Propagation Network Research Articles

Optimization of Ultrafast Singlet Fission in One-Dimensional Rings Towards Unit Efficiency

Singlet fission (SF) is an electronic transition that in the last decade has been under the spotlight for its applications in optoelectronics, from photovoltaics to spintronics. Despite considerable experimental and theoretical advancements, optimizing SF in materials like multichromophoric systems and molecular crystals remains a challenge, due to the complexity of its analysis beyond perturbative methods. Here, we tackle the case of one-dimensional rings, aiming to promote singlet fission and prevent its backreaction. We study ultrafast SF nonperturbatively, by numerically solving a spin-boson model, via exact propagation and tensor network methods. By optimizing over a parameter space relevant to organic molecular materials, we identify two classes of solutions that can take SF efficiency beyond 85% in the nondissipative (coherent) regime, and to 99% when exciton-phonon interactions can be tuned. After discussing the experimental feasibility of the optimized solutions, we conclude by proposing that this approach can be extended to a wider class of optoelectronic optimization problems. Published by the American Physical Society 2024

Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Drug-resistant epilepsy with frequent seizures are considered to undergo surgery to become seizure-free, but seizure-free rates have not dramatically improved, partly due to imprecise intervention locations. To address this clinical need, we construct effective connectivity to reveal epilepsy brain dynamics. Based on the propagation path captured by the high order effective connectivity, calculate the control centrality evaluation scheme of the excised area. We used three datasets: simulation dataset, clinical dataset, and public dataset. The epileptogenic propagation network was quantified by calculating high-order effective connection to obtain accurate propagation path, based on this, combined with the outdegree index for virtual resection. By removing electrodes and recalculating control centrality, we quantify each electrode or region’s control centrality to evaluate the virtual resection scheme. Three datasets obtained consistent results. We track the accurate propagation path and find the obvious inflection points occurring during the excision process. The minimum intervention targets were obtained by comparing different schemes without recurrence. The clinical data with multiple seizures found that after resection, the brain reaches a stable state and is less likely to continue spreading. By quantitative analysis of control centrality to evaluate the possible excision scheme, finally we obtain the best intervention area for epilepsy, which assist in developing surgical plans.

DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction.

Predicting propagation cascades is crucial for understanding information propagation in social networks. Existing methods always focus on structure or order of infected users in a single cascade sequence, ignoring the global dependencies of cascades and users, which is insufficient to characterize their dynamic interaction preferences. Moreover, existing methods are poor at addressing the problem of model robustness. To address these issues, we propose a predication model named DropMessage Hypergraph Attention Networks, which constructs a hypergraph based on the cascade sequence. Specifically, to dynamically obtain user preferences, we divide the diffusion hypergraph into multiple subgraphs according to the time stamps, develop hypergraph attention networks to explicitly learn complete interactions, and adopt a gated fusion strategy to connect them for user cascade prediction. In addition, a new drop immediately method DropMessage is added to increase the robustness of the model. Experimental results on three real-world datasets indicate that proposed model significantly outperforms the most advanced information propagation prediction model in both MAP@k and Hits@K metrics, and the experiment also proves that the model achieves more significant prediction performance than the existing model under data perturbation.

НЕЙРОСЕТЕВОЙ ПОДХОД К КОГНИТИВНОМУ МОДЕЛИРОВАНИЮ

Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.

A Fully Coupled Discontinuous Deformation Analysis Model for Simulating Hydromechanical Processes in Fractured Porous Media

Numerical simulations play a key role in the optimization of fracturing operation designs for unconventional reservoirs. Because of the presence of numerous natural discontinuities and pores, the rock masses of reservoirs can be regarded as fractured porous media. In this paper, a fully coupled discontinuous deformation analysis model is newly developed to simulate the hydromechanical processes in fractured and porous media. The coupling of fracture seepage, pore seepage, and fracture network propagation is realized under the framework of DDA. The developed model is verified with several examples. Then, the developed DDA model is applied to simulate the hydraulic fracturing processes in fractured porous rock masses, and the effects of rock mass permeability on fracturing are investigated. Our findings suggest that high rock permeability may inhibit the stimulation of fracture networks, while increasing the viscosity of fracturing fluids can enhance the fracturing efficiency. This study provides a valuable numerical tool for simulating hydromechanical processes in fractured and porous media and can be used to analyze various geo-mechanical problems related to fluid interactions.

PONYTA: prioritization of phenotype-related genes from mouse KO events using PU learning on a biological network.

Transcriptome data from gene knock-out (KO) experiments in mice provide crucial insights into the intricate interactions between genotype and phenotype. Differentially expressed gene (DEG) analysis and network propagation (NP) are well-established methods for analyzing transcriptome data. To determine genes related to phenotype changes from a KO experiment, we need to choose a cutoff value for the corresponding criterion based on the specific method. Using a rigorous cutoff value for DEG analysis and NP is likely to select mostly positive genes related to the phenotype, but many will be rejected as false negatives. On the other hand, using a loose cutoff value for either method is prone to include a number of genes that are not phenotype-related, which are false positives. Thus, the research problem at hand is how to deal with the trade-off between false negatives and false positives. We propose a novel framework called PONYTA for gene prioritization via positive-unlabeled (PU) learning on biological networks. Beginning with the selection of true phenotype-related genes using a rigorous cutoff value for DEG analysis and NP, we address the issue of handling false negatives by rescuing them through PU learning. Evaluations on transcriptome data from multiple studies show that our approach has superior gene prioritization ability compared to benchmark models. Therefore, PONYTA effectively prioritizes genes related to phenotypes derived from gene KO events and guides in vitro and in vivo gene KO experiments for increased efficiency. The source code of PONYTA is available at https://github.com/Jun-Hyeong-Kim/PONYTA.

Overcoming photon and spatiotemporal sparsity in fluorescence lifetime imaging with SparseFLIM.

Fluorescence lifetime imaging microscopy (FLIM) provides quantitative readouts of biochemical microenvironments, holding great promise for biomedical imaging. However, conventional FLIM relies on slow photon counting routines to accumulate sufficient photon statistics, restricting acquisition speeds. Here we demonstrate SparseFLIM, an intelligent paradigm for achieving high-fidelity FLIM reconstruction from sparse photon measurements. We develop a coupled bidirectional propagation network that enriches photon counts and recovers hidden spatial-temporal information. Quantitative analysis shows over tenfold photon enrichment, dramatically improving signal-to-noise ratio, lifetime accuracy, and correlation compared to the original sparse data. SparseFLIM enables reconstructing spatially and temporally undersampled FLIM at full resolution and channel count. The model exhibits strong generalization across experimental modalities including multispectral FLIM and in vivo endoscopic FLIM. This work establishes deep learning as a promising approach to enhance fluorescence lifetime imaging and transcend limitations imposed by the inherent codependence between measurement duration and information content.

Predicting Bacterial Antibiotic Resistance using MALDI-TOF Mass Spectrometry Databases with ELM Applications.

Early detection of antibiotic resistance is a crucial task, especially for vulnerable patients under prolonged treatments with a single antibiotic. To solve this, machine learning approaches have been reported in the state of art. Researchers have used MALDI-TOF MS in order to predict antibiotic resistance and/or susceptibility in bacterial samples. Weis, et al. implemented LR, LightGBM and ANN to study the antibiotic resistance on bacterial strains of Escherichia Coli, Staphylococcus Aureus, and Klebsiella Pneumoniae. Despite promising results, the models have not achieved perfect accuracy, specifically when the classes are unbalanced. On the other hand, Extreme Learning Machine (ELM) is a training algorithm for forward propagation of single hidden layer neural networks, which converges much faster than traditional methods and offers promising performance along with less programmer intervention. In this way, this study introduced improved ELMs, including two weighted ELMs proposed by Zong, and the SMOTE technique in order to create new synthetic samples of the minority class. After heuristic optimization of ELM hiper-parameters, results demonstrated 85% in accuracy and 85% in geometric mean for the classification problem in the case of weighted ELM 1 subject to the SMOTE technique of oversampling.

Aircraft engine fault diagnosis based on fused convolutional Transformer

Aircraft engines will face corrosion and erosion problems when working in harsh gas path environments for a long time, and the fault parameter characteristics are not obvious. Therefore, accurate aircraft engine fault diagnosis methods are of great significance to ensure the safe operation of aircraft. In order to improve the prediction accuracy, an aircraft engine fault diagnosis method based on fused convolutional Transformer is proposed. The self-attention mechanism is used to extract useful features and suppress redundant information. The maximum pooling layer (MaxPool) is introduced into the Transformer model to further reduce the model memory consumption and parameter quantity, and alleviate the overfitting phenomenon. The simulation data set of turbofan engines based on GasTurb modeling is used for verification. The results show that compared with the Transformer network and other traditional deep learning models Back Propagation Neural Networks (BP network), Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), the accuracy rates are improved by 6.552%、28.117%、13.189%and respectively 10.29%, which proves the effectiveness of the proposed method and can provide a certain reference for aircraft engine fault diagnosis.

Neural network and layer-wise relevance propagation reveal how ice hockey protective equipment restricts players' motion.

Understanding the athlete's movements and the restrictions incurred by protective equipment is crucial for improving the equipment and subsequently, the athlete's performance. The task of equipment improvement is especially challenging in sports including advanced manoeuvres such as ice hockey and requires a holistic approach guiding the researcher's attention toward the right variables. The purposes of this study were (a) to quantify the effects of protective equipment in ice hockey on player's performance and (b) to identify the restrictions incurred by it. Twenty male hockey players performed four different drills with and without protective equipment while their performance was quantified. A neural network accompanied by layer-wise relevance propagation was applied to the 3D kinematic data to identify variables and time points that were most relevant for the neural network to distinguish between the equipment and no equipment condition, and therefore presumable result from restrictions incurred by the protective equipment. The study indicated that wearing the protective equipment, significantly reduced performance. Further, using the 3D kinematics, an artificial neural network could accurately distinguish between the movements performed with and without the equipment. The variables contributing the most to distinguishing between the equipment conditions were related to the upper extremities and movements in the sagittal plane. The presented methodology consisting of artificial neural networks and layer-wise relevance propagation contributed to insights without prior knowledge of how and to which extent joint angles are affected in complex maneuvers in ice hockey in the presence of protective equipment. It was shown that changes to the equipment should support the flexion movements of the knee and hip and should allow players to keep their upper extremities closer to the torso.

Analysis of Low-Frequency Sound Absorption Performance and Optimization of Structural Parameters for Acoustic Metamaterials for Spatial Double Helix Resonators

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.

Influence maximization based on discrete particle swarm optimization on multilayer network

Influence maximization (IM) aims to strategically select influential users to maximize information propagation in social networks. Most of the existing studies focus on IM in single-layer networks. However, we have observed that individuals often engage in multiple social platforms to fulfill various social needs. To make better use of this observation, we consider an extended problem of how to maximize influence spread in multilayer networks. The Multilayer Influence Maximization (MLIM) problem is different from the IM problem because information propagation behaves differently in multilayer networks compared to single-layer networks: users influenced on one layer may trigger the propagation of information on another layer. Our work successfully models the information propagation process as a Multilayer Independent Cascade model in multilayer networks. Based on the characteristics of this model, we introduce an approximation function called Multilayer Expected Diffusion Value (MLEDV) for it. However, the NP-hardness of the MLIM problem has posed significant challenges to our work. To tackle the issue, we devise a novel algorithm based on Discrete Particle Swarm Optimization. Our algorithm consists of two stages: 1) the candidate node selection, where we devise a novel centrality metric called Random connectivity Centrality to select candidate nodes, which assesses the importance of nodes from a connectivity perspective. 2)the seed selection, where we employ a discrete particle swarm algorithm to find seed nodes from the candidate nodes. We use MLEDV as a fitness function to measure the spreading power of candidate solutions in our algorithm. Additionally, we introduce a Neighborhood Optimization strategy to increase the convergence of the algorithm. We conduct experiments on four real-world networks and two self-built networks and demonstrate that our algorithms are effective for the MLIM problem.

Graphical model for mixed data types

With the development of data collection technologies, data types have become more diverse. Additionally, graphical models, as tools for describing variable network relationships, have become increasingly popular in recent years. Previous studies have focused on graphical models tailored to specific types of data. However, these existing methods fail to identify graphical models for mixed data types. The difficulty of constructing graphical models for mixed data types lies in the fact that each type of data has its own space, which challenges the estimation of network relationships in a graphical model when the data are combined. To address this issue, this study presents a novel method that utilizes a vectorization and alignment strategy developed particularly for mixed data types, including scalar, interval-valued, compositional, and functional data, to estimate a graphical model. By iteratively employing a block-sparse graphical lasso method on aligned data, the method can achieve satisfactory results, as shown by numerous simulation experiments. The results also validate the superiority of our proposed method over potential competing methods. Furthermore, this method was applied to an engine damage propagation network as an illustrative example. Our method provides a novel modeling approach for graphical models in the case of mixed data types.

Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network.

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

Fast Selection of Indoor Wireless Transmitter Locations With Generalizable Neural Network Propagation Models

Fast Selection of Indoor Wireless Transmitter Locations With Generalizable Neural Network Propagation Models

Optical soliton solutions for the nonlinear Schrödinger equation with higher-order dispersion arise in nonlinear optics

Optical solitons and traveling wave solutions for the higher-order dispersive extended nonlinear Schrödinger equation are studied. Ultrashort pulse propagation in optical communication networks is described by this equation. To find exact solutions to the model, the unified Riccati equation expansion method and the Jacobi elliptic function expansion method are successfully applied. The optical solutions includes various solitary wave solutions, such as dark, bright, combined dark-bright, singular, combined periodic, periodic, Jacobian elliptic, and rational functions. Three-dimensional and two-dimensional graphs of solutions are presented. Also, the dynamical behavior of waves and the impact of time on solutions by selecting appropriate parameters are illustrated.

Novel static decoupling algorithm for the multi-axis wheel force sensor based on the Informer network

Ensuring the stability of heavy multi-axle vehicles necessitates the accurate calibration and decoupling of Multi-axis Wheel Force Sensors (MWFS). Traditional methods often neglect the temporal coupling present in MWFS output data, leading to reduced accuracy. This paper introduces an Improved Decoupling Algorithm (IDI) based on the Informer network, designed to temporally decouple MWFS and enhance precision. The Decoupling embedding layer (DE) performs linear decoupling of the MWFS, while the Token embedding layer (TE) and Informer encoder extract timing coupling features. The highway network and linear fully-connected layer then provide nonlinear decoupling compensation. Experimental results demonstrate that the IDI algorithm significantly outperforms traditional methods like Extreme Learning Machine (ELM) and Back Propagation Network (BPNN), achieving at least a 41.12% improvement in accuracy in the highly coupled Mz channel. In conclusion, the IDI algorithm not only achieves high-precision decoupling of MWFS but also presents a robust framework for modeling various sensor types.

Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics

Disease severity through an immunized population ensconced on a physical network topology is a key technique for preventing epidemic spreading. Its influence can be quantified by adjusting the common (basic) methodology for analyzing the percolation and connectivity of contact networks. Stochastic spreading properties are difficult to express, and physical networks significantly influence them. Visualizing physical networks is crucial for studying and intervening in disease transmission. The multi-agent simulation method is useful for measuring randomness, and this study explores stochastic characteristics of epidemic transmission in various homogeneous and heterogeneous networks. This work thoroughly explores stochastic characteristics of epidemic propagation in homogeneous and heterogeneous networks through extensive theoretical analysis (positivity and boundedness of solutions, disease-free equilibrium point, basic reproduction number, endemic equilibrium point, stability analysis) and multi-agent simulation approach using the Gilespie algorithm. Results show that Ring and Lattice networks have small stochastic variations in the ultimate epidemic size, while BA-SF networks have disease transmission starting before the threshold value. The theoretical and deterministic aftermaths strongly agree with multi-agent simulations (MAS) and could shed light on various multi-dynamic spreading process applications. The study also proposes a novel concept of void nodes, Empty nodes and disease severity, which reduces the incidence of contagious diseases through immunization and topologies.

Failure mechanism of a tunnel in a soil–rock mixture based on a new combined finite–discrete element method

The mechanical properties and failure characteristics of soil–rock mixtures (SRMs) directly affect the stability of tunnels constructed in SRMs. A new SRM modelling method based on the combined finite–discrete element method (FDEM) was proposed. Using this new SRM modelling method based on the FDEM, the mechanical characteristics and failure behaviour of SRM samples under uniaxial compression, as well as the failure mechanism of SRMs around a tunnel, were further investigated. The study results support the following findings: (1) The modelling of SRM samples can be achieved using a heterogeneous rock modelling method based on the Weibull distribution. By adjusting the relevant parameters, such as the soil–rock boundaries, element sizes and modelling control points, SRM models with different rock contents and morphologies can be obtained. (2) The simulation results of uniaxial compression tests of SRM samples with different element sizes and morphologies validate the reliability and robustness of the new modelling method. In addition, with increasing rock content, VBP (volumetric block proportion), the uniaxial compressive strength and Young’s modulus increase exponentially, but the samples all undergo single shear failure within the soil or along the soil–rock interfaces, and the shear failure angles are all close to the theoretical values. (3) Tunnels in SRMs with different rock contents all exhibit X-shaped conjugate shear failure, but the fracture network propagation depth, the maximum displacement around the tunnel, and the failure degree of the tunnel in the SRM roughly decreases via a power function as the rock content increases. In addition, as the rock content increases, such as when VBP = 40%, large rocks have a significant blocking effect on fracture propagation, resulting in an asymmetric fracture network around the tunnel. (4) The comparisons of uniaxial compression and tunnel excavation simulation results with previous theoretical results, laboratory test results, and numerical simulation results verify the correctness of the new modelling method proposed in this paper.