the metabolism of dihydroxyacetone (DHA), a versatile, ubiquitous, and important intermediate for various chemicals in industry, by analyzing its metabolism at the system level in Escherichia coli Using constraint-based modeling, we show that the growth of E. coli on DHA is suboptimal and identify the potential causes. Nuclear magnetic resonance analysis shows that DHA is degraded nonenzymatically into substrates known to be unfavorable to high growth rates. Transcriptomic analysis reveals that DHA promotes genes involved in biofilm formation, which may reduce the bacterial growth rate. Functional analysis of the genes involved in DHA metabolism proves that under the aerobic conditions used in this study, DHA is mainly assimilated via the dihydroxyacetone kinase pathway. In addition, these results show that the alternative routes of DHA assimilation (i.e., the glycerol and fructose-6-phosphate aldolase pathways) are not fully activated under our conditions because of anaerobically mediated hierarchical control. These pathways are therefore certainly unable to sustain fluxes as high as the ones predicted in silico for optimal aerobic growth on DHA. Overexpressing some of the genes in these pathways releases these constraints and restores the predicted optimal growth on DHA.IMPORTANCE DHA is an attractive triose molecule with a wide range of applications, notably in cosmetics and the food and pharmaceutical industries. DHA is found in many species, from microorganisms to humans, and can be used by Escherichia coli as a growth substrate. However, knowledge about the mechanisms and regulation of this process is currently lacking, motivating our investigation of DHA metabolism in E. coli We show that under aerobic conditions, E. coli growth on DHA is far from optimal and is hindered by chemical, hierarchical, and possibly allosteric constraints. We show that optimal growth on DHA can be restored by releasing the hierarchical constraint. These results improve our understanding of DHA metabolism and are likely to help unlock biotechnological applications involving DHA as an intermediate, such as the bioconversion of glycerol or C1 substrates into value-added chemicals.

Y2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences

Jointly learning representations of 3D shapes and text is crucial to support tasks such as cross-modal retrieval or shape captioning. A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels. Hence the method suffers from a lack of detailed geometry. To resolve this issue, we propose Y2Seq2Seq, a view-based model, to learn cross-modal representations by joint reconstruction and prediction of view and word sequences. Specifically, the network architecture of Y2Seq2Seq bridges the semantic meaning embedded in the two modalities by two coupled “Y” like sequence-tosequence (Seq2Seq) structures. In addition, our novel hierarchical constraints further increase the discriminability of the cross-modal representations by employing more detailed discriminative information. Experimental results on cross-modal retrieval and 3D shape captioning show that Y2Seq2Seq outperforms the state-of-the-art methods.