Explicit Relations Research Articles

A knowledge-guided process planning approach with reinforcement learning

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.

$\mathrm{BC}_{2}$-Type Multivariable Matrix Functions and Matrix Spherical Functions

Matrix spherical functions associated to the compact symmetric pair (\mathrm{SU}(m+2), \mathrm{S}(\mathrm{U}(2)\times \mathrm{U}(m))) , m\geq 2 , having a reduced root system of type \mathrm{BC}_{2} , are studied. We consider an irreducible K -representation (\pi,V) arising from the \mathrm{U}(2) -part of K , and the induced representation \mathrm{Ind}_{K}^{G} \pi splits multiplicity-free. The corresponding spherical functions, i.e. \Phi \colon G \to \mathrm{End}(V) satisfying \Phi(k_{1}gk_{2})=\pi(k_{1})\Phi(g)\pi(k_{2}) for all g\in G , k_{1},k_{2}\in K , are studied by examining certain leading terms which involve hypergeometric functions. This is done explicitly using the action of the radial part of the Casimir operator on these functions and their leading terms. To suitably grouped matrix spherical functions we associate two-variable matrix orthogonal polynomials giving a matrix analogue of Koornwinder’s 1970s two-variable orthogonal polynomials, which are Heckman–Opdam polynomials for \mathrm{BC}_{2} . In particular, we find explicit orthogonality relations with the matrix polynomials being eigenfunctions to an explicit second-order matrix partial differential operator. The scalar part of the matrix weight is less general than Koornwinder’s weight.

Designing fractional-order ramp controller with explicit formulas using non-integer plant parameters

This paper aims to achieve two goals. First, the tracking problem with a ramp-type setpoint and disturbance is addressed, where classical methods provide steady-state error. Second, the available fractional-order controller designs in the literature are based on complex calculations. This paper presents a design scheme that employs a target loop-based controller, allowing users to compute tunable parameters without approximation or assumption. The control structure is capable of handling both the step and ramp signals. The main advantage of the proposed design is that it can be analytically tuned using the maximum sensitivity and phase-margin specifications of choice. Explicit relationships are established for the new fractional-order ramp controller (FoRC) parameters to facilitate tuning. The numerical investigations revealed improved servo and regulatory responses compared to recently published strategies. The design controller ensures stability and zero steady-state error as the ramp input increases. Furthermore, hardware verification demonstrates the scheme’s superior performance on a two-tank level loop, even with the influence of disturbances and modeling variations.

The 3D-QSAR Study, Molecular Docking, and ADMET Analysis of Darunavir Derivatives of HIV-1 Protease Inhibitors

Background: Acquired Immunodeficiency Syndrome (AIDS) is one of most prevalent infectious diseases in the world , and HIV-1 protease (PR) is a vital target of drug design. Nowadays, three-dimensional quantitative structure-activity relationships (3D-QSAR) are applied to help design new protease inhibitions (PIs). Objective: The primary objective of this study is to apply the 3D-QSAR study to a series of 42 derivatives of Darunavir (DRV) and to design new molecules possessing high antivirus activity. Methods: Partial Least Squares (PLS) were used to cross-validate the dataset of compounds, and the optimal number of principal components (ONC), cross-validate coefficient (q²), standard error of estimate (SEE), non-cross-validated correlation coefficient (R²) and fisher test value (F) were calculated to assess model robustness. In this study, the CoMSIA-DAH model (q²=0.754, r²= 0.988, rpred2=0.57) possessed the highest predicted activity. Newly designed molecules were analyzed by docking studies with compound 25 taken as a template. Results: Within eight newly designed drugs, compound N02 possessed the highest antivirus activity (IC50=0.00461 nM) predicted by the CoMSIA-DAH model. The Surflex-Dock module of SYBYL-X 2.0 was used to affirm the predicted anti-PR activity of the newly designed compounds and the results of docking complex structure could be visualized. All newly designed molecules were in agreement with CSore above four and the docking study revealed that Asp29, Asp30, Ile50, Asp124, Asp128, Asp129 and Ile149 were critical residues in the process of inhibiting PR. Conclusion: One of the main aspects of this study is the successful design of a series of molecules with excellent investigatory values, which elucidates explicit quantitative structure-activity relationships of DRV derivatives and will provide significant suggestions for future pharmaceutical research.

Generalized face forgery detection with self-supervised face geometry information analysis network

The emergence of high-quality deepfake facial videos has raised concerns about the security of facial images. Existing face forgery detectors mainly tend to locate a specific forgery region of the human face for detection, which achieves satisfactory performance with known forgery patterns presented in the training set. However, with the continuous advancements in face forgery technology, this approach becomes less reliable with new forgery patterns that emerge. Towards this end, we proposed a novel Self-supervised Face Geometry Information Analysis Network (SF-GAN) method for generalized face forgery detection. SF-GAN effectively leverages the relationships among informative regions based on information theory. Drawing from information theory, regions with high uncertainty tend to contain more valuable information. Our methodology integrates a self-supervised learning mechanism, enabling the precise identification of multiple informative regions. Furthermore, we leverage facial geometry by establishing both explicit and latent geometric relationships through the use of Graph Convolutional Networks (GCNs). Within our framework, facial landmarks and informative regions are depicted as nodes in the GCNs. By analyzing the geometric relationships between the graph of facial landmarks and the graph of informative regions, we are able to identify valid anomalous regions, thereby minimizing uncertainty. Our proposed model gains a comprehensive understanding of common information in face forgery images. Extensive experiments on eight large-scale benchmark datasets: FaceForensics++ (FF++), WildDeepfake (WDF), Celeb-DF v2 (CDF), DeepFake Detection Challenge (DFDC), DFDC preview (DFDC-P), Deepfake Detection (DFD), DeeperForensics-1.0 (DF-1.0) and ForgeryNIR, show that the proposed method is comparable to state-of-the-arts and exhibits better generalizability. Specifically, our SF-GAN, when trained on high-quality FF++ data, achieves an impressive AUC of 76.43% on the CDF dataset.

Temperature-dependent charge transport measurements unveil morphological insights in non-fullerene organic solar cells

The morphological analysis of bulk heterojunction (BHJ) active layer stands as a critical imperative for advancing the performance of future organic solar cells. Conventional characterization tools employed for morphological investigation often require substantial resources, both in cost and physical space, thereby imposing restraints on research endeavors in this domain. Here, we extend the application of charge carrier transport characterization beyond conventional mobility assessments, utilizing it as a table-top method for preliminary morphological screening in organic thin films. The investigation focuses on several high-performance BHJ systems that utilize typical “Y” non-fullerene acceptors. It involves in-depth transport studies, including temperature- and field-dependent transport characterizations. The resulting transport data are analyzed in detail using the Gaussian disorder model to extract key transport parameters, specifically the high-temperature limited mobility (μ∞) and positional disorder (∑). Integrating these transport parameters with morphological insights obtained through various characterization tools—including x-ray scattering, sensitive spectroscopy, and quantum chemistry simulation—provides a deep understanding of the intricate interplay between charge transport properties and morphological characteristics. The results reveal explicit relationships, associating μ∞ with the degree of molecular stacking in BHJs and ∑ with the structural disorder in molecule skeleton. Our findings point to the promising potential of utilizing a simple transport characterization technique for the early stage evaluation of thin film packing and geometric properties of organic materials.

LAAP: Learning the Argument of An Entity with Event Prompts for document-level event extraction

Document-level Event Extraction (DEE) aims to identify event types within a document and extract their corresponding arguments, which is essential for structured information provision in various NLP applications. Unlike sentence-level extraction, DEE requires handling events and arguments scattered across a document. Existing methods often focus on intricate feature interactions, neglecting explicit argument-entity relationships. We introduce a novel method, Learning the Argument of an Entity with Event Prompts (LAAP), which constructs event prompts for type detection, incorporating sentence placeholders to elicit event-specific information. Additionally, we propose an entity argument learning strategy that narrows down entity types to find the most suitable one. Experiments on the ChFinAnn and three other public datasets show that our method surpasses state-of-the-art approaches in accuracy and effectiveness.

Modeling contextual goals and detecting context conflicts from BDD-based user stories

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development.

Public Behavior and Emotion Correlation Mining Driven by Aspect From News Corpus.

Emotion motivates behavior. Investigating the correlation between behavior and emotion, an often overlooked perspective, plays a significant role in uncovering the underlying motives behind behaviors and the intrinsic cause-effects of social events. This article proposes a methodology for mining the correlation between public behavior and emotion using daily news data. Initially, aspect-emotion-reaction (A-E-R) triplets are extracted and generalized, encompassing both explicit and implicit patterns. Then, a knowledge representation model based on hypothetical context (KRHC) with a self-reflection mechanism is proposed to uncover implicit relationships between emotion and behavior through attention mechanisms. By combining rule-based methods for explicit relationships and deep learning for implicit ones, an understanding of emotion-behavior patterns is achieved. In this study, the behaviors are divided into three categories of prosocial, antisocial, and normal behaviors with ten secondary types. Seven categories of emotions are adopted. The proposed deep learning model KRHC is validated on A-E-R datasets and public KINSHIP datasets. The experiment results are concluded; for example, when "fear", "sad", and "surprise" emotions appear, it drives behavior "panic" with most probability. These findings could provide insights for both human-computer interaction and public safety management applications.

Ichino periods for CM forms

In both local and global settings, we establish explicit relations between Ichino triple product period and Waldspurger toric periods for CM forms via the theta lifting and the see-saw principle.

Stability analysis of charged neutron stars and Darmois junction conditions

This research article examines the impact of f(Q,T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f({\\mathcal {Q}},{\\mathcal {T}})$$\\end{document} theory on the geometry of charged neutron stars filled with anisotropic matter configuration. Here, Q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {Q}}$$\\end{document} represents non-metricity and T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {T}}$$\\end{document} denotes the trace of energy-momentum tensor. We use a particular functional form of this modified theory to reduce the system’s complexity and derive explicit relations of the energy density and pressure components. Further, we consider viable non-singular solutions to analyze the internal structure of the charged neutron stars. The unspecified parameters in the metric coefficients are evaluated through Darmois junction conditions, which ensures consistency between interior and exterior solutions of the stellar objects. These parameters are then used to explore different physical characteristics such as the behavior of energy density, pressure components, anisotropy, energy bounds, equation of state parameter, compactness and redshift function in the interior of charged neutron stars. The stability and equilibrium states of the charged stellar objects are discussed using the Tolman–Oppenheimer–Volkoff equation and the speed of sound, respectively. Our results suggest that the charged neutron stars are viable and stable in the presence of dark source terms.

Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation

Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that aggregate explicit social links have shown promising prospects, they are often constrained by the absence of explicit social data and the assumption of homogeneity, thus overlooking variations in social influence and consistency. These limitations hinder semantic expression and recommendation performance. Therefore, we propose a novel framework for social recommendation. First, we design a bipartite network embedding scheme, which learns vertex representations in the embedding space by modeling 1st-order explicit relations and higher-order implicit relations between vertices. Then, the similarity of the embedding vectors is used to extract top-k semantically consistent friends for each user. Next, we design an algorithm to assign a specific influence value to each user. Finally, we combine the top-k friends of the user and their influence values into an ensemble and add it as a regularization term to the rating prediction process of the user to correct the bias. Experiments on three real benchmark datasets show significant improvements in EISF over state-of-the-art methods.

Beyond conventional critique in education: embracing the affirmative

ABSTRACT This article provides new insight into the potentialities of critique. Drawing on posthuman critical theory and the Spinozan concept of power to act, we explore the limits of critique that centrally depends on making explicit power relations that are frequently hidden. Set within the context of inequalities surrounding sexuality, gender and mental health, this exploration involves two Australian government schools and critique as practised. The interdependence of affective, discursive and material relations as they play into these inequalities is shown. Affect and material forces emerge as constitutive aspects of critique with the capacity to address these inequalities and do so in generative ways. Critique presents as more than exposing power structures and power manifests as potentialising, as well as normalising. The argument is made that we might with profit move beyond human-centred and exclusively critical modes of critique. A de-centering of human subjects and re-centering of human and more-than-human relations augments received views of critique and reflects the immanent and productive power to act that underpins it. In bringing an enacted ontology of power to bear, what counts as critique is complex relational positioning with both criticality and affirmation at its core.

Model order reduction in parallel of discrete-time linear systems based on Meixner and Krawtchouk polynomials

Abstract In this paper, based on the partition technique, we use Meixner and Krawtchouk polynomials to present an input-independent model order reduction method. Our main contributions are twofold. First, the explicit difference relations of Meixner polynomials and Krawtchouk polynomials are expressed in an unified form. The parallel computation is carried out on the partitioned subsystems using the Krylov subspaces by which one can generate reduced systems independent of the expansion coefficients of input and can save the computation time. Second, a parallel adaptive enrichment strategy is used to choose the reduced order of reduced systems. Theoretical analysis shows that the proposed method characterizes the property of invariable coefficients. Finally, two numerical examples demonstrate that the proposed method achieves good reduction results in terms of accuracy and reduced CPU time.

Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

Alternating multiple mixed values: Regularization, special values, parity, and dimension conjectures

In this paper, we define and study a variant of multiple zeta values (MZVs) of level four, called alternating multiple mixed values or alternating multiple M-values (AMMVs), forming a Q[i]-subspace of the colored MZVs of level four. This variant includes the alternating version of Hoffman’s multiple t-values, Kaneko–Tsumura’s multiple T-values, and the multiple S-values studied by the authors previously as special cases. We exhibit nice properties similar to the ordinary MZVs such as the generalized duality, integral shuffle and series stuffle relations. After setting up the algebraic framework we derive the regularized double shuffle relations of the AMMVs by adopting the machinery from color MZVs of level four. As an important application, we prove a parity result for AMMVs previously conjectured by two of us. We also investigate several alternating multiple S- and T-values by establishing some explicit relations of integrals involving arctangent function. At the end, we compute the dimensions of a few interesting subspaces of AMMVs for weight less than 9. Supported by theoretical and numerical evidence aided by numerical and symbolic computation, we formulate a few conjectures concerning the dimensions of the above-mentioned subspaces of AMMVs. These conjectures hint at a few very rich but previously overlooked algebraic and geometric structures associated with these vector spaces.

Novel techniques to analyze dynamical properties of quantum chaos with peculiar evidence of hybrid systems confinement

Recent results demonstrate the dynamical peculiarities of the quantum chaos within the hybrid systems by chaotic parameters and probe the pattern formation under the influence of condensation. The complex dynamic behavior of the considered systems was determined with numerical simulation and presented an efficient technique that studied fractional systems comprising chaos-coherence fractions. The findings divulge the peculiar association between the coherence structure and the correlations at finite relative momenta. Thus the present study helps to explore the partially chaos hybrid systems in order to stimulate the experimental applications of nonlinear phenomena. The coherent-chaotic parameters can be measured by examining the chaos peculiarities that possess explicit relations with the condensations to demonstrate the environs of the physical systems. We investigate the influence of the multiplicities, chaos, momentum and temperature of the nonlinear system on the coherent-chaotic normalized correlations. The chaotic parameters are suppressed considerably with the coherence fraction and it appears numerically zero at maximum condensation and one at ideal chaos emissions. We procure that the meaningful parameters decrease significantly with the multiplicity of the nonlinear systems and increase with the momentum in the specified regimes. The identical multiplicity leads to contemplating the coherence and thus the normalized chaotic parameters within its spectacular influences exhibit significance worth contemplating in earnest. The findings underscore the significance of cogitating correlations in deciphering the nonlinear system characteristics and bestowing extraordinary perceptiveness into the convoluted essence of complex systems. The contemplated methodology can be applied to evaluating and analyzing the nonlinear systems and such an innovative approach computes the problems of celestial mechanics, heartbeats and chemical reactions in engineering and medical fields.

Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting

Recognizing the evolution pattern of traffic condition and making accurate prediction play a vital role in intelligent transportation systems (ITS). With the massive increase of available traffic data, deep learning-based models have attracted considerable attention for their impressive performance in traffic forecasting. However, the majority of existing approaches neglect to model of asynchronously dynamic spatio-temporal correlation and fail to consider the impact of historical traffic data on future condition. Additionally, the attribute of deep learning method presents challenges in interpreting the explicit spatiotemporal relationships. In order to enhance the accuracy of traffic prediction as well as extract comprehensive and explainable spatial–temporal relevance in traffic networks, we propose a novel attention-based local spatial and temporal relation discovery (ALSTRD) model. Our model firstly implements feature representation learning to effectively express latent input traffic information. Then, a local attention mechanism structure is established to model asynchronous dependencies of historical input data. Finally, another attention network and the Pearson Correlation Coefficient method are introduced to extract the elaborate influence of the historical traffic condition of neighboring roads on the future condition of the target road. The experiment results on several datasets demonstrate that our model achieves significant improvements in prediction accuracy compared to other baseline methods, which can be attributed to its ability to extract the fine-grained correlation among historical traffic data and capture the dynamic association between past and future data. In addition, the incorporation of attention mechanism and Pearson Correlation Coefficient promotes the model’s ability to elucidate spatiotemporal correlations among traffic data, thereby providing a more robust explanation.

Transverse Spin Hall Effect and Twisted Polarization Ribbons at the Sharp Focus

Transverse Spin Hall Effect and Twisted Polarization Ribbons at the Sharp Focus

Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding

Grounding temporal video segments described in natural language queries effectively and efficiently is a crucial capability needed in vision-and-language fields. In this paper, we deal with the fast video temporal grounding (FVTG) task, aiming at localizing the target segment with high speed and favorable accuracy. Most existing approaches adopt elaborately designed cross-modal interaction modules to improve the grounding performance, which suffer from the test-time bottleneck. Although several common space-based methods enjoy the high-speed merit during inference, they can hardly capture the comprehensive and explicit relations between visual and textual modalities. In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a commonsense-aware cross-modal alignment network (C 2 AN), which incorporates commonsense-guided visual and text representations into a complementary common space for fast video temporal grounding. Specifically, the commonsense concepts are explored and exploited by extracting the structural semantic information from a language corpus. Then, a commonsense-aware interaction module is designed to obtain bridged visual and text features by utilizing the learned commonsense concepts. Finally, to maintain the original semantic information of textual queries, a cross-modal complementary common space is optimized to obtain matching scores for performing FVTG. Extensive results on two challenging benchmarks show that our C 2 AN method performs favorably against state-of-the-arts while running at high speed. Our code is available at https://github.com/ZiyueWu59/CCA.