The spiking neuron model (SNM) mimics the processing paradigm of synaptic and membrane potentials in the cerebral cortex. However, existing SNMs are limited by two issues. First, they lack spike diversity. Although a spiking neuron perceives temporally varying input currents, SNMs only use identical synaptic weights for regulation. Second, they are insensitive to weak spikes. The potential accumulation in SNMs is solely driven by external inputs, ignoring the internal dynamics of potential. Oligodendrocytes, a recent revelation in neuroscience, enhance neural signaling by forming bidirectional communication. This offers the potential to alleviate the aforementioned issues. In this paper, we first propose the mechanism of the oligodendrocyte-spiking neuron (Oli-N) model. Subsequently, using the Oli-N model, we develop our Oli-inspired spiking neural network (Oli-SNN), which broadens the diversity of spike representations and enhances neurons' firing precision through improved sparse coding to enhance weak spikes. Experiments show that our Oli-SNN achieves state-of-the-art performance in the classification task on both static and neuromorphic datasets.

With the increasing size of datasets and the expansion of Deep Neural Networks (DNNs), the training process has become exceedingly time-consuming. Distributed training, specifically the Pipeline Model Parallelism (PMP) method, commonly mitigates this problem but suffers from bubble time delays. This paper proposes LIBPipe, a pipeline training framework that explicitly incorporates a load-imbalance method to reduce bubble time in PMP. Within LIBPipe, a model-unequal-partitioning method is designed from the perspective of load imbalance to reshape the pipeline execution pattern and significantly shorten idle periods during training. On top of this method, a performance-guided unequal-partitioning search algorithm is developed to efficiently identify near-optimal partitioning strategies under memory constraints. The paper theoretically proves the time efficiency of adopting load imbalance in pipeline models. Comprehensive experiments are conducted on an 8-GPU server to evaluate the efficiency of LIBPipe, using the IMDB and mini-ImageNet datasets as well as well-known models such as BERT and ResNet. The BERT-series models achieve a maximum throughput improvement of 60.3%, while the ResNet-series models achieve a maximum improvement of 74.1%.

Change detection in synthetic aperture radar (SAR) images is a hot yet highly challenging task in remote sensing. Existing unsupervised SAR change detection methods often struggle with inherent speckle noise and insufficiently utilize pseudo-labels, particularly neglecting uncertain areas. In this paper, we propose a multilevel difference-enhanced denoise dual-branch network (MDDNet), comprising representation learning and change detection branches. First, fuzzy c-means clustering is employed to generate pseudo-labels, categorizing the image areas as changed, nochanged, and uncertain. Second, we design a denoise representation loss function in the representation learning branch to maximize the use of pseudo-labels, while mitigating speckle noise. Furthermore, a multilevel difference computation module is proposed to focus on changes in ground objects and capture more comprehensive change information. Experimental results on three public SAR datasets show that the proposed method outperforms six state-of-the-art methods, achieving the best performance with an average overall accuracy of 98.86% and an average Kappa coefficient of 89.36%.

The rapid development of 3D technology has significantly impacted precision livestock management, enabling the evaluation and optimization of beef cattle growth by analyzing their three-dimensional geometric characteristics. This study presents a non-contact beef cattle surface reconstruction method using multi-view point clouds, captured by a synchronized multi-camera system, followed by point cloud preprocessing, the Intrinsic Shape Signature (ISS) keypoints extraction, and Principal Components Analysis (PCA) feature fusion for accurate alignment and reconstruction. In order to realize the point cloud alignment for 4 angles, the Super 4-Points Congruent Sets (Super4PCS) is used for coarse alignment, and then the Iterative Closest Point (ICP) algorithm accelerated by K-dimension tree (KD-Tree) is used to complete the fine alignment. Aiming at the missing point cloud caused by the obstruction of the railings, the repair method of bilateral filtering combined with the improved cubic B-spline of statistical analysis is proposed. Based on the highest point of the withers, the symmetry plane is determined to realize the reconstruction from unilateral point cloud to complete point cloud. Finally, Withers Height (WH), Hip Height (HH), Oblique Body Length (OBL), Heart Girth (HG), Abdominal Girth (AG), Cannon Girth (CG), Ischial Width (IW), and Hip Width (HW) values were measured by Euclidean Distance and B-spline curve fitting. The mean absolute error of the predicted values for the eight body scales for 100 beef cows was 4.09 cm, and the mean absolute percentage error was 4.01%. The maximum absolute error was 9.09 cm and the minimum absolute error was 0.74 cm. The experimental results show that this method can provide a new and accurate method for the reconstruction of multi-view livestock point clouds.

With the rapid development of CNN or Transformer, the present mainstream approaches regard an image patch as the reference of the target to perform tracking, which is known as template matching-based trackers. However, most existing template matching-based trackers only consider the per-frame localization accuracy, neglecting the potential distractor (similar object) dependencies among multiple video frames, which poses a fundamental challenge in template matching-based tracking. In this work, we propose a novel comprehensive framework with multi-frame distractor suppression for visual object tracking (MFDSTrack), which explicitly models the temporal history of both the target object and potential distractors. Specifically, we utilize a universal target candidate generation module to detect target candidates (both target and distractors), providing a holistic view of the scene. In addition, a temporal and distractor-aware association module is designed to suppress multi-frame distractors by adopting a simple encoder-decoder Transformer architecture. The encoder accepts inputs of target candidates’ history, while the decoder takes current target candidate queries and the output of the encoder as inputs to associate current target candidate queries with historical trajectories. We extensively evaluate our trackers, MFDSTrack-SD, MFDSTrack-OS, MFDSTrack-GRM, and MFDSTrack-LT on the LaSOT,${\mathrm {LaSOT}}_{ext}$, TrackingNet, GOT-10k, UAV123, NFS, and OTB100 benchmark. Extensive experiments show that our methods outperform previous state-of-the-art trackers on seven tracking benchmarks.

Most existing multi-view graph clustering models focus on integrating the topological structure of different views directly, which cannot efficiently stimulate the collaboration between multiple views. To alleviate this problem, this paper proposes a Triangle Topology Enhancement (T2E) module, which expands two topological structures based on the raw topology of each view, including the self-triangle enhanced topology that highlights the local view information and the cross-view triangle enhanced topology containing the global-local view information. Afterward, this paper designs a novel multi-view graph clustering model, named MGC-T2E, to integrate both the raw and derived topological structures and directly induce consistent clustering indicators based on a self-supervised clustering module. In the simulation, the experimental results demonstrate that MGC-T2E achieves state-of-the-art performances compared with a mass of current competitors.

Seed expansion tasks play an important role in various network applications such as recommendation systems, social network analysis, and bioinformatics. Given a network and a small group of examples as seeds, these tasks involve identifying additional members of interest from the same community. While most existing expansion methods focus on defining a fixed metric function based on the network structure alone, they often overlook the rich content associated with nodes in attributed networks.

Change detection in synthetic aperture radar (SAR) images is a vital application in remote sensing image processing. Existing unsupervised SAR change detection methods often rely on pre-classification to generate pseudo-labels for classifying the image regions into three classes: nochanged, changed, and uncertain. However, these methods do not fully exploit the pseudo-labels by focusing only on changed and nochanged regions. In this letter, we propose a wavelet transform-based multi-scale self-supervised network (WS2Net), which maximizes the utilization of pseudo-labels and incorporates discriminative feature learning. First, we employ clustering as pre-classification to obtain the aforementioned pseudo-labels. Second, we propose a self-supervised triple loss inspired by contrastive and representation learning. This loss comprises the nochanged and changed losses in the feature domain along with the uncertain loss in the source domain. Furthermore, to extract valuable information from SAR images and to improve the noise robustness of the network, we design a wavelet transform-based multi-scale feature extraction module. Finally, a difference image is generated by comparing the features output from the network, which can be further analyzed through segmentation to obtain the final change map. Comparative experiments are conducted with five state-of-the-art methods on three public SAR data sets, showing that the proposed WS2Net achieves the best performance with an average percent correct classification of 97.89% and an average kappa coefficient of 90.24%.

Single-cell Hi-C data are a common data source for studying the differences in the three-dimensional structure of cell chromosomes. The development of single-cell Hi-C technology makes it possible to obtain batches of single-cell Hi-C data. How to quickly and effectively discriminate cell types has become one hot research field. However, the existing computational methods to predict cell types based on Hi-C data are found to be low in accuracy. Therefore, we propose a high accuracy cell classification algorithm, called scHiCStackL, based on single-cell Hi-C data. In our work, we first improve the existing data preprocessing method for single-cell Hi-C data, which allows the generated cell embedding better to represent cells. Then, we construct a two-layer stacking ensemble model for classifying cells. Experimental results show that the cell embedding generated by our data preprocessing method increases by 0.23, 1.22, 1.46 and 1.61$\%$ comparing with the cell embedding generated by the previously published method scHiCluster, in terms of the Acc, MCC, F1 and Precision confidence intervals, respectively, on the task of classifying human cells in the ML1 and ML3 datasets. When using the two-layer stacking ensemble framework with the cell embedding, scHiCStackL improves by 13.33, 19, 19.27 and 14.5 over the scHiCluster, in terms of the Acc, ARI, NMI and F1 confidence intervals, respectively. In summary, scHiCStackL achieves superior performance in predicting cell types using the single-cell Hi-C data. The webserver and source code of scHiCStackL are freely available at http://hww.sdu.edu.cn:8002/scHiCStackL/ and https://github.com/HaoWuLab-Bioinformatics/scHiCStackL, respectively.

Chromosome is composed of many distinct chromatin domains, referred to variably as topological domains or topologically associating domains (TADs). The domains are stable across different cell types and highly conserved across species, thus these chromatin domains have been considered as the basic units of chromosome folding and regarded as an important secondary structure in chromosome organization. However, the identification of TAD boundaries is still a great challenge due to the high cost and low resolution of Hi-C data or experiments. In this study, we propose a novel ensemble learning framework, termed as StackTADB, for predicting the boundaries of TADs. StackTADB integrates four base classifiers including Random Forest, Logistic Regression, K-NearestNeighbor and Support Vector Machine. From the analysis of a series of examinations on the data set in the previous study, it is concluded that StackTADB has optimal performance in six metrics, AUC, Accuracy, MCC, Precision, Recall and F1 score, and it is superior to the existing methods. In addition, the comparison of the performance of multiple features shows that Kmers-based features play an essential role in predicting TADs boundaries of fruit flies, and we also apply the SHapley Additive exPlanations (SHAP) framework to interpret the predictions of StackTADB to identify the reason why Kmers-based features are vital. The experimental results show that the subsequences matching the BEAF-32 motif play a crucial role in predicting the boundaries of TADs. The source code is freely available at https://github.com/HaoWuLab-Bioinformatics/StackTADB and the webserver of StackTADB is freely available at http://hwtad.sdu.edu.cn:8002/StackTADB.

MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although various experimental techniques have been developed to detect chromatin loops, they have been found to be time-consuming and costly. Nowadays, various sequence-based computational methods can capture significant features of 3D genome organization and help predict chromatin loops. However, these methods have low performance and poor generalization ability in predicting chromatin loops. RESULTS: Here, we propose a novel deep learning model, called CLNN-loop, to predict chromatin loops in different cell lines and CTCF-binding sites (CBS) pair types by fusing multiple sequence-based features. The analysis of a series of examinations based on the datasets in the previous study shows that CLNN-loop has satisfactory performance and is superior to the existing methods in terms of predicting chromatin loops. In addition, we apply the SHAP framework to interpret the predictions of different models, and find that CTCF motif and sequence conservation are important signs of chromatin loops in different cell lines and CBS pair types. AVAILABILITY AND IMPLEMENTATION: The source code of CLNN-loop is freely available at https://github.com/HaoWuLab-Bioinformatics/CLNN-loop and the webserver of CLNN-loop is freely available at http://hwclnn.sdu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient.