The Thinprep Cytologic Test (TCT) is a vital component in the early detection of cervical cancer. However, conventional manual screening methods are hindered by inefficiencies and high levels of subjectivity. This study presents YOLO-TCT, an enhanced YOLOv9 network designed for the automated detection of abnormal cervical cells in TCT smears. Our model features a novel detection head with an adaptive weighting mechanism and Hard Polarized Self Attention (HPSA) to improve the differentiation of subtle cell characteristics. Additionally, we enhance the network’s ability to distinguish between foreground and background by incorporating a detection box quality factor into the classification loss function, and we introduce class parameters into the quality focal loss to address the class imbalance. Experimental evaluations on the CQTCT dataset reveal that YOLO-TCT achieves a mean Average Precision at IoU=0.5 (AP50) of 26.37% and a mean Average Recall (mAR) of 46.10%, with a significant improvement in recall rates for tail classes by over 26%. Notably, our model operates approximately five times faster than traditional R-CNN models while achieving superior precision and recall rates. These results underscore the effectiveness of our approach for real-time cervical cell detection in the context of long-tail distribution. The source code is accessible at https://github.com/threedteam/YOLO-TCT.

Time series anomaly detection is one kind of critical time series analytical tasks, which is widely applied to various real-world applications. Recently, the diffusion models have shown promising performance on time series imputation for anomaly detection. And the high computing requirements of the diffusion models naturally extend their computation paradigm from the centralized computing to the cloud computing, and then further to JointCloud computing which allows the diffusion models to be deployed across multiple clouds. However, the JointCloud computing paradigm faces the challenge of task offloading over multiple nodes across multiple clouds. Unfortunately, most of existing task offloading methods overlook the dynamic nature of network conditions among clouds. To address this issue, we propose a time series anomaly detection approach named AdaDiffAD by adaptively segmenting diffusion models in JointCloud environment with dynamic network conditions. Specifically, we design a task offloading strategy by segmenting the denoising process of the diffusion model onto both the edge clouds and central clouds, and utilizing the edge cloud results directly for anomaly detection when the network condition is not ideal. By conducting comprehensive experiments on seven datasets, the experimental results demonstrate that our proposed AdaDiffAD always achieves lower time consumption while maintaining competitive anomaly detection performance compared to the state-of-the art without adaptive task offloading strategy.

Cervical cell detection, a crucial component in Thin-prep Cytologic Test (TCT) screening for early cervical cancer diagnosis, faces challenges due to the small size of abnormal cells. To address this issue, we propose a novel model, the TCT Small Object DEtection TRansformer (TSO-DETR). TSO-DETR incorporates two key techniques to improve detection performance. Firstly, a multi-scale approach is employed by integrating PVTv2 and Deformable DETR. This approach enhances the feature representation of small cervical cells by combining shallow and deep features from different scales. Secondly, a novel approach is utilized by combining Spatial Reconstruction Unit (SRU) with our proposed Mixture Channel Attention (MixCA). This combination enhances the feature extraction ability of the model from both spatial and channel dimensions. Notably, the introduction of MixCA enables the model to prioritize important image features, thereby reducing interference from background noise. Comparative experimental results demonstrate TSO-DETR’s superiority over other state-of-the-art methods in our TCT dataset, achieving 24.8% in mean Average Precision (mAP) and 50.5% in mean Average Recall (mAR). These results reflect improvements of 8.0 percentage points in mAP and 10.6 percentage points in mAR, respectively, over the second-best model. Consequently, TSO-DETR can serve as an additional valuable method for cervical cell detection. The source code is available at https://github.com/threedteam/TSO-DETR/.

The Depth-Image-Based Rendering (DIBR) algorithm is pivotal in the advancement of Virtual Reality (VR) and Augmented Reality (AR) technologies due to its capacity to generate virtual views from arbitrary perspectives. Nonetheless, the generation process is often marred by the occurrence of holes due to sharp depth transitions, significantly degrading the quality of the synthesized view. To mitigate this issue, this study introduces a layered hole-filling method to enhance the quality of virtual views. The effectiveness of our proposed method is ensured through three key techniques: Firstly, a depth-aware decomposition method is employed to precisely segregate foreground objects from the background within a reference view. This is achieved by leveraging both the reference image and its corresponding depth map, facilitating accurate instance-level separation of foreground objects. Secondly, a Generative Adversarial Network (GAN)-enhanced background reconstruction method is proposed to generate hole-free target views devoid of foreground objects. Lastly, the integration of Masked 3D Image Warping (M3DIW) and Layered Mergence (LM) algorithms facilitates filling holes with foreground or background textures in a layered manner. Comprehensive experimental results demonstrate the superiority of our proposed method compared to state-of-the-art methods. Notably, our method demonstrates an improvement of 7.5% in mean average Peak Signal-to-Noise Ratio (PSNR) and 1.8% in mean average Structural Similarity Index Measure (SSIM) compared to existing techniques. Additionally, it impressively lowers mean average Learned Perceptual Image Patch Similarity (LPIPS) by 28.8% and significantly reduces mean average Fréchet Inception Distance (FID) by 28.9% for all sequences tested. These results affirm the effectiveness of our approach in enhancing the quality of virtual view synthesis within DIBR applications. Source code is available athttps://github.com/threedteam/dibr.

Federated learning (FL) is an effective paradigm for training a machine-learning model based on data distributed at a large quantity of users in Internet of Things (IoT) without sharing their raw data. However, federated optimization of the global model in heterogeneous IoT—while considering the heterogeneity among users and limited network constraints—remains to be an open challenge. In this article, we propose a novel adaptive federated optimization algorithm, Adp-FedProx, to achieve the optimal learning performance within the limited computation and communication resources at the edge. In particular, we analyze how the training loss is affected by each user’s global update frequency and the time and energy used for learning by obtaining the novel convergence bound of federated training loss in heterogeneous IoT. With our proposed algorithm, all users can dynamically adjust their number of local iterations in each global interval and will not drop out during the training process for resource exhaustion, so as to impair the negative effect of heterogeneity among users and guarantee the convergence of the training model. In addition, we can get the optimal learning performance by minimizing the gap between the final loss function and the optimal one within limited resources. Finally, extensive numerical results demonstrate the algorithmic advantages in adapting system heterogeneity and admirable performance of the proposed methodologies in speeding up FL 5%–10% and reduce the energy consumption in training about 10% compared with FedProx.

A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a “stacking with ridge regression” strategy is applied to the fusion block to get the final output of MFBLS. Experimental results show that MFBLS achieves the accuracies of 92.25%, 81.03%, and 54.66% on SVHN, CIFAR-10, and CIFAR-100, respectively, which outperforms BLS and its variants. Besides, it is even superior to the deep network, convolutional deep belief network, in both accuracy and training time on CIFAR-10. Code for the paper is available at https://github.com/threedteam/mfbls .

Mobile-edge computing (MEC) is considered as a promising technology in 5G, as it can solve the contradiction between the explosive growth of computation-intensive tasks and the limited computation power and battery life of local devices. However, in the 5G environment, most of the existing studies on task offloading in MEC have either failed to study the compatible multiple access technologies or have not considered the hierarchical relationship between small cell base station (SBS) and macro base station (MBS). Therefore, to explore the MEC offloading problem under the unique 5G architecture is of great significance at present. In light of this, we study the task offloading strategy in the nonorthogonal multiple access (NOMA)-enabled small cell MEC network. Specifically, we first describe a noval small cell MEC architecture in which MBS and SBS are both deployed with edge servers and there is a hierarchical relationship between the two. Based on this architecture, we have established the communication model and computation model, respectively. Then, we formulate the energy and delay weighted sum minimization problem, which aims at minimizing the total cost of task offloading under different requirements and takes into account the constraints of computation capabilities. To solve the problem, we develop a hybrid genetic hill climbing (HGHC) algorithm that can quickly find the optimal solution. Moreover, we perform a lot of simulation experiments to evaluate the performance of our algorithm under different parameters. The experimental results show that our algorithm can converge within about 20 iterations, which is superior to traditional heuristic algorithms.

The expansion complexity is a new figure of merit for cryptographic sequences. In this paper, we present an explicit formula of the (irreducible) expansion complexity of ultimately periodic sequences over finite fields. We also provide improved upper and lower bounds on the$N$th irreducible expansion complexity when they are not explicitly determined. In addition, for some infinite sequences with given nonlinear complexity, a tighter upper bound of their$N$th expansion complexity is given.

This article presents a geometric algebra-based model for topological relation computation. This computational model is composed of three major components: the Grassmann structure preserving hierarchical multivector-tree representation (MVTree), multidimensional unified operators for intersection relation computation, and the judgement rules for assembling the intersections into topological relations. With this model, the intersection relations between the different dimensional objects (nodes at different levels) are computed using the Tree Meet operator. The meet operation between two arbitrary objects is accomplished by transforming the computation into the meet product between each pair of MVTree nodes, which produces a series of intersection relations in the form of MVTree. This intersection tree is then processed through a set of judgement rules to determine the topological relations between two objects in the hierarchy. Case studies of topological relations between two triangles in 3D space are employed to illustrate the model. The results show that with the new model, the topological relations can be computed in a simple way without referring to dimension. This dimensionless way of computing topological relations from geographic data is significant given the increased dimensionality of geographic information in the digital era.

The fatigue test for LY12 aluminum alloys specimens at various stresses and stress ratios were performed. The distributions of the crack-nucleating particle sizes and ratios were determined using scanning electron microscopy (SEM) measured from specimens fracture surfaces. The results show that the correlation between particles sizes and ratios versus fatigue lives. And with the increase of stress ratio, fatigue crack originated from deeper subsurface at the same maximal stress. With the increase of maximal stress, fatigue crack originated from shallower subsurface or surface at same stress ratio. It has been also observed that the incubated fatigue micro cracks source localizes at the triple junction adjacent particle boundaries. The calculated results show that microcracks could nucleate and propagate from a smaller particle under a higher stress ratio, which agree reasonably with experimental observation.