Spectrome-AI: a Neural Network Framework for Inferring MEG Spectra

Computational modeling is a tool that allows for biological systems involving large networks to be studied, such as in studying the correlations between structural connectivity and functional connectivity in the human brain. Raj et al. proposed the spectral graph model in 2019 as a linear, low-dimensional alternative to conventional neural field and mass models that are more computationally expensive, especially when optimizing parameters, which is necessary in order to obtain quantitative and qualitative information about functional neural activity. The initial method used for inferring the spectral graph model parameters was Markov chain Monte Carlo (MCMC) sampling, which provided a robust way to estimate what the target parameter distributions were most likely to be. However, MCMC methods are still slow and computationally expensive. In this study, we trained a fully connected neural network on MCMC-simulated magnetoencephalography (MEG) data to perform parameter estimation for the spectral graph model in an accelerated manner. We found that the neural network was able to predict most parameters of interest without much loss in precision while generating the parameters in less than a second. This approach puts us closer to obtaining real time neurophysiological information from functional neuroimaging data for applications in diagnosis, prognosis, and characterization of various neurological diseases

ONLINE SERVICE CO-CUSTOMIZATION: HOW THE PARTNER AND THE INFORMATION PRESENTATION AFFECTS TOURISTS’ CHOICE OF ONLINE TOUR SERVICES

People always travel with their friends. Some of them would like to design their travel plan together while some others would like to design their plan singly. Prior studies most focus on the single decision context. This paper investigates the collaborative customization in the joint decision and joint consumption context, and the information presentation format (attribute-based vs. bundle-based) effect on the tourists’ decision and behaviour is discussed. We also consider the relationship effect. And finally the potential theoretical contribution and practical implication are discussed

Characterization of transient groundwater flow through a high arch dam foundation during reservoir impounding

AbstractEven though a large number of large-scale arch dams with height larger than 200 m have been built in the world, the transient groundwater flow behaviors and the seepage control effects in the dam foundations under difficult geological conditions are rarely reported. This paper presents a case study on the transient groundwater flow behaviors in the rock foundation of Jinping I double-curvature arch dam, the world's highest dam of this type to date that has been completed. Taking into account the geological settings at the site, an inverse modeling technique utilizing the time series measurements of both hydraulic head and discharge was adopted to back-calculate the permeability of the foundation rocks, which effectively improves the uniqueness and reliability of the inverse modeling results. The transient seepage flow in the dam foundation during the reservoir impounding was then modeled with a parabolic variational inequality (PVI) method. The distribution of pore water pressure, the amount of leakage, and the performance of the seepage control system in the dam foundation during the entire impounding process were finally illustrated with the numerical results

Protocol selection for second-order consensus against disturbance

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings

Multi-scale diff-changed feature fusion network for hyperspectral image change detection.

For hyperspectral images (HSI) change detection (CD), multi-scale features are usually used to construct the detection models. However, the existing studies only consider the multi-scale features containing changed and unchanged components, which is difficult to represent the subtle changes between bi-temporal HSIs in each scale. To address this problem, we propose a multi-scale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bi-temporal HSIs under different scales. In this network, a temporal feature encoder-decoder sub-network, which combines a reduced inception module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation module is proposed to learn the fine changed features of bi-temporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multi-scale attention fusion module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change of bi-temporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods

λ-Density Functional Valence Bond: A Valence Bond-Based Multiconfigurational Density Functional Theory With a Single Variable Hybrid Parameter

A new valence bond (VB)-based multireference density functional theory (MRDFT) method, named λ-DFVB, is presented in this paper. The method follows the idea of the hybrid multireference density functional method theory proposed by Sharkas et al. (2012). λ-DFVB combines the valence bond self-consistent field (VBSCF) method with Kohn–Sham density functional theory (KS-DFT) by decomposing the electron–electron interactions with a hybrid parameter λ. Different from the Toulouse's scheme, the hybrid parameter λ in λ-DFVB is variable, defined as a function of a multireference character of a molecular system. Furthermore, the EC correlation energy of a leading determinant is introduced to ensure size consistency at the dissociation limit. Satisfactory results of test calculations, including potential energy surfaces, bond dissociation energies, reaction barriers, and singlet–triplet energy gaps, show the potential capability of λ-DFVB for molecular systems with strong correlation

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China’s carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development

Synthesis of 1-chloro-1,2,4,6-selenatriazines and some products of reduction

xv, 172 leaves : ill. ; 29 cm.A general route to 1-chloro-1,2,4,6-selenatriazines with substuents on 3,5 positions has been developed by the reactions of N-imidoylamidines with selenium tetrachloride. The mechanism for these reactions is discussed according to the observed intermediates. At least two intermediates exist. One of the intermediates, 1,1-dichloro-3-trichloromethyl-4H-5-diisopropylphenyl-1,2,4,6-selenatriazine, was identified by 1HNMR, Mass spectroscopy and X-ray crystallography. 1-Chloro-1,2,4,6-selenatriazines were synthesized in high yield and fully characterized. Five 1-chloro-1,2,4,6-selenatriazine crystal structures were obtained. Reduction of 1-chloro-1,2,4,6-selenatriazines with triphenylamtimony immediately produced the corresponding selenatriazinyl radicals in hot acetonitrile. Pure radicals were obtained by in-situ crystallization as their dimers from reaction. Two crystal structures were obtained for 3-trifluoromethyl-5-p-tolyl-1,2,4,6-selenatriazinyl dimer and 3-trifluoromethyl-5-p-methyloxyphenyl-1,2,4,6-selenatriazinyl dimer. EPR spectroscopy measured all radicals coupling to three unique nitrogen atoms with 7 broad lines. There is no resolvable hyperfine coupling to 77Se, 37Cl/19F and phenyl protons