Interlayer Correlations Research Articles

Coupled awareness-epidemic spreading with the consideration of self-isolation behavior

Epidemic transmission and the associated awareness diffusion are fundamentally interactive. There has been a burgeoning interest in exploring the coupled epidemic-awareness dynamic. However, current research predominantly focuses on self-protection behavior stimulated by awareness, paying less attention to self-isolation behavior. Given the constraints of government-mandated quarantine measures, spontaneous self-isolation actions assume greater significance in the long-term response to epidemics. In response, we propose a coupled awareness-epidemic spreading model with the consideration of self-isolation behavior and subsequently employ a Micro Markov Chain Approach to analyze the model. Extensive experiments show that self-isolation behavior can effectively raise the epidemic threshold and reduce the final outbreak scale. Notably, in multiplex networks with positive inter-layer correlation, the inhibitory effect is the greatest. Moreover, there exists a metacritical point, only when the awareness diffusion probability exceeds the critical value of this point, the epidemic threshold will increase with the increase of awareness diffusion probability. In addition, the growth of the average degree of the virtual-contact layer can reduce the value of this metacritical point. This research emphasizes the significant role of self-isolation behavior in curbing epidemic transmission, providing valuable perspectives for epidemic prevention through the interplay of awareness and epidemic spreading.

Quenched Pair Breaking by Interlayer Correlations as a Key to Superconductivity in La_{3}Ni_{2}O_{7}.

The recent discovery of superconductivity in La_{3}Ni_{2}O_{7} with T_{c}≃80 K under high pressure opens up a new route to high-T_{c} superconductivity. This material realizes a bilayer square lattice model featuring a strong interlayer hybridization unlike many unconventional superconductors. A key question in this regard concerns how electronic correlations driven by the interlayer hybridization affect the low-energy electronic structure and the concomitant superconductivity. Here, we demonstrate using a cluster dynamical mean-field theory that the interlayer electronic correlations (IECs) induce a Lifshitz transition resulting in a change of Fermi surface topology. By solving an appropriate gap equation, we further show that the leading pairing instability, s± wave, is enhanced by the IECs. The underlying mechanism is the quenching of a strong ferromagnetic channel, resulting from the Lifshitz transition driven by the IECs. Based on this picture, we provide a possible reason of why superconductivity emerges only under high pressure.

Sparse-angle optical projection tomography based on multi-layer sparsity and deep image priors

Optical projection tomography (OPT) is a computational imaging technique to acquire the volumetric images of biological samples ranging from millimeters to centimeters. For in-vivo OPT, it is essential to minimize the inspection time to reduce the adverse impacts on organisms, including the anesthetic side effect and phototoxicity. It can be achieved by projecting the samples from equally spaced sparse angles, but this method will induce radial artifacts in the reconstructed tomographic images. This paper develops a high-quality reconstruction method for sparse-angle OPT by jointly exploiting the multi-layer sparsity prior and deep image prior (DIP) on the volumetric images. The DIP module works in an unsupervised manner without requirement on a training dataset. This method can also address the inter-layer correlation within the samples, and process multi-layer images in parallel to improve the reconstruction accuracy and efficiency. Simulations and experiments demonstrate the superiority of the proposed method over some widely used reconstruction algorithms for sparse-angle OPT.

Enhancing the robustness of interdependent networks by positively correlating a portion of nodes

Cascading failures caused by interdependencies make modern coupled systems extremely fragile to failures. In existing network robustness enhancing methods, maximizing interlayer degree-degree correlations has been proven to be an effective way to improve the robustness of interdependent networks under random failures. Here, we propose a portion of nodes positively correlated strategy (PNC) to improve network robustness by positively correlating a portion of nodes, in which the nodes that are positively correlated are selected in descending order of degree, starting at a cutoff value. Based on percolation theory, we verify the effectiveness of PNC on different networks. And find that, when the nodes with the highest degree are preferentially correlated, i.e. the cutoff value takes the maximum degree, this strategy achieves a state-of-the-art optimization effect. In particular, for interdependent scale-free networks with power-law exponent γ that satisfies 2<γ<3 , we theoretically demonstrate that the highest degree preferentially correlated mode can maximize network robustness by changing the coupling state of the near 0 proportion of nodes. For γ = 3, such a mode can make the network turn into a second-order phase transition at the collapse point. Finally, we discuss the relationship between the robustness of optimized networks and common links in real-world networks.

Influence of spatial variability of soil shear-wave velocity considering intralayer correlation on seismic response of engineering site

Shear-wave velocity uncertainty significantly impacts seismic response analysis at engineering sites. Previous studies focused on the correlations of shear-wave velocities within soil layers. However, the influence of the vertical spatial variability of shear-wave velocity within individual soil layers was not considered. This study selected a typical Site Class II engineering site to investigate this influence based on the equivalent linear seismic response analysis method of layered soil deposits. Existing field test data on shear-wave velocity were used to discretize the shear-wave velocity within soil layers into a one-dimensional (1D) random field through covariance matrix decomposition and local average process theory. Monte Carlo simulations generated random shear-wave velocity profiles with different vertical correlation distances. Three artificial records with different earthquake return periods were used as input motions at the engineering bedrock for the 1D free-field model. Considering soil shear-wave velocity uncertainty, the peak shear strain increased by 19–27% with the input ground motion amplitude. Moreover, the site peak shear strain variability increased by 25–34% with the vertical correlation distance. The proposed 1D random field model effectively simulated the interlayer correlation and spatial variability of shear-wave velocity within soil layers, demonstrating its significance in seismic response analysis.

Critical properties of Heider balance on multiplex networks.

Heider's structural balance theory has proven invaluable in comprehending the dynamics of social groups characterized by both friendly and hostile relationships. Since people's relations are rarely single faceted, we investigate Heider balance dynamics on a multiplex network, consisting of several copies of the same agent displaying correlated relations at different layers building the multiplex. Intralayer interactions in our model adhere to Heider dynamics, while interlayer correlations stem from Ising interactions, with the heat-bath dynamics of link signs. Our investigation reveals a multifaceted system with a diverse equilibrium landscape contingent on the coexistence of distinct phases across layers. We observe that, starting from a paradise state with positive links in all layers, an increase in temperature triggers a discontinuous transition to a disordered state akin to single-layer scenarios. The critical temperature surpasses that of the single-layer case, a fact verified through extended mean-field analysis and agent-based simulations. Furthermore, the scenario shifts when one layer exhibits a two-clique configuration instead of a paradise state. This change introduces additional transitions: synchronization of interlayer relations and a transition to the disorder, appearing at a different, lower temperature compared to matching paradise states. This exploration shows the intricate interplay of Heider balance and multiplex interactions.

A New Automated Prognostic Prediction Method Based on Multi-Sequence Magnetic Resonance Imaging for Hepatic Resection of Colorectal Cancer Liver Metastases.

Colorectal cancer is a prevalent and life-threatening disease, where colorectal cancer liver metastasis (CRLM) exhibits the highest mortality rate. Currently, surgery stands as the most effective curative option for eligible patients. However, due to the insufficient performance of traditional methods and the lack of multi-modality MRI feature complementarity in existing deep learning methods, the prognosis of CRLM surgical resection has not been fully explored. This paper proposes a new method, multi-modal guided complementary network (MGCNet), which employs multi-sequence MRI to predict 1-year recurrence and recurrence-free survival in patients after CRLM resection. In light of the complexity and redundancy of features in the liver region, we designed the multi-modal guided local feature fusion module to utilize the tumor features to guide the dynamic fusion of prognostically relevant local features within the liver. On the other hand, to solve the loss of spatial information during multi-sequence MRI fusion, the cross-modal complementary external attention module designed an external mask branch to establish inter-layer correlation. The results show that the model has accuracy (ACC) of 0.79, the area under the curve (AUC) of 0.84, C-Index of 0.73, and hazard ratio (HR) of 4.0, which is a significant improvement over state-of-the-art methods. Additionally, MGCNet exhibits good interpretability.

Impact of Divalent Cations on In-Layer Positional Order of DNA-Based Liquid Crystals: Implications for DNA Condensation.

The layered liquid crystalline phases formed by DNA molecules, which include rigid and flexible segments ("gapped DNA"), enable the study of both end-to-end stacking and side-to-side (helix-to-helix) lateral interactions, forming a model system to study such interactions at physiologically relevant DNA and ion concentrations. The observed layer structure exhibits long-range interlayer and in-layer positional correlations. In particular, the in-layer order has implications for DNA condensation, as it reflects whether these normally repulsive interactions become attractive under certain ionic conditions. Using synchrotron small-angle X-ray scattering measurements, we investigate the impact of divalent Mg2+ cations (in addition to a constant 150 mM Na+) on the stability of the inter- and in-layer DNA ordering as a function of temperature between 5 and 65 °C. DNA constructs with different terminal base pairings were created to mediate the strength of the attractive end-to-end stacking interactions between the blunt ends of the gapped DNA constructs. We demonstrate that the stabilities at a fixed DNA concentration of both interlayer and in-layer order are significantly enhanced even at a few mM Mg2+ concentration. The stabilities are even higher at 30 mM Mg2+; however, a marked decrease is observed at 100 mM Mg2+, suggesting a change in the nature of side-by-side interactions within this Mg2+ concentration range. We discuss the implications of these results in terms of counterion-mediated DNA-DNA attraction and DNA condensation.

Convolutional neural network-based seismic fragility analysis of subway station structure considering spatial variation of site shear-wave velocity

Prior research demonstrated that the uncertainty of soil parameter can significantly impact the seismic response analysis and seismic performance evaluation of underground structures. To this end, the seismic response of a three-story, three-span subway station embedded in a typical layered engineering site was investigated in this study and the uncertainty of soil parameters was explicitly considered using the shear-wave velocity interlayer correlation model. In addition, an incremental dynamic analysis considering the uncertainty of the shear-wave velocity was performed using the one-dimensional convolutional neural network (1D-CNN) model as a surrogate model for the traditional finite element method. The results indicate that the uncertainty of soil shear-wave velocity increases the average PGA values for minor, moderate, and severe failures with a probability of exceeding 50% by about 10%, and increases the logarithmic standard deviation by about 40%. When considering the uncertainty of shear-wave velocity, the discreteness of the fragility curve will increase. Moreover, the rightward shift of the fragility curve midpoint leads to a comparatively more safety seismic performance evaluation of the structure under significant seismic input.

Recognition of multivariate geochemical anomalies using a geologically-constrained variational autoencoder network with spectrum separable module – A case study in Shangluo District, China

This study has developed a novel variational autoencoder architecture by incorporating the spectrum separable module, termed SSM-VAE, so as to recognize the multi-mineral-species geochemical patterns over the Shangluo district, central China, and then facilitate a better understanding of the regional metallogenesis. The primary advantage of SSM-VAE is its ability to explore the interlayer correlations and then integrate them into the reconstructed features, which greatly improved the geological interpretability of the model products. In the training process, a hybrid loss function with a geologically-constrained term derived from the fractal analysis was designed to guide the model to focus on anomalies related to mineralization. Afterward, the factor analysis together with the EM-MML thresholding algorithm were integrated as a post-processing tool. Finally, the mineral-spot identification rate (δ) versus the size of the anomalous area (S) are used to validate the ore-bearing potential of different anomaly divisions. Comparing to other state-of-the-art models without regard to the interlayer correlations, SSM-VAE can produce well-zoned, mineral species-specific, and more metalliferous anomalous patches. To be specific, we separated out 10 zoned anomaly divisions, and on average, when S = 1% there are 8.5013 mineral spots falling within.

Light-induced hexatic state in a layered quantum material

The tunability of materials properties by light promises a wealth of future applications in energy conversion and information technology. Strongly correlated materials such as transition metal dichalcogenides offer optical control of electronic phases, charge ordering and interlayer correlations by photodoping. Here, we find the emergence of a transient hexatic state during the laser-induced transformation between two charge-density wave phases in a thin-film transition metal dichalcogenide, 1T-type tantalum disulfide (1T-TaS2). Introducing tilt-series ultrafast nanobeam electron diffraction, we reconstruct charge-density wave rocking curves at high momentum resolution. An intermittent suppression of three-dimensional structural correlations promotes a loss of in-plane translational order caused by a high density of unbound topological defects, characteristic of a hexatic intermediate. Our results demonstrate the merit of tomographic ultrafast structural probing in tracing coupled order parameters, heralding universal nanoscale access to laser-induced dimensionality control in functional heterostructures and devices.

Berezinskii—Kosterlitz—Thouless correlations in copper-based quasi-2D spin systems (Review Article)

We present an overview of selected copper-based quasi-2D square-lattice spin-1/2 materials with an easy-plane anisotropy, providing the possibility to study emergent Berezinskii-Kosterlitz-Thouless (BKT) correlations. In particular, in those materials with a comparatively small exchange coupling, the effective XY anisotropy of the low-temperature spin correlations can be controlled by an applied magnetic field, yielding a systematic evolution of the BKT correlations. In cases where the residual interlayer correlations are small enough, dynamical BKT correlations in the critical regime may be observed experimentally, whereas the completion of the genuine BKT transition is preempted by the onset of long-range order.

A Topological Mechanism of Superdiffusion on Duplex Networks

Superdiffusion is an interesting phenomenon from both theoretical and practical perspectives. We propose a sufficient condition for the occurrence of superdiffusion on duplex networks by applying the generating functionology from combinatorial mathematics. Our result indicates that, with fixed values of intra-layer diffusion constants, superdiffusion emerges from a certain combination of different network topologies, which provides a topological mechanism to regulate the whole diffusion process. On a more general ground, we provide two inter-layer linking strategies to expand the applicability of the above topological mechanism on real-world networks, and construct a class of specific duplex networks on which superdiffusion occurs. We further verify that diffusion with negative inter-layer correlation is faster than those with positive correlation from a typical duplex structure. Given the ubiquity of diffusion in the real world, our results constitute a significant step toward a better understanding of superdiffusion processes on multiplex networks.

A compressive sensing encryption scheme for dual color images based on discrete memristor map and Rubik’s cube scramble

The most common in practical image encryption are multiple color images, however, less attention is paid to inter-layer correlation broken in existing color image encryption algorithms. In this paper, a 2-D compressive sensing dual color images encryption algorithm based on a discrete memristor hyper-chaotic map and Rubik's cube scramble is proposed. First, we propose a new discrete memristor-Ikeda map (DM-Ikeda), the analysis result shows the DM-Ikeda map has extremely high complexity and hyper-chaotic dynamical behavior. Second, to facilitate ciphertext transmission, the images are compressed and encrypted simultaneously by DM-Ikeda map and 2D compressive sensing. Then, we design a new rubik's cube scramble algorithm, which combines the layers of two images into a cube that can be expanded in different orders, and swaps the pixels of rows, columns or faces of each image by rotating the cube to achieve image scramble. This algorithm can break the correlation not only of pixels within layers of two images but also of pixels between different layers of images. Finally, two images are further diffused with the use of hyper-chaotic sequences. The algorithm can achieve both compression and encryption of dual color images. Experimental results indicate that, our scheme has excellent compression, and can break the correlation between different images and layers, which is excellent resistance to statistical attacks and has high security.

Efficient Tensor Network Algorithm for Layered Systems.

Strongly correlated layered 2D systems are of central importance in condensed matter physics, but their numerical study is very challenging. Motivated by the enormous successes of tensor networks for 1D and 2D systems, we develop an efficient tensor network approach based on infinite projected entangled-pair states for layered 2D systems. Starting from an anisotropic 3D infinite projected entangled-pair state ansatz, we propose a contraction scheme in which the weakly interacting layers are effectively decoupled away from the center of the layers, such that they can be efficiently contracted using 2D contraction methods while keeping the center of the layers connected in order to capture the most relevant interlayer correlations. We present benchmark data for the anisotropic 3D Heisenberg model on a cubic lattice, which shows close agreement with quantum MonteCarlo and full 3D contraction results. Finally, we study the dimer to Néel phase transition in the Shastry-Sutherland model with interlayer coupling, a frustrated spin model that is out of reach of quantum MonteCarlo due to the negative sign problem.

Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference

This article presents a novel and flexible multitask multilayer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting intralayer and interlayer correlations. It removes the need for a robot to access and process information from many separate maps when performing a complex task, advancing the way robots interact with their environments. To this end, we design a multitask deep neural network with attention mechanisms as our front-end to provide heterogeneous observations for multiple map layers simultaneously. Our back-end runs a scalable closed-form Bayesian inference with only logarithmic time complexity. We apply the framework to build a dense robotic map including metric-semantic occupancy and traversability layers. Traversability ground truth labels are automatically generated from exteroceptive sensory data in a self-supervised manner. We present extensive experimental results on publicly available datasets and data collected by a 3D bipedal robot platform and show reliable mapping performance in different environments. Finally, we also discuss how the current framework can be extended to incorporate more information such as friction, signal strength, temperature, and physical quantity concentration using Gaussian map layers. The software for reproducing the presented results or running on customized data is made publicly available.

Correlated disorder and crystal structure of β-VOSO4

Pauflerite β-VOSO4 has recently been identified as a one-dimensional S = ½ Heisenberg system, of interest both from a fundamental point of view and a potential material for future spintronics applications. The observation of diffuse scattering in a synthetic β-VOSO4 provides a microscopic interpretation of the underlying correlated disorder, which is linked to the inversion of the short–long V—O distance pairs along VO6 chains, forming a local defect state. Direct Monte Carlo modeling indicates that such defects form thin layers with a positive inter-layer correlation, forming small domains with inverted vanadyl bonding patterns. Two-dimensional defects in anisotropic magnetic systems may perturb, or even destroy, long-range magnetic ordering leading to unusual interactions. In particular, the lack of inversion symmetry in the defect layers opens up the possibility for the Dzyaloshinskii–Moriya interaction (DMI) and, consequently, chiral magnetism localized in the defect planes. The defect β-VOSO4 structure, therefore, opens up new possibilities for the study of low-dimensional magnetic systems.

Investigation of physical properties of Si crystallites in W/Si multilayers

The structural inhomogeneities of silicon films embedded within W/Si multilayer mirrors were studied by X-ray reflection, grazing-incidence small-angle X-ray scattering (GISAXS) and X-ray photoelectron spectroscopy (XPS). In the diffuse scattering spectra, evidence of laterally and vertically ordered in-layer inhomogeneities was consistently observed. In particular, specific substructures resonant in nature (named here `ridges') were detected. The properties of the ridges were similar to the roughness determined by quasi-Bragg peaks of scattering, which required a high interlayer correlation of particles. The XPS showed the nanocrystalline nature of the Si particles in the amorphous matrix. The geometric characteristics and in-layer and inter-layer correlations of the nanoparticles were determined. In GISAXS imaging, the unusual splitting of the waists between the Bragg sheets into filament structures was observed, whose physical nature cannot yet be explained.

Analytical renormalization group approach to competing orders at charge neutrality in twisted bilayer graphene

We investigate twisted bilayer graphene near charge neutrality using a generalized Bistritzer-MacDonald continuum model accounting for corrugation effects. The Fermi velocity vanishes for particular twist angles properly reproducing the physics of the celebrated magic angles. Using group representation theory, we identify all contact interaction potentials compatible with the symmetries of the model. This enables us to identify two classes of quartic interactions leading to either the opening of a gap or to nematic ordering. We then implement a renormalization group analysis to study the effect of quantum fluctuations on the appearance of these states for twist angles approaching the first magic value. This combined group theory-renormalization study reveals that the proximity to the first magic angle favors the occurrence of a layer-polarized, gapped state with a spatial modulation of interlayer correlations, which we call a nematic insulator.

Sol-gel processing of a covalent organic framework for the generation of hierarchically porous monolithic adsorbents

Sol-gel processing of a covalent organic framework for the generation of hierarchically porous monolithic adsorbents