Dimensional Interaction Research Articles

Dimensional Interaction, Intimacy Building: Roles of Perceived Intimacy and Parasocial Effects on Fans Relations with Virtual Idols

Abstract: The appearance of virtual idols has introduced discussions about the connection with fans, even though their idols are always disembodied and fictitious. To some degree, the theory of parasocial interaction can have academic value in the virtual characters, extending beyond the impact on the real stars and fans. This study emphasizes particularly on exploring the role of intimacy in the parasocial relationship, which contains the perspective of fan psychology and youth subculture. By exerting structured interview, the survey collects records from 7 college students who are fans of popular virtual idols including A-SOUL, Luxiem, LASER, etc. The results provide insights into how fans perceive the presence of intimacy and what factors influence the levels of intimacy based on the behaviors of online media usage. Compared with the real idols and life, there are some findings showcasing characteristics of virtual idol-fan intimacy when fans have developed the parasocial interaction.

Micropolar fluid-thin elastic structure interaction: variational analysis

We consider the non-stationary flow of a micropolar fluid in a thin channel with an impervious wall and an elastic stiff wall, motivated by applications to blood flows through arteries. We assume that the elastic wall is composed of several layers with different elastic characteristics and that the domains occupied by the two media are infinite in one direction and the problem is periodic in the same direction. We provide a complete variational analysis of the two dimensional interaction between the micropolar fluid and the stratified elastic layer. For a suitable data regularity, we prove the existence, the uniqueness and the regularity of the solution to the variational problem associated to the physical system. Increasing the data regularity, we prove that the fluid pressure is unique, we obtain additional regularity for all the unknown functions and we show that the solution to the variational problem is solution for the physical system, as well.

GAPANet: group alternate perceived attention network for optical imaging infrared thermal radiation effect correction

To address the problem of insufficient information interaction between different dimensions of image features, and the inability of real-time simultaneously in the current thermal radiation effect correction methods, in this paper, we construct a group alternate perceived attention network. At coarser scales, the group alternate perceived attention module is constructed, which requires only fewer parameters and computational complexity. The group alternate perceived attention module mainly consists of two alternating parts: a global attention block combined with the local feature of three-branch dilated convolution, and a channel attention block for local information interaction. At the finest scale, a dimensional information interaction attention module is constructed. The correction network fully realizes attention perception at the inter-dimensional level globally and channel level locally. The experimental results of a large number of simulated and actual degraded images show that our model corrects better and realizes real-time.

Fermi Rubik’s Cube in High-pressure Induced Chlorine-riched Compounds

Abstract In the quasi-free electron model, which is widely used in modern solid-state physics, the Fermi surface spreads into a sphere in the Brillouin zone, i.e., the Fermi Sphere. The Fermi Sphere exists widely in metal systems, no matter whether the crystal is in a body-center cubic, face-center cubic, or hexagonal close-packed lattice. Here, we report a class of compounds stabilized at high pressure with a Rubik's cubic Fermi surface, in which the representative example is P m-3n-CaCl3. Our quantum-mechanical variable- composition evolutionary simulations predicted the thermal stabilities of CaCl3, and the tight-binding model revealed its unique Fermi surface originates from the quasi-one- dimensional interaction, structural symmetric protection, and particle-hole symmetry breaking. Furthermore, by its flat and steep band structure, CaCl3 has a huge span of effective mass from 9.08×103 me (super-heavy) to 5.13×10-4 me on the Fermi level, which supplies an interesting platform for quasiparticle research.

Interaction mechanisms of urban ecosystem resilience based on pressure-state-response framework: A case study of the Yangtze River Delta

The unprecedented pace of urbanization has intensified environmental concerns, underscoring the critical importance of bolstering urban ecosystem resilience (UER) in contemporary cities. This study presents a Pressure–State–Response framework for evaluating the UER within the Yangtze River Delta region spanning 2012 to 2021. Utilizing spatial visualization techniques and the panel vector auto–regressive model, spatial differentiation patterns and interaction mechanisms among sub–dimensions are examined. Findings indicate an overall positive trajectory of UER, characterized by decreasing Pressure, noteworthy steady growth in State, and rising Response. Spatially, Pressure is higher in the north and lower in the south, with large cities becoming new centers of high pressure; State is higher in the southwest mountains and lower in the northeast plains; Responses center around Shanghai, expanding toward the southwest and northwest. The complex interactions between Pressure, State, and Response form a nested cycle of UER, indicating that the UER in the Yangtze River Delta is evolving towards a more rational development trend, where the tight mechanism of societal Response plays a key role. The implications underscore the urgent need for tailored regional governance strategies that leverage spatial variations, balance human impact on ecosystems, and optimize dimensional interactions for resilient city development. This research contributes a comprehensive framework for understanding and enhancing ecological resilience in the Yangtze River Delta region, offering valuable insights into regional governance and long–term urban ecological sustainability amidst global environmental challenges.

Remaining useful life prediction of machinery based on improved Sample Convolution and Interaction Network

Remaining useful life (RUL) prediction is significant in ensuring the safe and reliable operation of machinery and reducing maintenance costs. There are currently numerous deep learning-based methods for machinery RUL prediction. However, some studies overlook the differences in the contributions of data from different sensors or different time points of the same sensor, and most research only extracts information from feature or sequence dimensions, which inevitably affects the efficiency and accuracy of RUL prediction. Therefore, we proposed a method based on a multi-dimensional attention mechanism and feature-sequence dimensional sample convolution and interaction network (MFSSCINet) to predict the machinery RUL effectively, which includes a Feature-Sequence Dimension Attention Module to capture information interactions in feature dimension and learn the impact weights of various time steps in sequence dimension. Then, a Multi-Source Information Fusion Module was constructed to extract helpful information from features of different dimensions and time resolutions and fuse them. Finally, a RUL Prediction Module was built to estimate the machine RUL effectively. The method’s effectiveness is validated in C-MAPSS and XJTU-SY datasets. Experimental results show that the MFSSCINet model has higher accuracy in machine RUL prediction tasks than other advanced computational methods.

EMA-YOLO: A Novel Target-Detection Algorithm for Immature Yellow Peach Based on YOLOv8.

Accurate determination of the number and location of immature small yellow peaches is crucial for bagging, thinning, and estimating yield in modern orchards. However, traditional methods have faced challenges in accurately distinguishing immature yellow peaches due to their resemblance to leaves and susceptibility to variations in shooting angles and distance. To address these issues, we proposed an improved target-detection model (EMA-YOLO) based on YOLOv8. Firstly, the sample space was enhanced algorithmically to improve the diversity of samples. Secondly, an EMA attention-mechanism module was introduced to encode global information; this module could further aggregate pixel-level features through dimensional interaction and strengthen small-target-detection capability by incorporating a 160 × 160 detection head. Finally, EIoU was utilized as a loss function to reduce the incidence of missed detections and false detections of the target small yellow peaches under the condition of high density of yellow peaches. Experimental results show that compared with the original YOLOv8n model, the EMA-YOLO model improves mAP by 4.2%, Furthermore, compared with SDD, Objectbox, YOLOv5n, and YOLOv7n, this model's mAP was improved by 30.1%, 14.2%,15.6%, and 7.2%, respectively. In addition, the EMA-YOLO model achieved good results under different conditions of illumination and shooting distance and significantly reduced the number of missed detections. Therefore, this method can provide technical support for smart management of yellow-peach orchards.

Complements or substitutes? A Meta-analysis of the role of social capital for individual knowledge transfer

PurposeThis study aims to investigate the effects of different dimensions of social capital on individual knowledge transfer to nurture the organization’s intellectual capital, as well as the interactions among these dimensions and explore the potential moderators.Design/methodology/approachThis study conducted a meta-analysis with 108 independent empirical studies to examine the different dimensions of social capital–knowledge transfer relationships and the effects of moderators and used meta-analytic structural equation modeling (MASEM) to test the internal relationships among social capital dimensions.FindingsThe results show that structural, relational and cognitive social capitals are all positively related to knowledge transfer. In addition, different dimensions of social capital act as complements to one another. Further examinations reveal that the level of economic development has no significant moderating effect on the relationship between social capital and knowledge transfer. Then, the cultural context and profit climate characteristics moderate the relationship between social capital and knowledge transfer.Originality/valueLeveraging the trilogy of signaling, learning and spillover effects, this meta-analytic study quantitatively integrates the relationships between different dimensions of social capital and knowledge transfer. It reconciles the present disparate findings, demonstrates the validity of different dimensional social capital interactions and obtains highly generalized conclusions. This study also introduces a dichotomy, saturation versus reinforcement, to explain the mixed results, which enriches social capital theory.

A decomposition method for two and three dimensional fluid-solid interaction scattering problem

In this paper, we propose a decomposition formula for the fluid-solid interaction scattering problem. By the decomposition of the displacement, the corresponding problem is transformed into a coupled boundary value problem. For the two dimensional case, the coupled boundary value problem is about two scalar Helmholtz equations, and the other one is a scalar Helmholtz equation and a vector Helmholtz equation for the three dimensional. Then the coupled boundary value problem is solved by the method of fundamental solution. At last we give some examples to show the effectiveness of the present method in two and three dimensional case. A benchmark example is available for checking the feasibility and accuracy of the presented method. In the numerical examples, a part of exact solution for the boundary value problem is including for checking the accuracy.

Improving 3D Object Detection with Context-Aware and Dimensional Interaction Attention

Recently, 3D object detection technology based on point clouds has developed rapidly. However, too few points of distant and occluded objects are scanned by the sensor, and thus these objects suffer from too insufficient features to be detected. This case damages the detection accuracy. Therefore, we constitute a novel 3D object detection with Context-aware and dimensional Interaction Attention Network (CIANet) to explore vital geometric cues for enriching the feature representation of the object, thus boosting the overall detection performance. Specifically, in the first stage, we employ the 3D sparse convolution to extract voxel features, and then construct a Channel-Spatial Hybrid Attention (CSHA) module and a Contextual Self-Attention (CSA) module to enhance voxel features for generating proposals. The CSHA module aims to enhance the key information of the channel and spatial domains of 2D Bird’s Eye View (BEV) features, and the CSA module is applied to supplement contextual information to the enhanced BEV features, thus generating accurate proposals. In the second stage, we construct a Dimensional Interaction Attention (DIA) module to refine Region of Interest (RoI) features within the proposals. It enhances the interactions among the channel and spatial dimensions of RoI features to learn accurate boundaries of objects for proposal refinement. Extensive experiments on the KITTI and Waymo benchmarks show the superior detection performance of CIANet compared to recent methods, especially for objects such as pedestrians and cyclists.

M2-YOLOX: A Novel Method for Object Detection Based on an Improved YOLOX Algorithm Introducing a Global Attention Mechanism and a Feature Enhancement Module

Deep learning-based algorithms for detecting objects in remote sensing images have produced excellent results recently. However, the target recognition and classification process of remote sensing images has problems such as dense targets, uneven distribution, large-scale changes and complex backgrounds. In order to improve the effectiveness of existing detection methods, based on the YOLOX algorithm, a remote sensing image object detection algorithm introducing Global Attention Mechanism (GAM) and Feature Enhancement Module (FEM) proposed, named the M2-YOLOX(GAM+FEM+YOLOX) algorithm. First, a novel GAM module is developed that employs a sequential channel-space attention mechanism and redesigns the Convolutional Block Attention Module (CBAM), to address the issues of low effective information extraction and weak information representation of the feature map in the backbone network. CBAM is capable of amplifying global dimensional interaction features while reducing information dispersion as well. Second, the goal of the FEM is to improve the target feature extraction capabilities of the backbone feature extraction network by fusing numerous perceptual field features in lower-level feature maps. Then, the Flexible Rectified Linear Unit (FReLU) activation function is introduced under the action of feature fusion and global attention mechanism. Four-way feature map output in Feature Pyramid Networks (FPN) with Non-Maximum Suppression (NMS) and score filtering for object detection and output results. In comparison to the YOLOX algorithm, the experimental results show that the mAP value of the M2-YOLOX algorithm is improved by 0.0123, the LAMR value is decreased by 0.0150, the Precision rate value is increased by 0.105450, the Recall value is increased by 0.053250, and the mF1 value is increased by 0.0425.

Dimensional interaction is driven by feature integration: A binding theory of garner interference.

For nearly half a century now, Garner interference has been serving as the gold standard measure of dimensional interaction and selective attention. But the mechanisms that generate Garner interference are still not well understood. The current study proposes a novel theory that ascribes the interference (and dimensional interaction in general) to episodic feature integration processes at the micro (trial-to-trial) level. The novel account builds on earlier well-established notions of "feature integration" and "object files," and is augmented by formal derivations. The sequential binding account predicts that the magnitude of Garner interference is related to the strength of feature integration along consecutive trials. Three experiments were set to test this novel binding theory. Experiment 1 and Experiment 2 tested performance with integral dimensions (chroma and value, and width and height of rectangles); whereas Experiment 3 examined performance with a pair of separable dimensions (circle's size and angle of a diameter). In addition, the time lag ensuing between consecutive trials was manipulated. The results strongly supported the predictions of the sequential binding account: (a) with integral dimensions, substantial amounts of Garner interference were correlated with large partial repetition costs (e.g., consensual markers of feature integration), but this pattern was not observed with separable dimensions, and (b) the magnitude of both Garner interference and partial repetition costs diminished as a function of the ensuing time lag between consecutive trials, pointing to a common time-dependent memory mechanism. These results adduce strong support in the predictions of the feature binding theory of Garner interference, giving currency to the idea that dimensional interaction is driven by feature integration. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Structure elucidation, DFT and IRI computational investigations of a novel 4-(3-(4-chlorophenyl)-1-phenyl-1H-pyrazol-4-yl)2,6-dimethyl-1,4-dihydropyridine-3,5-dicarbonitrile

A novel symmetric dihydropyridine possessing cyano group at C11 and C14 has been synthesized using a modified Hantzsch method. The compound (▪) was characterized spectroscopically by Mass, IR and 1H NMR methods and finally confirmed by X-ray diffraction study. The compound crystallizes in a monoclinic crystal class with P21/c space group. In the title molecule, the dihydropyridine ring is puckered and displayed an envelope conformation of the type E1. The solid state of the title compound is governed by an intermolecular C–H…N, C–H…Cl and C–H…π interactions in the building of a crystal. It is investigated by three dimensional Hirshfeld surfaces mapped on different properties, also the relevant fingerprint graphs are obtained to recognize the percentage contribution from various inter atomic contacts. The topology of three dimensional interaction energy frameworks were constructed using the energy density wave function of DFT/B3LYP/6-311++G(d,p) and the energy corresponding to intermolecular interactions were assessed. The density functional theory analysis has been carried out and hence an energy gap and global index parameters are evaluated, also the geometric parameters are computed by optimizing the molecular structure of the title compound. The natural bonding orbitals (NBO) analysis is carried out to know the intermolecular charge transfer in the molecule. Reduced density gradients (RDG) and interaction region indicators (IRI) analysis reveals the chemical bonding and weak interactions regions. The molecular docking has been performed on 1B38, 2OHM, 3VNT and 4URO receptors shows good binding affinities and their results reveal that it serves as potential anti-cancer agent.

Soliton solutions of a (2+1)-dimensional nonlinear time-fractional Bogoyavlenskii equation model

In this analysis, we propose a mathematical approach named the extended (ℵ, ℜ) expansion scheme to integrate nonlinear fractional and classical evolution models. We utilize the technique to the time fractional Bogoyavlenskii equation, which signifies the (2 + 1)-dimensional interaction of propagating Riemann waves along a definite axis and a wave normal to it. Additionally, we integrate the model using the new extended (G′/G) expansion scheme. Consequently, we derive exact wave solutions, including singular and multiple periodic soliton solutions, kink waves, anti-kink waves, bell-shaped waves, lump waves, rogue waves, periodic lump waves, and interactions between lump and kink wave profiles. The properties of the fractional parameter on the achieved outcomes are also analyzed. We have created 3D plots, 3D plots with contour lines, and 2D plots of our attained solutions using the computational software, Maple. These systems can also represent various solutions for other fractional models in the domains of nonlinear science and engineering.

The method of fundamental solutions for the high frequency acoustic-elastic problem and its relationship to a pure acoustic problem

This paper firstly documents the method of fundamental solutions for the two dimensional high frequency acoustic-elastic interaction problem, and investigates its relationship to a pure acoustic problem with a sound-hard boundary. We approximate the displacements by the linear combination of the fundamental matrix tensors of the Navier equation. The acoustic scattered field outside the elastic solid is approximated by the linear combination of the fundamental solutions of the Helmholtz equation. At last, we give some examples using the real data in four cases, including the copper alloy in the water, in the oil, in the cork wood and in the air. Especially in the first example, an exact solution is available for checking the accuracy of the presented method. We also find that the acoustic-elastic interaction problem is close to a pure acoustic problem with a sound-hard boundary as the ratio of two medium density increasing.

SocialInteractionGAN: Multi-Person Interaction Sequence Generation

Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we focus on a unimodal representation of interactions and propose to tackle interaction generation in a data-driven fashion. In particular, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction generation. Our model builds on a recurrent encoder-decoder generator network and a dual-stream discriminator, that jointly evaluates the realism of interactions and individual action sequences and operates at different time scales. Crucially, contextual information on interacting participants is shared among agents and reinjected in both the generation and the discriminator evaluation processes. Experiments show that albeit dealing with low dimensional data, SocialInteractionGAN succeeds in producing high realism action sequences of interacting people, comparing favorably to a diversity of recurrent and convolutional discriminator baselines, and we argue that this work will constitute a first stone towards higher dimensional and multimodal interaction generation. Evaluations are conducted using classical GAN metrics, that we specifically adapt for discrete sequential data. Our model is shown to properly learn the dynamics of interaction sequences, while exploiting the full range of available actions.

Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution

In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs.

Fast hyperbolic wavelet regression meets ANOVA

We use hyperbolic wavelet regression for the fast reconstruction of high-dimensional functions having only low dimensional variable interactions. Compactly supported periodic Chui–Wang wavelets are used for the tensorized hyperbolic wavelet basis. In a first step we give a self-contained characterization of tensor product Sobolev–Besov spaces on the d-torus with arbitrary smoothness in terms of the decay of such wavelet coefficients. In the second part we perform and analyze scattered-data approximation using a hyperbolic cross type truncation of the basis expansion for the associated least squares method. The corresponding system matrix is sparse due to the compact support of the wavelets, which leads to a significant acceleration of the matrix vector multiplication. In case of i.i.d. samples we can even bound the approximation error with high probability by loosing only log -terms that do not depend on d compared to the best approximation. In addition, if the function has low effective dimension (i.e. only interactions of few variables), we qualitatively determine the variable interactions and omit ANOVA terms with low variance in a second step in order to increase the accuracy. This allows us to suggest an adapted model for the approximation. Numerical results show the efficiency of the proposed method.

COMET: Convolutional Dimension Interaction for Collaborative Filtering

Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction information among historical interactions and embedding dimensions. In this article, we propose a novel representation learning-based model called COMET ( CO nvolutional di M E nsion in T eraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions. To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two “embedding maps”. In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks (CNN) with kernels of different sizes simultaneously. A fully connected multi-layer perceptron (MLP) is then applied to obtain two interaction vectors. Lastly, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and rationality of our proposed method.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk.

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.