Compared with general multi-target tracking filters, this paper focuses on multi-target trajectories in scenarios where the detection probability of the sensor is unknown. In this paper, two trajectory Poisson multi-Bernoulli (TPMB) filters with unknown detection probability are proposed: one for alive trajectories and the other for all trajectories. First, an augmented trajectory state with detection probability is constructed, and then two new state transition models and a new measurement model are proposed. Then, this paper derives the recursion of TPMB filters with unknown detection probability. Furthermore, the detailed beta-Gaussian implementations of TPMB filters for alive trajectories and all trajectories are presented. Finally, simulation results demonstrate that the proposed TPMB filters with unknown detection probability can achieve robust tracking performance and effectively estimate multi-target trajectories.

The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, we proposed a structure-preserved scRNA-seq data integration approach using heterogeneous graph neural network (scHetG). By establishing a heterogeneous graph that represents the interactions between multiple batches of cells and genes, and combining a heterogeneous graph neural network with contrastive learning, scHetG concurrently obtained cell and gene embeddings with structural information. A comprehensive assessment covering different species, tissues and scales indicated that scHetG is an efficacious method for eliminating batch effects while preserving the structural information of cells and genes, including batch-specific cell types and cell-type specific gene co-expression patterns.

MOTIVATION: Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment. RESULTS: In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. AVAILABILITY AND IMPLEMENTATION: The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.

Convolutional neural networks (CNN) have attracted considerable interest in seismic interpolation, in these networks, convolution operators are adopted to extract the features of seismic data, and the interpolation network is guided to learn the mapping between the corrupted data and their labels. However, the trained network only captures the interrelationship between data localities due to the local receptive field limitation of the convolution kernel, limiting the accuracy of interpolation. The Transformer uses a self-attention mechanism and has performed well in multiple areas. Motivated by this, we propose a multi-scale Transformer (MST) to restore incomplete seismic data. Based on the self-attention mechanism, the Transformer module calculate multiple groups of self-attention for multi-scale feature maps to capture long-range dependencies; it can recover the detailed information of missing data with higher accuracy. Synthetic and field seismic data interpolation experiments verified the performance of the proposed reconstruction method.

Convolutional neural network (CNN)-based methods are powerful tools for seismic data denoising. Most methods adopt a supervised learning strategy, which requires noise-free labels to construct an objective function to guide the training of network parameters; however, it is impossible to obtain true noise-free field data. We propose a novel unsupervised random-noise-suppression method that can train a network directly on noisy target data without noise-free labels. The proposed method is inspired by the simple denoising idea of averaging multiple noisy observations, and it requires noise to satisfy two assumptions: it should be zero-mean and independent of the signal. In this method, multiple observations can be used as labels, and the loss function is constructed as the mean square error expected between the network output and these observations. The trained network estimates the expected value of these noise observations (i.e., the clean signal). This unsupervised method theoretically requires multiple noisy observations. Considering the good nonlocal self-similarity of seismic data, we used self-similar blocks to rearrange the data to construct multiple pseudo-observations and finally realize unsupervised training. The proposed method was compared with the traditionalf-xdeconvolution, curvelet, and generator CNN method on synthetic and field data, and the experimental results verified the effectiveness of the proposed method.

Existing deep learning-based seismic data denoising methods mainly involve supervised learning, in which a denoising network is trained using a large amount of noisy input/clean label pairs. However, the scarcity of high-quality clean labels in practice, limits the applicability of these methods. Recently, the blind spot (BS) strategy in the field of image processing has attracted extensive attention. Under the assumption that the noise is statistically independent, and the true signal exhibits some correlation, BS strategy allows us to estimate a denoiser from the noisy data itself. In this paper, we study the application of the BS strategy to the random noise attenuation of seismic data, and propose an unsupervised blind spot network (BSnet) method. Specifically, considering the characteristics of the random noise, we improve the commonly used Unet network and design two types of randomly mask operators to deal with Gaussian white noise and band-pass noise respectively. Synthetic and real data experiments validate the effectiveness of the proposed method.

ABSTRACT This paper proposes a novel method to detect the number of two‐dimensional (2D) harmonics in additive colored noise based on the enhanced covariance matrix. We define an enhanced covariance matrix using the covariances of the observed signal. We get a special inherent relation between the number of 2D harmonics in additive colored noise and the eigenvalues of the enhanced covariance matrix, which can be used to detect the number of 2D harmonics in additive colored noise by analyzing the eigenvalues of the enhanced covariance matrix. The proposed method has a super resolution and does not need to assume the color and distribution of the additive noise. The effectiveness of the proposed method has been validated by both the theoretical analysis and extensive simulations. Copyright © 2012 John Wiley & Sons, Ltd.

Classification is an important task in data mining, while accurate class probability estimation is also desirable in real-world applications. Some probability-based classifiers, such as the k-nearest neighbor algorithm (KNN) and its variants, can estimate the class membership probabilities of the test instance. Unfortunately, a good classifier is not always a good class probability estimator. In this paper, we try to improve the class probability estimation performance of KNN and its variants. As we all know, KNN and its variants are all of the distance-related algorithms and their performance is closely related to the used distance metric. Value Difference Metric (VDM) is one of the widely used distance metrics for nominal attributes. Thus, in order to scale up the class probability estimation performance of the distance-related algorithms such as KNN and its variants, we propose an Attribute Weighted Value Difference Metric (AWVDM) in this paper. AWVDM uses the mutual information between the attribute variable and the class variable to weight the difference between two attribute values of each pair of instances. Experimental results on 36 UCI benchmark datasets validate the effectiveness of the proposed AWVDM.

A large number of distance metrics have been proposed to measure the difference of two instances. Among these metrics, Short and Fukunaga metric (SFM) and minimum risk metric (MRM) are two probability-based metrics which are widely used to find reasonable distance between each pair of instances with nominal attributes only. For simplicity, existing works use naive Bayesian (NB) classifiers to estimate class membership probabilities in SFM and MRM. However, it has been proved that the ability of NB classifiers to class probability estimation is poor. In order to scale up the classification performance of NB classifiers, many augmented NB classifiers are proposed. In this paper, we study the class probability estimation performance of these augmented NB classifiers and then use them to estimate the class membership probabilities in SFM and MRM. The experimental results based on a large number of University of California, Irvine (UCI) data-sets show that using these augmented NB classifiers to estimate the class membership probabilities in SFM and MRM can significantly enhance their generalisation ability.

In this letter, we present a new method for estimating the number of harmonics in colored noise using enhanced matrix. We construct an enhanced matrix from the data samples and then analyze the eigenvalues of the covariance matrix of enhanced matrix. The number of harmonics is inherent with the eigenvalues, and it can be estimated using the special property of the eigenvalues. The presented method does not assume the distribution and color of the additive noise. Simulation results are provided to verify the effectiveness of the presented method