Hierarchical Constraints Research Articles

Perspectives on Constrained Forecasting

This expository paper discusses Bayesian decision analysis perspectives on problems of constrained forecasting. Foundational and pedagogic discussion contrasts decision analytic approaches with the traditional, but typically inappropriate, inferential approach. Illustrative examples include development of novel constrained point forecasting and entropic tilting methodology to explore consistency of a predictive distribution with an imposed or hypothesized constraint. Linear, aggregate constraints define illuminating examples that relate to broadly important problems involving aggregate and hierarchical constraints in commercial and economic forecasting. Discussion explores the impact of different loss functions, questions of how constrained forecasting is impacted by dependencies among outcomes being predicted, and promotes the broader use of decision analysis including routine evaluation of predictive distributions of loss under chosen forecasts/decisions. Extensions to more general constrained forecasting problems, connections with broader interests in forecast reconciliation and other considerations are noted.

Overcoming Barriers to Healthcare Reform: A Call to Action.

The success of healthcare reform hinges on policymaker, regulator, and administrator actions that shape policies at various levels. These policies can either facilitate or hinder the practice of healthcare professionals and collaborative work environments. It is imperative for all healthcare professionals to fully utilize their education and certification, as fostering an equitable workplace culture is vital for retaining staff and improving access to care. Using nurse practitioners (NPs) as an exemplar, this article aims to specify systemic barriers to healthcare reform and call for policymakers, regulators, and clinical agency administrators to enact change. Barriers to NP practice include restrictive oversight by external stakeholders, financial incentives for indirect billing, and hierarchical constraints that limit NP contributions to the healthcare system. The growing healthcare provider shortage disproportionately impacts primary care and rural settings. NPs are increasingly more likely to fill these roles than medical doctors and have documented positive patient health outcomes. Removing systemic obstacles for NP practice increases access to care. Nursing-the largest healthcare workforce with diverse roles-operates under complex oversight from multiple organizations for licensure, accreditation, certification, and education. The recent trend of external stakeholders influencing and requiring additional oversight has created barriers to nursing practice. Despite national education, accreditation, and certification standards, nursing licensure and practice are increasingly negotiated with external stakeholders and supervised at the state and institutional levels. Supporting all healthcare professionals to practice according to their education and certification can advance healthcare reform, address workforce shortages, increase access to care, and improve health.

Complex-valued image reconstruction for compressed sensing MRI using hierarchical constraint

In Magnetic Resonance Imaging (MRI), the sequential acquisition of raw complex-valued image data in Fourier space, also known as k-space, results in extended examination times. To speed up the MRI scans, k-space data are usually undersampled and processed using numerical techniques such as compressed sensing (CS). While the majority of CS-MRI algorithms primarily focus on magnitude images due to their significant diagnostic value, the phase components of complex-valued MRI images also hold substantial importance for clinical diagnosis, including neurodegenerative diseases. In this work, complex-valued MRI reconstruction is studied with a focus on the simultaneous reconstruction of both magnitude and phase images. The proposed algorithm is based on the nonsubsampled contourlet transform (NSCT) technique, which offers shift invariance in images. Instead of directly transforming the complex-valued image into the NSCT domain, we introduce a wavelet transform within the NSCT domain, reducing the size of the sparsity of coefficients. This two-level hierarchical constraint (HC) enforces sparse representation of complex-valued images for CS-MRI implementation. The proposed HC is seamlessly integrated into a proximal algorithm simultaneously. Additionally, to effectively minimize the artifacts caused by sub-sampling, thresholds related to different sub-bands in the HC are applied through an alternating optimization process. Experimental results show that the novel method outperforms existing CS-MRI techniques in phase-regularized complex-valued image reconstructions.

Deep bidirectional hierarchical matrix factorization model for hyperspectral unmixing

Existing deep nonnegative matrix factorization-based approaches treat shallow and deep layers equally or with similar strategies, neglecting the heterogeneous physical structures between the shallow and progressively deeper layers, thus failing to explore the latent different sub-manifold structures in the hyperspectral image. In this paper, we propose a deep nonnegative matrix factorization model with bidirectional constraints to achieve hyperspectral unmixing. The sub-manifold structures in hyperspectral image are fully exploited by filtering and penalizing the shallow abundance layer with a denoised regularizer and a manifold regularizer. In contrast to the shallow abundance layer, the remaining layers are constrained by an extremely common regularizer to avoid over-denoising and maintain fidelity. In this way, the fine cues between different substances are exploited to a large extent. Additionally, the overall reconstruction error can be well controlled because the performance of the designed feedback mechanism can be fine-tuned by the inverse hierarchical constraints. Finally, we employ Nesterov's optimal gradient method to solve the optimization problem effectively. Experiment results are conducted on both synthetic datasets and real datasets, and all results show that the proposed method is superior to recent canonical unmixing methods.

Optimized Design Method Based on Parallel Stepwise Hierarchical Constraints and its Application for 1-DOF Six-Bar Finger

Optimized Design Method Based on Parallel Stepwise Hierarchical Constraints and its Application for 1-DOF Six-Bar Finger

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

Hierarchical Constraints on the Distribution of Attention in Dynamic Displays.

Human vision is remarkably good at recovering the latent hierarchical structure of dynamic scenes. Here, we explore how visual attention operates with this hierarchical motion representation. The way in which attention responds to surface physical features has been extensively explored. However, we know little about how the distribution of attention can be distorted by the latent hierarchical structure. To explore this topic, we conducted two experiments to investigate the relationship between minimal graph distance (MGD), one key factor in hierarchical representation, and attentional distribution. In Experiment 1, we constructed three hierarchical structures consisting of two moving objects with different MGDs. In Experiment 2, we generated three moving objects from one hierarchy to eliminate the influence of different structures. Attention was probed by the classic congruent-incongruent cueing paradigm. Our results show that the cueing effect is significantly smaller when the MGD between two objects is shorter, which suggests that attention is not evenly distributed across multiple moving objects but distorted by their latent hierarchical structure. As neither the latent structure nor the graph distance was part of the explicit task, our results also imply that both the construction of hierarchical representation and the attention to that representation are spontaneous and automatic.

HCDR-based motion planning for mobile manipulators with the continuous trajectory task

Mobile manipulators have been widely used in modern industrial fields due to their advantages of large operating workspace and high motion dexterity. For the application, the path planning has been a core foundation for its motion control. However, as the environmental complexity and performance requirements increase, existing methods are limited by low execution efficiency, and lack of comprehensive consideration of motion performances. A motion planning technique is proposed for mobile manipulators with continuous trajectory task. In this technique, a hierarchical constraints dimension reduction (HCDR) strategy is presented based on the task decomposition and performance requirements analysis of each stage. HCDR strategy aims to improve the execution efficiency of the algorithm through local operations of performance constraints and the local simplification of the distance calculation at different stages of the task. Models of inverse kinematics and performance indexes are established to provide basis for constraint conditions, including distance models with different accuracy, manipulability index model and trajectory tracing error model. Two optimization strategies are presented to solve the motion planning problem considering the overall performance of the system. And simulation results show that the three-level strategy has the higher convergence efficiency than the bi-level strategy, as well as can guarantee the motion performance of the system.

Identifiability of Hierarchical Latent Attribute Models

Hierarchical Latent Attribute Models (HLAMs) are a family of discrete latent variable models that are attracting increasing attention in educational, psychological, and behavioral sciences. The key ingredients of an HLAM include a binary structural matrix and a directed acyclic graph specifying hierarchical constraints on the configurations of latent attributes. These components encode practitioners' design information and carry important scientific meanings. Despite the popularity of HLAMs, the fundamental identifiability issue remains unaddressed. The existence of the attribute hierarchy graph leads to degenerate parameter space, and the potentially unknown structural matrix further complicates the identifiability problem. This paper addresses this issue of identifying the latent structure and model parameters underlying an HLAM. We develop sufficient and necessary identifiability conditions. These results directly and sharply characterize the different impacts on identifiability cast by different attribute types in the graph. The proposed conditions not only provide insights into diagnostic test designs under the attribute hierarchy, but also serve as tools to assess the validity of an estimated HLAM.

A Safety-Aware Framework for Physical Human-Robot-Human Interaction With Hierarchical Constraints

A Safety-Aware Framework for Physical Human-Robot-Human Interaction With Hierarchical Constraints

EACE: A document-level event argument extraction model with argument constraint enhancement

There are two key challenges remaining for the document-level event argument extraction tasks: long-range dependency and same-role argument assignment. The existing methods could not effectively handle the above two challenges at the same time, resulting in argument misidentification and over- or under-extraction, reducing the precision and recall of event argument extraction. In this paper, we propose a document-level event argument extraction model with argument constraint enhancement (EACE), which constructs the argument constraint tree using the hierarchical constraints between arguments to address the above two challenges simultaneously. Specifically, EACE first constructs an argument constraint decoder and uses abstractive summarization to establish the long-range hierarchical constraint relationships between arguments and to obtain the trunk structure of the argument constraint tree, which improves argument identification precision. Secondly, EACE calculates dynamic branch thresholds to expand the branch structure of the argument constraint tree and improve the recall of argument extraction. Extensive experiments on WIKIEVENTS and RAMS have shown that EACE outperforms the baseline models by 2.2% F1 and 0.2% F1, respectively. Moreover, it exceeds the baseline model (PAIE) by up to 17.1% F1 in the same-role argument assignment setting in WIKIEVENTS.

A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis

Medical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is different from, but associated with, the image domain. Such methods exploit either single-domain or dual-domain information and suffer from inefficient information coupling across domains. Moreover, these models are usually designed specifically and not general enough for different tasks. Here we present a generalized dual-domain generative framework to facilitate the connections within and across domains by elaborately-designed hierarchical consistency constraints. A multi-stage learning strategy is proposed to construct hierarchical constraints effectively and stably. We conducted experiments for representative generative tasks including low-dose PET/CT reconstruction, CT metal artifact reduction, fast MRI reconstruction, and PET/CT synthesis. All these tasks share the same framework and achieve better performance, which validates the effectiveness of our framework. This technology is expected to be applied in clinical imaging to increase diagnosis efficiency and accuracy.

Seeking tourism in a social context: an examination of Chinese rural migrant workers’ travel motivations and constraints

ABSTRACT This study examines travel motivations and constraints of rural migrant workers in China as a socially disadvantaged group. Through 20 in-depth interviews, the study identified 21 motivations in three categories: personal seeking, interpersonal seeking and personal escaping. Nine travel constraint factors were also identified. The identified motivations and constraints were interpreted through the Escaping-Seeking tourism motivation framework and the Hierarchical Constraint Model. The study concludes that personal seeking motivations predominate rural migrant workers’ travel motivations, whereas structural constraints are more influential than intrapersonal and interpersonal constraints. Social context was found to be a significant confounding factor in determining travel motivations and constraints of this social group. This study contributes to the theories of travel motivations and constraints.

Intention as Hierarchical Constraints in Human Planning

Intention as Hierarchical Constraints in Human Planning

A hierarchical constraint-based graph neural network for imputing urban area data

Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to estimate the values of unsampled regular areas, while minimal attention has been paid to the values of irregular areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas at different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset show that the proposed model outperforms state-of-the-art baselines and exhibits robustness. The model is adaptable to numerous geographic applications, including traffic management, public safety, and public resource allocation.

MOHP-EC: A Multiobjective Hierarchical Prediction Framework for Urban Rail Transit Passenger Flow

For sophisticated management, advertisement placement, and epidemic prevention control of urban rail transit (URT), accurate and real-time predictions of passenger flows at different levels are of great importance. Unlike traditional prediction tasks, hierarchical prediction (HP) requires that the hierarchical constraints be satisfied as much as possible (the sum of the predicted passenger flow of child nodes should nearly equal the parent node) to achieve realistic predictions. This article proposes a multiobjective HP (MOHP) framework with an error compensation (EC) mechanism for predicting URT passenger flow with a hierarchical structure. Three components are included: the initial prediction module, the EC module, and the hierarchical coordination module. In the initial prediction module, the initial passenger flow prediction of each layer is carried out. The EC model is developed based on proportional-integral-derivative control to compensate for the initial predicted value of every layer. As a final step, a trainable HP model is constructed based on deep learning to coordinate the prediction values of each layer. As examples, we construct three scenarios of passenger flow hierarchy based on the URT system in Wuxi, China. The constructed prediction framework is used to conduct experimental analyses. As a result, the MOHP-EC prediction framework could satisfy the hierarchical constraints and use the passenger flow hierarchical information to reduce prediction errors. The mean absolute error was reduced by 35%, and the root-mean-square error was reduced by 39%.

Mechanism innovation in multi-hierarchical environmental regulation towards air quality improvement

This study takes the central, provincial and municipal Environmental Protection Interviews (multi-hierarchical EPI) as a mechanism innovation for precise environmental regulation in Yangtze River Delta (YRD) and constructs a multi-cutoff regression discontinuity design to accurately measure the policy effects of multi-hierarchical EPI on air quality improvement in YRD. Overall, the findings demonstrate that EPI at all levels is correlated with improving the air quality for interviewed cities in the short term, which tends to weaken after 3–6 months and may even rebound to the initial level after a longer period. The improvement effect of EPI on air quality is primarily reflected in controlling the pollutant concentration of PM2.5, PM10 and NO2. Moreover, there were great differences in the policy effects of the multi-hierarchical EPI for air quality improvement amongst cities in YRD. The higher administrative level of EPI, the stronger policy effects of the corresponding EPI were. The provincial and municipal EPI are likely to involve initiative regulation and exert greater effects. Some cities under the provincial and municipal EPI could break through the hierarchical constraints and shift to stronger air quality improvement effects, providing new reference for China's precise environmental governance mechanism.

Multimodal cues to intonational categories: Gesture apex coordination with tonal events

This study argues for a multimodal view of the identification, representation, and implementation of intonational structure, with evidence from gesture apex-tone coordination in Turkish. Many studies have reported consistent synchronisation of atomic prominence markers across modalities (i.e., pitch accents and gesture apexes). This is prima facie evidence that gesture and prosody are implemented together, and therefore the former can play a role in the identification and perception of the latter through apex-tone synchronisation. However, only few studies considered the full intonational context when investigating synchronisation (e.g., potential alignment of apexes with boundary tones). This is particularly relevant for Turkish as there is disagreement in the literature about whether all words in Turkish bear a pitch accent. In this study, we test the synchronisation of apexes with all intonational events in Turkish natural speech data annotated for gesture and prosody, resulting in 820 gesture apex and 3697 tonal event annotations. The study uses syllable duration (160ms) to determine synchronisation between these anchors via equivalence tests while also integrating gestural and prosodic context as factors that can affect the temporal distance between these units through mixed-effects linear regression. The findings showed that apexes were chiefly synchronised with pitch accents (71%), indicating that prominence was the primary constraint for synchronisation. However, analysis of cases with no prosodic prominence provides the first evidence for a hierarchical constraint on synchronisation, since apexes were preferentially synchronised with the tones marking prosodic words (76%) and not with the markers of prosodic constituents higher in the hierarchy. This finding supports the claim that there may be accentless words in Turkish since the absence of prominence caused a systematic shift in the synchronisation behaviour of apexes. More generally, the study shows how multimodal evidence from gesture can be used in the identification of phonological categories, and that prosodic structure is likely to be expressed through multimodal cues as a composite signal.

Using Computational Models to Test Syntactic Learnability

We studied the learnability of English filler-gap dependencies and the “island” constraints on them by assessing the generalizations made by autoregressive (incremental) language models that use deep learning to predict the next word given preceding context. Using factorial tests inspired by experimental psycholinguistics, we found that models acquire not only the basic contingency between fillers and gaps, but also the unboundedness and hierarchical constraints implicated in the dependency. We evaluated a model’s acquisition of island constraints by demonstrating that its expectation for a filler-gap contingency is attenuated within an island environment. Our results provide empirical evidence against the argument from the poverty of the stimulus for this particular structure.

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine

BackgroundThe research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint.ResultsTo favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect.ConclusionsAdaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.