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Abstract Inferring the 3D surface shape of a known template from 2D images captured by a monocular camera is a challenging problem. Due to the severely underconstrained nature of the problem, inferring shape accurately becomes particularly challenging when the template exhibits high curvature, resulting in the disappearance of feature points and significant differences between the inferred and actual deformations. To address this problem, this paper proposes a concise and innovative approach that utilizes a physical simulator incorporating the object’s material properties and deformation law. We utilize a view frustum space constructed from the contours of a monocular camera image to effectively restrict the physically-based free motion of the template. Additionally, we employ mesh denoising techniques to ensure the smoothness of the surface following deformation. To evaluate our shape inference results, we utilize a ground truth 3D point cloud generated from multiple viewpoint images. The results demonstrate the superior performance of our approach compared to other methods in accurately inferring deformations, particularly in scenarios where feature points are unobservable. This method carries significant practical implications across diverse domains, including virtual reality, digital modeling, and medical surgery training.

Effectively monitoring pest-infested areas by computer vision is essential in precision agriculture in order to minimize yield losses and create early scientific preventative solutions. However, the scale variation, complex background, and dense distribution of pests bring challenges to accurate detection when utilizing vision technology. Simultaneously, supervised learning-based object detection heavily depends on abundant labeled data, which poses practical difficulties. To overcome these obstacles, in this paper, we put forward innovative semi-supervised pest detection, PestTeacher. The framework effectively mitigates the issues of confirmation bias and instability among detection results across different iterations. To address the issue of leakage caused by the weak features of pests, we propose the Spatial-aware Multi-Resolution Feature Extraction (SMFE) module. Furthermore, we introduce a Region Proposal Network (RPN) module with a cascading architecture. This module is specifically designed to generate higher-quality anchors, which are crucial for accurate object detection. We evaluated the performance of our method on two datasets: the corn borer dataset and the Pest24 dataset. The corn borer dataset encompasses data from various corn growth cycles, while the Pest24 dataset is a large-scale, multi-pest image dataset consisting of 24 classes and 25k images. Experimental results demonstrate that the enhanced model achieves approximately 80% effectiveness with only 20% of the training set supervised in both the corn borer dataset and Pest24 dataset. Compared to the baseline model SoftTeacher, our model improves mAP@0.5 (mean Average Precision) at 7.3 compared to that of SoftTeacher at 4.6. This method offers theoretical research and technical references for automated pest identification and management.

The natural gas containing hydrogen sulfide in China has great exploration potential, and the origin, source and formation mechanism of hydrogen sulfide are of concern to petroleum workers. Hydrogen sulfide content and sulfur isotope analysis are two common and important identification indicators. Due to strong toxicity of hydrogen sulfide, many laboratories in China conduct fewer tests or cancel such testing items. In this paper, hydrogen sulfide conversion reagent is preferring selected in pretreatment process. At the same time, through improving isotope mass spectrometer supporting equipment, i.e. tuning the parameters of ion beam focusing of sulfur isotope Faraday cup, installing special chromatography column, Teflon pipeline, sulfur reaction tube, etc., the technology platform for combined measurement of hydrogen sulfide content and sulfur isotope is constructed. Secondly, the high purity sulfur dioxide standard gas is replaced with working standard gas of low concentration, low pressure and small volume, and the optimal experimental conditions are determined through system condition experimental exploration (stability, standard substance analysis, etc.). Finally, the solid precipitate generated by the reaction was sent into the mass spectrometer for the detection of sulfur isotope composition information, and the concentration of hydrogen sulfide in the gas to be measured is calculated by comparing signal with the isotopic composition of standard pure sulfur-containing substances. By using this method, the hydrogen sulfide content and sulfur isotope in some natural gas of Daniudi and Fuxian gas fields in Ordos Basin, and Hongxing area in western Hubei and eastern Chongqing are measured. The measured results are stable with good precision, and are consistent with the results of external laboratories. Comparing traditional method, this method can obtain sulfur content and sulfur isotope values by a single sampling. The preferred silver acetate reagent with one-step chemical method can reduce isotopic fractionation. In addition, the hydrogen sulfide inverting into solid silver sulfide are more reliable directly compared with the standard substance silver sulfide isotope. With low concentration, low pressure, small volume SO2 as the working standard gas, it reduces the laboratory safety risk and instrument damage, and meets the environmental safety requirements.

Abstract Effective compression of point clouds is essential for implementing virtual and mixed reality applications, which require encoding millions or even tens of millions of points. This paper offers a new geometric compression for point clouds based on sparse cascaded residuals and sparse attention. A sparse cascaded residual module is posited to connect multiple residual modules through shortcuts, thereby augmenting the network's learning capacity and compression efficacy. The authors developed a sparse attention module to acquire global features by computing interdependencies among points, enhancing compression performance to a greater extent. Trade‐off parameters are employed to optimize the rate and distortion. The authors’ method outperforms the state‐of‐the‐art open‐source method regarding rate‐distortion on the ShapeNet, ModelNet, and Microsoft Voxelized Upper Bodies datasets, with average bjøntegaard‐delta (BD)‐rate gains of −14.44% and −15.38%.

Abstract 360° depth estimation has been extensively studied because 360° images provide a full field of view of the surrounding environment as well as a detailed description of the entire scene. However, most well‐studied convolutional neural networks (CNNs) for 360° depth estimation can extract local features well, but fail to capture rich global features from the panorama due to a fixed receptive field in CNNs. PCformer, a parallel convolutional transformer network that combines the benefits of CNNs and transformers, is proposed for 360° depth estimation. The transformer has the nature to model long‐range dependency and extract global features. With PCformer, both global dependency and local spatial features can be efficiently captured. To fully incorporate global and local features, a dual attention fusion module is designed. Besides, a distortion‐weighted loss function is designed to reduce the distortion in panoramas. Extensive experiments demonstrate that the proposed method achieves competitive results against the state‐of‐the‐art methods on three benchmark datasets. Additional experiments also demonstrate that the proposed model has benefits in terms of model complexity and generalisation capability.

In recent years, remote sensing analysis has gained significant attention in visual analysis applications, particularly in segmenting and recognizing remote sensing images. However, the existing research has predominantly focused on single-period RGB image analysis, thus overlooking the complexities of remote sensing image capture, especially in highly vegetated land parcels. In this paper, we provide a large-scale vegetation remote sensing (VRS) dataset and introduce the VRS-Seg task for multi-modal and multi-temporal vegetation segmentation. The VRS dataset incorporates diverse modalities and temporal variations, and its annotations are organized using the Vegetation Knowledge Graph (VKG), thereby providing detailed object attribute information. To address the VRS-Seg task, we introduce VRSFormer, a critical pipeline that integrates multi-temporal and multi-modal data fusion, geometric contour refinement, and category-level classification inference. The experimental results demonstrate the effectiveness and generalization capability of our approach. The availability of VRS and the VRS-Seg task paves the way for further research in multi-modal and multi-temporal vegetation segmentation in remote sensing imagery.

For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a self-adapted model is a challenging problem. To address this issue, an efficient deep learning framework that uses a derived extreme learning machine (ELM) has been put forward. Different from typical neural network methods, the provided analytical machine learning model can match the different OAM modes automatically. In the model selection phase, a multilayer ELM is adopted to quantify the laser spot characteristics. In the parameter optimization phase, a fast iterative shrinkage-thresholding algorithm makes the model present the analytic expression. After the feature extraction of the received intensity distributions, the proposed method develops a relationship between laser spot and OAM mode, thus building the steady neural network architecture for the new received vortex beam. The whole recognition process avoids the trial and error caused by user intervention, which makes the model suitable for a time-varying atmospheric environment. Numerical simulations are conducted on different experimental datasets. The results demonstrate that the proposed method has a better capacity for OAM recognition.

Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCGC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size.

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.

The real-time rendering of large-scale terrain scenes has been a challenge and is a popular research topic in the fields of 3D games and virtual reality. To fully utilize GPU features, this paper proposes a real-time rendering algorithm for large-scale regular grid terrain based on GPU Tessellation. The CPU processes a coarse-grained quadtree to generate terrain patches and then passes the patches to the GPU after view culling. Then, the GPU refines the terrain by using GPU Tessellation and displacement mapping. The most important advantage of our method is that during the tessellation stage, a new rule that the distance, screen space projection error and variance of the height, as the standard of terrain roughness, is designed. During the tessellation stage, the distance, screen space projection error and variance of the height, as the standard of terrain roughness, are considered. The experimental results show that our algorithm can greatly reduce the CPU processing time and main memory space requirement, and fully utilize the new features of the GPU; In conclusion, the proposed approach can render large-scale terrain with high-precision in real-time.

This paper aims to create a resource-saving method for the routing problem in space information network. To this end, a multi-objective routing algorithm was created based on game theory for space information network. Specifically, the auction game was introduced to solve the routing problem using the delay-tolerating network (DTN) protocol. Considering the topological periodicity of low earth orbit (LEO) satellite network, a typical space information network, the dynamic topological structure was divided into relatively static time slots. Then, the routing problem was solved through the auction game in these slots. The proposed algorithm can minimize the number of selfish nodes in the network and avoid network congestion resulted from excessive resource consumption of individual nodes. Finally, the proposed algorithm was compared with other well-known routing models like the epidemic routing model (Epidemic) and the first contact routing model (FC). The results show that the proposed algorithm outperformed the contrastive models in both average delay and network overhead ratio. The research findings shed important new light on the routing of space information network.

The isotope monitoring of desorption gas from shale cores was carried out at the drill site. Gas is lost during core recovery and thus the isotope variation for the whole process of gas release from shale cores cannot be obtained. A high pressure methane saturation-desorption device was developed and connected to the chromatographic-isotope mass spectrometry system to monitor the carbon isotope change of released CH4 on-line. The isotopic curves of releaed methane were initially stable, and then tended to become lighter and finally heavier, which was related to the phase transformation and the mixing ratio of free gas and adsorbed gas. Isotopes have potential applications in monitoring shale gas development.

Network anomaly detection aims to identify network anomalies, and it has obtained many achievements using the supervised classification technique. Since the supervised classifier depends on the prior data, it is difficult to accurately classify the rare anomalies when they account less in the training set. Data augmentation can tackle the imbalanced training set problem through creating artificial rare anomaly samples. However, the existing data augmentation methods either ignore the data distribution or ignore the spatial knowledge between features. Therefore, this article addresses this issue by proposing a Network Anomaly Detection Scheme based on feature Representation and data Augmentation (NADS-RA). Re-circulation Pixel Permutation strategy is first designed as feature representation strategy to construct images, and it rotates each feature left by the times of feature number to maintain the spatial knowledge between original network traffic features. An image-based augmentation strategy is thus designed to produce augmented images according to the distribution characteristics of rare network anomaly images with the help of Least Squares Generative Adversarial Network, which alleviates the effect of imbalanced training set and avoids over-fitting. After that, NADS-RA is implemented on the Convolutional Neural Network classification model. We conduct experiments on five public benchmark datasets, including NSL-KDD and UNSW-NB15, and so on, and compare against 12 detection methods and 17 data generation methods. The experimental results demonstrate the superior effectiveness of our work to state-of-the-art methods and the general applicability in different scenarios.

Reservoirs in the dolomites of the Middle Permian Qixia Formation in the Sichuan Basin are currently important oil and gas exploration objects in China. However, the questions concerning the sources of the dolomitized fluids and the control factors of the Qixia hydrothermal dolomites remain unclear. In this study, the original hydrothermal dolomites (the replacement dolomites (RDs) and saddle dolomites (SDs)) from the Qixia Formation in the southwestern Sichuan Basin (the PR1 well and Baoxing section) were mainly examined using novel in situ carbonate U-Pb dating with clumped isotopes (∆47). Our results show that the U-Pb ages of the latest SDs from the PR1 well (located in the middle zone of the Emeishan large igneous province (ELIP) and distanced from the Orogenic Belt of Longmenshan) are 257.9–251.0 Ma, coincident with the period of main activity of the ELIP. Combined with the previous U-Pb dating, we propose that the high-temperature T∆47 (82.2–108.4 °C and 127.5–205.9 °C) recorded for SDs from the PR1 well and Baoxing section may have responded to ELIP activity and Longmenshan orogeny activity, respectively. In addition, in the entire southwestern Sichuan Basin, the RDs and SDs yield similar δ13C and δ18O values, indicating that the dolomites were formed by hydrothermal fluids of similar sources, with marine hydrothermal fluids being a highly possible source. Finally, this study proposes a new hydrothermal dolomite genesis model for the Qixia Formation, emphasizing that the formation of hydrothermal dolomites mainly depends on the proximity to tectonic thermal events in space and time.

Mutually complementary bases are crucial to secure quantum key distribution (QKD). By aiming at free-space QKD with use of spatially structured photons, the effect of turbulence on the mutual unbiasedness of two complementary bases relevant to photonic orbital-angular-momentum (OAM) modes is modeled theoretically, with novel insightful expressions and physical explanations yielded. For two complementary bases constructed from Laguerre-Gaussian (LG) modes with a fixed radial index of zero, it is shown that the superposed joint-two-LG-mode (JTLGM) correlation function plays a fundamental role in determining the turbulence-induced mutual-unbiasedness decay. The mutual information between the sent and detected photonic states is used as a metric to quantify the degree of turbulence-induced mutual-unbiasedness decay. It is found that the degree of turbulence-induced mutual-unbiasedness decay depends on the scaled atmospheric coherence width, anisotropy of turbulence and contents of the complementary bases, and it can become non-negligible in certain cases.

Abstract Background Imbalanced data classification is an inevitable problem in medical intelligent diagnosis. Most of real-world biomedical datasets are usually along with limited samples and high-dimensional feature. This seriously affects the classification performance of the model and causes erroneous guidance for the diagnosis of diseases. Exploring an effective classification method for imbalanced and limited biomedical dataset is a challenging task. Methods In this paper, we propose a novel multilayer extreme learning machine (ELM) classification model combined with dynamic generative adversarial net (GAN) to tackle limited and imbalanced biomedical data. Firstly, principal component analysis is utilized to remove irrelevant and redundant features. Meanwhile, more meaningful pathological features are extracted. After that, dynamic GAN is designed to generate the realistic-looking minority class samples, thereby balancing the class distribution and avoiding overfitting effectively. Finally, a self-adaptive multilayer ELM is proposed to classify the balanced dataset. The analytic expression for the numbers of hidden layer and node is determined by quantitatively establishing the relationship between the change of imbalance ratio and the hyper-parameters of the model. Reducing interactive parameters adjustment makes the classification model more robust. Results To evaluate the classification performance of the proposed method, numerical experiments are conducted on four real-world biomedical datasets. The proposed method can generate authentic minority class samples and self-adaptively select the optimal parameters of learning model. By comparing with W-ELM, SMOTE-ELM, and H-ELM methods, the quantitative experimental results demonstrate that our method can achieve better classification performance and higher computational efficiency in terms of ROC, AUC, G-mean, and F-measure metrics. Conclusions Our study provides an effective solution for imbalanced biomedical data classification under the condition of limited samples and high-dimensional feature. The proposed method could offer a theoretical basis for computer-aided diagnosis. It has the potential to be applied in biomedical clinical practice.

The identification of students with financial difficulties is one of the main problems in campus data research. Effective and timely identification not only provides convenience to campus administrators but also helps students who are really in financial hardship. The popular using of smart cards makes it possible to identify students with financial difficulties through big data. In this paper, we collect behavioural records from undergraduate students’ smart cards and propose five features by which to associate with students’ poverty level. Based on these features, we proposed the Apriori Balanced Algorithm (ABA) to mine the relationship of poverty level with students’ daily behaviour. Association rules show that students’ poverty level is most closely related to their academic performance, followed by consumption level, diligence level, and life regularity. Finally, we adopted the semisupervised K-means algorithm to more accurately find out students with financial difficulties. Tested by classical classification algorithms, our method has a higher identification rate, which is helpful for university administrators discover students in real financial hardship effectively.

Both symmetric and asymmetric color image encryption have advantages and disadvantages. In order to combine their advantages and try to overcome their disadvantages, chaos synchronization is used to avoid the key transmission for the proposed semi-symmetric image encryption scheme. Our scheme is a hybrid chaotic encryption algorithm, and it consists of a scrambling stage and a diffusion stage. The control law and the update rule of function projective synchronization between the 3-cell quantum cellular neural networks (QCNN) response system and the 6th-order cellular neural network (CNN) drive system are formulated. Since the function projective synchronization is used to synchronize the response system and drive system, Alice and Bob got the key by two different chaotic systems independently and avoid the key transmission by some extra security links, which prevents security key leakage during the transmission. Both numerical simulations and security analyses such as information entropy analysis, differential attack are conducted to verify the feasibility, security, and efficiency of the proposed scheme.

Utilising the properties of quantum mechanics, i.e., entanglement, parallelism, etc., a quantum structure is proposed for representing and manipulating emotion space of robots. This quantum emotion space (QES) provides a mechanism to extend emotion interpretation to the quantum computing domain whereby fewer resources are required and, by using unitary transformations, it facilitates easier tracking of emotion transitions over different intervals in the emotion space. The QES is designed as an intuitive and graphical visualisation of the emotion state as a curve in a cuboid, so that an “emotion sensor” could be used to track the emotion transition as well as its manipulation. This ability to use transition matrices to convey manipulation of emotions suggests the feasibility and effectiveness of the proposed approach. Our study is primarily influenced by two developments. First, the massive amounts of data, complexity of control, planning and reasoning required for today’s sophisticated automation processes necessitates the need to equip robots with powerful sensors to enable them adapt and operate in all kinds of environments. Second, the renewed impetus and inevitable transition to the quantum computing paradigm suggests that quantum robots will have a role to play in future data processing and human-robot interaction either as standalone units or as part of larger hybrid systems. The QES proposed in this study provides a quantum mechanical formulation for quantum emotion as well as a platform to process, track, and manipulate instantaneous transitions in a robot’s emotion. The new perspective will open broad areas, such as applications in emotion recognition and emotional intelligence for quantum robots.