Research on self-cross transformer model of point cloud change detecter

With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based ontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network

Bayesian Inference of Time-Varying Origin-Destination Matrices from Boarding/Alighting Counts for Transit Services

Origin-destination (OD) demand matrices are crucial for transit agencies to design and operate transit systems. This paper presents a novel temporal Bayesian model designed to estimate transit OD matrices at the individual bus-journey level from boarding/alighting counts at bus stops. Our approach begins by modeling the number of alighting passengers at subsequent bus stops, given a boarding stop, through a multinomial distribution parameterized by alighting probabilities. Given the large scale of the problem, we generate alighting probabilities with a latent variable matrix and factorize it into a mapping matrix and a temporal matrix, thereby substantially reducing the number of parameters. To further encode a temporally-smooth structure in the parameters, we impose a Gaussian process prior on the columns of the temporal factor matrix. For model inference, we develop a two-stage algorithm with the Markov chain Monte Carlo (MCMC) method. In the first stage, latent OD matrices are sampled conditional on model parameters using a Metropolis-Hastings sampling algorithm with a Markov model-based proposal distribution. In the second stage, we sample model parameters conditional on latent OD matrices using slice and elliptical slice sampling algorithms. We assess the proposed model using real-world data collected from three bus routes with varying numbers of stops, and the results demonstrate that our model achieves accurate posterior mean estimation and outperforms the widely used iterative proportional fitting (IPF) method. Additionally, our model can provide uncertainty quantification for the OD demand matrices, thus benefiting many downstream planning/operational tasks that require robust decisions

Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression

Accurately forecasting bus travel time and passenger occupancy with uncertainty is essential for both travelers and transit agencies/operators. However, existing approaches to forecasting bus travel time and passenger occupancy mainly rely on deterministic models, providing only point estimates. In this paper, we develop a Bayesian Markov regime-switching vector autoregressive model to jointly forecast both bus travel time and passenger occupancy with uncertainty. The proposed approach naturally captures the intricate interactions among adjacent buses and adapts to the multimodality and skewness of real-world bus travel time and passenger occupancy observations. We develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm to approximate the resultant joint posterior distribution of the parameter vector. With this framework, the estimation of downstream bus travel time and passenger occupancy is transformed into a multivariate time series forecasting problem conditional on partially observed outcomes. Experimental validation using real-world data demonstrates the superiority of our proposed model in terms of both predictive means and uncertainty quantification compared to the Bayesian Gaussian mixture model

Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach

Time series analysis is a fundamental task in various application domains, and deep learning approaches have demonstrated remarkable performance in this area. However, many real-world time series data exhibit significant periodic or quasi-periodic dynamics that are often not adequately captured by existing deep learning-based solutions. This results in an incomplete representation of the underlying dynamic behaviors of interest. To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain. The Floss method first automatically detects major periodicities from the time series. It then employs periodic shift and spectral density similarity measures to learn meaningful representations with periodic consistency. In addition, Floss can be easily incorporated into both supervised, semi-supervised, and unsupervised learning frameworks. We conduct extensive experiments on common time series classification, forecasting, and anomaly detection tasks to demonstrate the effectiveness of Floss. We incorporate Floss into several representative deep learning solutions to justify our design choices and demonstrate that it is capable of automatically discovering periodic dynamics and improving state-of-the-art deep learning models.Comment: 12 page

Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression

The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Towards this end, we develop a time-varying reduced-rank vector autoregression (VAR) model whose coefficient matrices are parameterized by low-rank tensor factorization. Benefiting from the tensor factorization structure, the proposed model can simultaneously achieve model compression and pattern discovery. In particular, the proposed model allows one to characterize nonstationarity and time-varying system behaviors underlying spatiotemporal data. To evaluate the proposed model, extensive experiments are conducted on various spatiotemporal data representing different nonlinear dynamical systems, including fluid dynamics, sea surface temperature, USA surface temperature, and NYC taxi trips. Experimental results demonstrate the effectiveness of modeling spatiotemporal data and characterizing spatial/temporal patterns with the proposed model. In the spatial context, the spatial patterns can be automatically extracted and intuitively characterized by the spatial modes. In the temporal context, the complex time-varying system behaviors can be revealed by the temporal modes in the proposed model. Thus, our model lays an insightful foundation for understanding complex spatiotemporal data in real-world dynamical systems. The adapted datasets and Python implementation are publicly available at https://github.com/xinychen/vars

Colchicine protects against acute pancreatitis via downregulation of cytokine levels

Purpose: To investigate the effect of colchicine on acute pancreatitis (AP) in a rat model, and the molecular mechanism involved. Methods: Acute pancreatitis (AP) was induced in rats by injection with 5 % sodium taurocholate. Changes in histology of pancreatic tissues were determined following treatment with colchicine. Serum amylase activity was measured using Automated Biochemistry Analyser. Results: Hematoxylin and eosin (H & E) staining showed that colchicine prevented histopathological changes such as infiltration of interstitial leukocytes and erythrocytes, cell necrosis, oedema formation and vacuolization in the rat pancreas. Treatment of AP rats with colchicine significantly and dosedependently decreased ascite volume in the abdominal cavity. Serum amylase activity was significantly suppressed in AP rats on treatment with 100 mg/kg colchicine. Furthermore, treatment of the AP rats with colchicine caused marked decrease in the expressions of interleukin 6 and tumor necrosis factor α, and upregulated expressions of IL 10 in serum. Colchicine treatment of AP rats also caused significant increase in CGRP level in the plasma. Conclusion: Colchicine prevents pancreatic tissue damage induced by AP by down-regulating proinflammatory cytokines, upregulation of anti-inflammatory cytokines, and enhancing CGRP release. Therefore, colchicine may be useful for the treatment of acute pancreatitis

Prediction for the surface settlement of double-track subway tunnels for shallow buried loess based on peck formula

In the process of constructing double-track subway tunnels in shallow buried loess areas, the interaction of double-track tunnels is significantly influenced by the net distance and the cross-section size, which is challenging to control the surface settlement. Therefore, the surface settlement prediction is essential while constructing double-track subway tunnels in shallow buried loess areas. The paper analyzed the surface settlement law of shallow buried double-track tunnels in loess areas through theoretical research and numerical simulation. The research results show that with the decrease of the net distance, the surface settlement superimposed curve was double V shape -W shape - single V shape. When the superimposed curve is double V shape and W shape, the Peck formula was used to calculate the surface settlement curve of the single-track tunnel, then superimposed to obtain the final surface settlement curve. When the superimposed surface settlement curve was V shape, based on the Peck formula, the formula for predicting the surface settlement suitable for symmetry and asymmetry was established. The net distance ratio and the area ratio were defined, and considering the tunnel’s interaction, the value and position of the maximum were corrected. Then numerical tests were carried out 16 times with different net distance ratios and area ratios, to determine the parameters of increments and position offsets of the maximum regarding the net distance ratio and the area ratio. Finally, two engineering were conducted for verifying the rationality and applicability exhibited by the prediction formula. The prediction formula served for predicting the surface settlement of double-track subway tunnels in shallow buried loess areas. Which can reduce construction risks and assure the safety of buildings above the ground

The Effect of Nanoparticle Surface State on Trap Level Distribution of Polyimide/Aluminum Nitride-montmorillonite Nanocomposite Films

The electrical properties of polyimide (PI) nanocomposites, which are widely used in microelectronic industry and electrical engineering fields, strongly depend on the surface state of nanoparticles. To explore this dependence, the aluminum nitride (AIN) nanoparticles were treated by γ-aminopropyltriethoxysilane coupling agent, while PI/MMT, PI/AlN, and PI/AlN-MMT nanocomposite films doped by 5 wt% of treated and untreated AlN nanoparticles were prepared by the in-situ polymerization process. The SEM and TEM results indicate that the untreated AlN nanoparticles are prone to accumulation in the polymer matrix, while those treated by the coupling agent are readily combined with the polyimide matrix, and their compatibility and dispersion exhibit a significant improvement. The trap level distributions of nanocomposite films were studied by the isothermal discharge current (IDC) method based on the charge decay theory linking IDC with the trap level density (TLD). The TLD and number of trapped charges of PI/AlN and PI/AlN-MMT films doped by treated AlN nanoparticles are found to be much higher than those of untreated ones. The TLD of the PI/AlN (treated) film is 6.490×1023 eV·m-3, which is about 2.27 times higher than that of pure PI film in the range of 0.9~1.1 eV, while the maximum TLD=9.370×1023 eV·m-3 is observed in the PI/AlN (treated)-MMT film