Constraint Network Research Articles

QOL-02. SOCIAL NETWORKS CAN BE QUANTITATIVELY MEASURED AND NETWORKS WITH GREATER STRUCTURAL DIVERSITY ARE ASSOCIATED WITH HIGHER HUMAN FLOURISHING

Abstract BACKGROUND In patients with glioblastoma, the predicted progressive deterioration in physical and cognitive abilities leads to decreased quality of life for patients and their caregivers. Research has shown that one’s social environment is a major contributor to health outcomes. However, in this population, study of the social environment is a gap in the literature. METHODS We examined the personal social networks of 25 adult glioblastoma patients at one time between initial diagnosis and first cycle of adjuvant chemotherapy from March to September 2023. We additionally collected demographic, tumor measures, and the European Organization for Research and Treatment of Cancer Quality-of-Life for Patients with Brain Tumors (EORTC QLQ-BN20) and the Harvard Human Flourishing Index (HFI) measures. RESULTS The average age was 64.5 years old, 56% were female, and 84% had a Karnofsky Performance Status (KPS) of 70 or higher. Patients had large network sizes (mean of 8.8). Patients reported high levels of flourishing despite the challenging diagnosis. Flourishing was statistically significantly associated with lower network constraint (networks in which alters are less likely to be connected to each other) in a patient’s social network (p=0.046) and less lower extremity weakness (p=0.025), controlling for age, gender, income, tumor at size of diagnosis, and KPS. CONCLUSION Our study indicates personal social networks with more contacts who are not connected to each other are associated with greater flourishing. Quantifying the social networks of patients with glioblastoma has implications as an objective measure for supportive care trials. Moreover, standard quality-of-life measures may not adequately capture what it means to meaningfully live and flourish. Therefore, future studies should include how social networks and flourishing change over the course of illness and how this may also impact overall survival.

Digging into Depth and Color Spaces: A Mapping Constraint Network for Depth Super-Resolution

Scene depth super-resolution (DSR) poses an inherently ill-posed problem due to the extremely large space of one-to-many mapping functions from a given low-resolution (LR) depth map, which possesses limited depth information, to multiple plausible high-resolution (HR) depth maps. This characteristic renders the task highly challenging, as identifying an optimal solution becomes significantly intricate amidst this multitude of potential mappings. While simplistic constraints have been proposed to address the DSR task, the relationship between LR and HR depth maps and the color image has not been thoroughly investigated. In this paper, we introduce a novel mapping constraint network (MCNet) that incorporates additional constraints derived from both LR depth maps and color images. This integration aims to optimize the space of mapping functions and enhance the performance of DSR. Specifically, alongside the primary DSR network (DSRNet) dedicated to learning LR-to-HR mapping, we have developed an auxiliary degradation network (ADNet) that operates in reverse, generating the LR depth map from the reconstructed HR depth map to obtain depth features in LR space. To enhance the learning process of DSRNet in LR-to-HR mapping, we introduce two mapping constraints in LR space: (1) the cycle-consistent constraint, which offers additional supervision by establishing a closed loop between LR-to-HR and HR-to-LR mappings, and (2) the region-level contrastive constraint, aimed at reinforcing region-specific HR representations by explicitly modeling the consistency between LR and HR spaces. To leverage the color image effectively, we introduce a feature screening module to adaptively fuse color features at different layers, which can simultaneously maintain strong structural context and suppress texture distraction through subspace generation and image projection. Comprehensive experimental results across synthetic and real-world benchmark datasets unequivocally demonstrate the superiority of our proposed method over state-of-the-art DSR methods. Our MCNet achieves an average MAD reduction of 3.7% and 7.5% over state-of-the-art DSR method for ×8 and ×16 cases on Milddleburry dataset, respectively, without incurring additional costs during inference.

Tumor classification algorithm via parallel collaborative optimization of single- and multi-objective consistency on PET/CT

Malignant tumors still have a high incidence and mortality rate worldwide. Pathological examination remains the clinical gold standard for tumor diagnosis. However, some patients cannot undergo pathological examination due to advanced age and special lesion location. Therefore, making full use of PET/CT to assist doctors in tumor classification has important clinical significance. Since category labels are calibrated according to pathological images, it is difficult to obtain effective pathological category features directly using PET-CT image modeling. In response to this problem, this paper proposes a novel tumor classification algorithm. This method fully utilizes multi-gray-level 3D gray-level co-occurrence matrix and the proposed rough and fine constraint network under the constraint loss of rough and fine labels. Based on single- and multi-objective consistency, a parallel collaborative optimization method is proposed, including category consistency loss and feature specificity loss. To reduce the interference of redundant features, an improved Boruta feature selection method using multiple classifiers and multiple steps is proposed. The final result is obtained through a conditional weighted voting function. The proposed method shows excellent performance in both the submodels and the fusion model. We validated the proposed tumor classification method on three datasets and achieved good performance with the accuracy of 0.80–0.85 and F1-score of 0.78–0.88. The results indicate that the proposed method has good performance and generalization ability.

Reconstructing the local structures of Chinese ancient architecture using unsupervised depth estimation

Digitalization of ancient architectures is one of the effective means for the preservation of heritage structures, with 3D reconstruction based on computer vision being a key component of such digitalization techniques. However, Chinese ancient architectures are located in mountainous areas, and existing 3D reconstruction methods fall short in restoring the local structures of these architectures. This paper proposes a self-attention-guided unsupervised single image-based depth estimation method, providing innovative technical support for the reconstruction of local structures in Chinese ancient architectures. First, an attention module is constructed based on features extracted from architectural images learned by the encoder, and then embedded into the encoder-decoder to capture the interdependencies across local features. Second, a disparity map is generated using the loss constraint network, including reconstruction matching, smoothness of the disparity, and left-right disparity consistency. Third, an unsupervised architecture based on binocular image pairs is constructed to remove any potential adverse effects due to unknown scale or estimated pose errors. Finally, with the known baseline distance and camera focal length, the disparity map is converted into the depth map to perform the end-to-end depth estimation from a single image. Experiments on the our architecture dataset validates our method, and it performs well also well on KITTI.

Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies

In the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching.

On prime scenarios in qualitative spatial and temporal reasoning

The concept of prime implicant is a fundamental tool in Boolean algebra, which is used in Boolean circuit design and, recently, in explainable AI. This study investigates an analogous concept in qualitative spatial and temporal reasoning, called prime scenario. Specifically, we define a prime scenario of a qualitative constraint network (QCN) as a minimal set of decisions that can uniquely determine solutions of this QCN. We propose in this paper a collection of algorithms designed to address various problems related to prime scenarios, and also show how certain results can be useful for measuring the robustness of a QCN. In addition, we study the relationship between our notions in this paper and the notion of prime sub-QCN in the literature, and establish theoretical results in the process. Further, we devise a language based on the notion of prime scenario for knowledge compilation. Finally, an experimental evaluation is performed with instances of Allen's Interval Algebra and RCC8 to assess the efficiency of our algorithms and, hence, also the difficulty of the newly introduced problems here.

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data.

Atmospheric scattering model and dark channel prior constraint network for environmental monitoring under hazy conditions

Environmental monitoring systems based on remote sensing technology have a wider monitoring range and longer timeliness, which makes them widely used in the detection and management of pollution sources. However, haze weather conditions degrade image quality and reduce the precision of environmental monitoring systems. To address this problem, this research proposes a remote sensing image dehazing method based on the atmospheric scattering model and a dark channel prior constrained network. The method consists of a dehazing network, a dark channel information injection network (DCIIN), and a transmission map network. Within the dehazing network, the branch fusion module optimizes feature weights to enhance the dehazing effect. By leveraging dark channel information, the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the output of the deep learning model aligns with physical laws, we reconstruct the haze image using the prediction results from the three networks. Subsequently, we apply the traditional loss function and dark channel loss function between the reconstructed haze image and the original haze image. This approach enhances interpretability and reliability while maintaining adherence to physical principles. Furthermore, the network is trained on a synthesized non-homogeneous haze remote sensing dataset using dark channel information from cloud maps. The experimental results show that the proposed network can achieve better image dehazing on both synthetic and real remote sensing images with non-homogeneous haze distribution. This research provides a new idea for solving the problem of decreased accuracy of environmental monitoring systems under haze weather conditions and has strong practicability.

Pixel-associated autoencoder for hyperspectral anomaly detection

Autoencoders (AEs) are central to hyperspectral anomaly detection, given their impressive efficacy. However, the current methodologies often neglect the global pixel similarity of the hyperspectral image (HsI), thereby limiting reconstruction accuracy. This study introduces an innovative pixel-associated AE approach that leverages pixel associations to augment hyperspectral anomaly detection. First, a dictionary construction methodology was introduced based on superpixel distance estimation to construct distinct dictionaries for background and local anomalies. Second, to recognize pixel similarities, the similarity metric of each pixel from the original HsI to the background dictionary and to the local anomaly dictionary was employed as the AE network input in lieu of the original HsI. Third, a dual hidden-layer feature similarity constraint network was proposed to enhance the reconstruction error of background and anomaly targets. Finally, the reconstruction error was utilized to score the anomaly target. The proposed method was benchmarked against other state-of-the-art techniques using synthetic and real HsI datasets to assess its effectiveness. The experimental results demonstrated the superior performance of the proposed method, outperforming the alternatives.

A multiscale anisotropic polymer network model coupled with phase field fracture

AbstractThe study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local‐global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double‐edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. The results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber‐like materials.

Decomposing Constraint Networks for Calculating c-Representations

It is well-known from probability theory that network-based methods like Bayesian networks constitute remarkable frameworks for efficient probabilistic reasoning. In this paper, we focus on qualitative default reasoning based on Spohn’s ranking functions for which network-based methods have not yet been studied satisfactorily. With constraint networks, we develop a framework for iterative calculations of c-representations, a family of ranking models of conditional belief bases which show outstanding properties from a commonsense and formal point of view, that are characterized by assigning possible worlds a degree of implausibility via penalizing the falsification of conditionals. Constraint networks unveil the dependencies among these penalty points (and hence among the conditionals) and make it possible to compute the penalty points locally on so-called safe sub-bases. As an application of our framework, we show that skeptical c-inferences can be drawn locally from safe sub-bases without losing validity.

Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification

Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the network. To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion. Secondly, to fully exploit these complex occluded images, we develop a DualPath Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dualpath interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.

Encoding Constraints as Binary Constraint Networks Satisfying BTP

Recently, the Binary Constraint Tree (BCT), a tree structured Binary Constraint Network (BCN), has been shown to be more succinct than various ad-hoc constraints. In this paper, we investigate the modelling power of a well-known tractable hybrid class generalizing BCT, i.e. the class of BCNs satisfying Broken Triangle Property (BTP) called BTP Networks (BTPNs). We show that the consistency checker of BTPN can be computed by polysize monotone circuit, thus, some global constraints cannot be encoded as polysize BTPN, such as the AllDifferent and Linear constraints. Then our study reveals that BTPN is strictly more succinct than the DNNF constraint and all 14 ad-hoc constraints discussed in (Wang and Yap 2023), such as the context-free grammar, BCT and smart table constraints. Furthermore, we also show that BTPN is as powerful as DNNF in terms of computing various operations and queries. In addition, we prove that it is NP-hard to determine the minimum sized BTPN encoding a constraint.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

Geometric information constraint 3D object detection from LiDAR point cloud for autonomous vehicles under adverse weather

3D object detection, as the core of the autonomous vehicle perception module, is essential for efficient transportation and comfortable experiences. However, the challenge of 3D object detection under adverse weather conditions hinders the advancement of autonomous vehicles to higher levels. Hence, achieving accurate 3D object detection under adverse weather conditions is increasingly crucial as it forms the foundation for trajectory planning and driving strategy making in autonomous vehicles, thereby revolutionizing transportation modes for both goods and passengers. Advances in Light Detection and Ranging (LiDAR) technology have facilitated the development of 3D object detection in the past few years. Adverse weather, which inevitably occurs in real-world driving scenarios, could degrade measurement accuracy and point density of LiDAR and lead to particle interference. Detecting accurate 3D bounding boxes from sparse, incomplete point clouds with particle interference is difficult. Therefore, this research presents a novel geometric information constraint network for 3D object detection tasks from LiDAR point clouds under adverse weather (GIC-Net). In this study, we focus on how to incorporate geometric location information and line geometric feature information into the network against adverse weather. Further, we propose a geometric location constrained backbone module (GLC) to reduce rain and snow particle interference and ensure sufficient receptive fields. Then, we design a line geometric feature constraint module (LGFC) to add line constraints of 3D bounding boxes into the training process. Finally, a line loss function is designed, and features from the GLC and LGFC modules are fed into the multi-task detection head for accurate 3D bounding box prediction. Experiments on the Canadian Adverse Driving Conditions (CADC) autonomous vehicle dataset demonstrate the superiority of our method over six other state-of-the-art methods under adverse weather, which is at least 13.32 %, 4.67 %, and 10.44 % mAP higher than the other compared methods in the car, truck, and pedestrian classes respectively. Also, we further verify the better generalization ability of our network compared to other methods.

MCCSeg: Morphological embedding causal constraint network for medical image segmentation

Medical image multi-object segmentation aims to accurately extract each object that is great significant for the medical image analysis. Although several methods based on deep learning have been widely developed, it is still challenging due to the similarities of different objects, the significant morphological variability, data heterogeneity, and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Embedding Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed to decorrelate features by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) is employed to solve nonlinear dependency problems and remove correlations between features to mitigate the influence of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphological information for enhancing feature representation, which is concatenated into the deep supervision module in the decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, achieving improvements of 3.76% and 5.41% in terms of DICE and HD95 scores on Synapse dataset, respectively. The ablation experiments further verify the effectiveness of MCCSeg in improving generalization.

Рассуждения с темпоральными ограничениями

The work deals with the organization of temporal reasoning basing on constraint satisfaction methods. The definition of the constraint satisfaction problem and the notion of constraint consistency are given. The possibilities of epresentation of a planning problem as an interval constraint network are considered. As a mathematical apparatus for the formalization of temporal reasining, the Allen’s interval algebra is described, which main operations are composition and the intersection of temporal relations. A path consistency algorithm is given that implements one of the types of local consistency on interval constraint network and uses computations based on operations of interval algebra. An example of the application of this algorithm is presented. In the conclusion the prospects for the development of methods of temporal reasoning are considered.

Enriching interactive explanations with fuzzy temporal constraint networks

Humans often use expressions with vague terms which play a fundamental role for effective communication. These expressions are successfully modeled with fuzzy technology, but they are not usually integrated yet with Natural Language Processing models and techniques. Large-scale pre-trained language models yield excellent results in many language tasks, but they have some drawbacks such as their lack of transparency and thorough temporal reasoning capabilities. Therefore, the use of such models may provoke inconsistent or incorrect dialogues in the context of conversational agents which were aimed at providing users of intelligent systems with interactive explanations.In this paper, we propose a model for fuzzy temporal reasoning to overcome some inconsistencies detected in pre-trained language models in a specific application domain of a conversational agent carefully designed for providing users with explanations which are endowed with a good balance between naturalness and fidelity. More precisely, starting from a knowledge graph that provides an intuitive representation of the entities and relations in the application domain, we describe how to map the temporal information onto a fuzzy temporal constraint network. This formalism allows to represent imprecise temporal information and provides mechanisms for checking consistency in conversations.In addition, as a proof of concept, we have developed TimeVersa, a conversational agent which integrates the proposed model into an application domain (i.e., a virtual assistant for tourists) that requires handling imprecise temporal constraints. We illustrate in a use case how the agent can identify temporal inconsistencies and answer queries related to temporal information properly. Results after a user study report that users' perception of consistency is significantly higher in a conversation with TimeVersa than in a similar conversation using the well-known GPT-3 Large Language Model, when vague temporal information is involved. The proposed approach is a step forward for developing conversational agents operating in application domains that require temporal reasoning under uncertainty.

SemiPSCN: Polarization Semantic Constraint Network for Semi-Supervised Segmentation in Large-Scale and Complex-Valued PolSAR Images

SemiPSCN: Polarization Semantic Constraint Network for Semi-Supervised Segmentation in Large-Scale and Complex-Valued PolSAR Images

Point‑Voxel Consistency Constraint Network for LiDAR Point Cloud Classification Under Urban Scenes

Point‑Voxel Consistency Constraint Network for LiDAR Point Cloud Classification Under Urban Scenes