Complementarity Of Information Research Articles

A general image fusion framework using multi-task semi-supervised learning

Existing image fusion methods primarily focus on solving single-task fusion problems, overlooking the potential information complementarity among multiple fusion tasks. Additionally, there has been no prior research in the field of image fusion that explores the mixed training of labeled and unlabeled data for different fusion tasks. To address these gaps, this paper introduces a novel multi-task semi-supervised learning approach to construct a general image fusion framework. This framework not only facilitates collaborative training for multiple fusion tasks, thereby achieving effective information complementarity among datasets from different fusion tasks, but also promotes the (unsupervised) learning of unlabeled data via the (supervised) learning of labeled data. Regarding the specific network module, we propose a so-called pseudo-siamese Laplacian pyramid transformer (PSLPT), which can effectively distinguish information at different frequencies in source images and discriminatively fuse features from distinct frequencies. More specifically, we take datasets of four typical image fusion tasks into the same PSLPT for weight updates, yielding the final general fusion model. Extensive experiments demonstrate that the obtained general fusion model exhibits promising outcomes for all four image fusion tasks, both visually and quantitatively. Moreover, comprehensive ablation and discussion experiments corroborate the effectiveness of the proposed method. The code is available at https://github.com/wwhappylife/A-general-image-fusion-framework-using-multi-task-semi-supervised-learning.

Dynamic graph transformer network via dual-view connectivity for autism spectrum disorder identification

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that requires objective and accurate identification methods for effective early intervention. Previous population-based methods via functional connectivity (FC) analysis ignore the differences between positive and negative FCs, which provide the potential information complementarity. And they also require additional information to construct a pre-defined graph. Meanwhile, two challenging demand attentions are the imbalance of performance caused by the class distribution and the inherent heterogeneity of multi-site data. In this paper, we propose a novel dynamic graph Transformer network based on dual-view connectivity for ASD Identification. It is based on the Autoencoders, which regard the input feature as individual feature and without any inductive bias. First, a dual-view feature extractor is designed to extract individual and complementary information from positive and negative connectivity. Then Graph Transformer network is innovated with a hot plugging K-Nearest Neighbor (KNN) algorithm module which constructs a dynamic population graph without any additional information. Additionally, we introduce the PolyLoss function and the Vrex method to address the class imbalance and improve the model's generalizability. The evaluation experiment on 1102 subjects from the ABIDE I dataset demonstrates our method can achieve superior performance over several state-of-the-art methods and satisfying generalizability for ASD identification.

Species Abundance Modelling of Arctic-Boreal Zone Ducks Informed by Satellite Remote Sensing

The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape is challenging, in part due to extent and data scarcity. Species abundance models for ducks in the ABZ commonly use static (time invariant) habitat covariates to inform predictions, such as wetland type and extent maps. For the first time in this region, we developed species abundance models using high-resolution, time-varying wetland inundation data produced using satellite remote sensing methods. This data captured metrics of surface water extent and inundated vegetation in the Peace Athabasca Delta, Canada, which is within the NASA Arctic Boreal Vulnerability Experiment core domain. We used generalized additive mixed models to demonstrate the improved predictive value of this novel data set over time-invariant data. Our findings highlight both the potential complementarity and efficacy of dynamic wetland inundation information for improving estimation of duck abundance and distribution at high latitudes. Further, these data can be an asset to spatial targeting of biodiversity conservation efforts and developing model-based metrics of their success under rapidly changing climatic conditions.

Multi-input mutual supervision network for single-pixel computational imaging

In this study, we propose a single-pixel computational imaging method based on a multi-input mutual supervision network (MIMSN). We input one-dimensional (1D) light intensity signals and two-dimensional (2D) random image signal into MIMSN, enabling the network to learn the correlation between the two signals and achieve information complementarity. The 2D signal provides spatial information to the reconstruction process, reducing the uncertainty of the reconstructed image. The mutual supervision of the reconstruction results for these two signals brings the reconstruction objective closer to the ground truth image. The 2D images generated by the MIMSN can be used as inputs for subsequent iterations, continuously merging prior information to ensure high-quality imaging at low sampling rates. The reconstruction network does not require pretraining, and 1D signals collected by a single-pixel detector serve as labels for the network, enabling high-quality image reconstruction in unfamiliar environments. Especially in scattering environments, it holds significant potential for applications.

A high-precision fusion bathymetry of multi-channel waveform curvature for bathymetric LiDAR systems

Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and perform bathymetry at various water depths. However, in echo waveform processing, detailed information about the water body described by the different echo waveforms is lacking, making it difficult to overcome severe waveform superposition in extremely shallow areas or to separate the weak echo of the water bottom from the noise in extremely deep areas. Therefore, we employed a novel method of multi-channel waveform fusion bathymetry (MWFB) to achieve information complementarity, gain multi-channel waveforms, and detect the robustness of water and bottom echo signals. This method uses an adaptive signal waveform extraction of a multichannel fusion mechanism to eliminate systematic and random noise, and the waveform curvature is used to reconstruct the energy–waveform curves. Iterative decomposition based on waveform curvature and energy curves was conducted to improve the correctness of waveform decomposition and the reliability of waveform components. Furthermore, the water surface and bottom peaks were detected based on the multichannel waveform curvature, which enabled acquisition of the accurate temporal positions of the peaks and achieve high-accuracy bathymetry. According to the experimental results and a comparison with reference datasets, the highest bathymetric accuracy of the MWFB method reached an RMSE of 0.22. For the different study areas, the bathymetric accuracy of the slope reached 95%, 92%, and 97% at Ganquan Island, Lingyang Reef, and Bei Island, respectively. Furthermore, the bathymetric point numbers were effectively increased and bathymetric accuracy was also improved in shallow and deep water, which illustrates the superior bathymetric performance of the MWFB method and its ability to provide a high-efficiency waveform dataset through multi-channel data fusion. The novel LiDAR bathymetric method proposed in this study can effectively achieve high-accuracy nearshore bathymetry and seafloor topographical mapping.

Development of a novel machine learning-based approach for brain function assessment and integrated software solution

Industry 5.0 emphasizes human-centered solutions. However, bridging the gap between human conditions and engineering systems remains a challenge in this era. Promoting the development of cybernetics applications in the field of rehabilitation is imperative. In the healthcare sector, the evaluation of rehabilitation products’ ergonomics and the dynamic assessment of the human body are of utmost importance. Amid the burgeoning prominence of data-driven product design concepts, ergonomics methods have emerged as indispensable evaluative tools. Nevertheless, the integration of data from multiple sources remains a contemporary challenge; one must extract meaningful information from diverse data sets while enhancing data analysis and fusion techniques. In this study, we collect multimodal neurobiological signals using electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to devise a robust multimodal rehabilitation data fusion method based on graph convolutional network (GCN), particularly focusing on the motor imagery (MI) task. To achieve a more holistic evaluation, we incorporate granger causality (GC) and brain region adjacency matrix as supplementary features, integrating electrophysiological and hemodynamic perspectives. This integration aims to enhance information complementarity and foster a comprehensive understanding of the neural processes involved in MI. Our results demonstrate the superiority of the multimodal fusion method: it achieves higher average accuracy and improved stability, as indicated by the reduced standard deviation of accuracy. This enhanced performance suggests its potential regarding broader research applications. Building upon our successful findings, we establish an intelligent algorithmic rehabilitation platform based on multimodal neural data. This platform not only holds significant clinical value, facilitating precision and personalized medicine, but also propels the advancement of medical practices, paving the way for a more tailored and effective approach to patient care. Our research makes progresses in the areas of rehabilitation modeling, equipment design, intelligence, and interdisciplinary collaboration through the in-depth study and extension of cybernetic principles and techniques.

Autonomous Driving System Driven by Artificial Intelligence Perception Fusion

Perception, as the information input module of the automatic driving system, determines the lower limit of the entire automatic driving system. Both autonomous driving perception and robot perception are constantly approaching the real physical world through digital methods, and this real physical world is only limited to the scope of human perception, such as lane lines, traffic lights, driving obstacles, and so on. The main premise of this process is that humans already know the categories or properties of the physical world, and only allow machines and systems to replicate human responses. Whether it is a pure visual route or a multi-source fusion route, the essence is the difference between the perceptual system schemes, one focusing on the vertical and the other on the horizontal. The pure vision solution represented by Tesla or the multi-source sensor fusion file represented by Waymo. In fact, the perception module of the automatic driving system usually has multiple sensors to achieve information redundancy and information complementarity through multiple dimensions, but there is the possibility of information conflict between different sensors. This paper aims at the advantages of perception-driven artificial intelligence to achieve breakthroughs in autonomous driving innovation, and analyzes how perception fusion drive is applied to the practical application of autonomous driving, so as to analyze the future development prospects of artificial intelligence.

Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

Semantic segmentation of human images is a research hotspot in the field of computer vision. At present, the semantic segmentation models based on U-net generally lack the ability to capture the spatial information of images. At the same time, semantic incompatibility exists because the feature maps of encoder and decoder are directly connected in the skip connection stage. In addition, in low light scenes such as at night, it is easy for false segmentation and segmentation accuracy to appear. To solve the above problems, a portrait semantic segmentation method based on dual-modal information complementarity is proposed. The encoder adopts a double branch structure, and uses a SK-ASSP module that can adaptively adjust the convolution weights of different receptor fields to extract features in RGB and gray image modes respectively, and carries out cross-modal information complementarity and feature fusion. A hybrid attention mechanism is used in the jump connection phase to capture both the channel and coordinate context information of the image. Experiments on human matting dataset show that the PA and MIoU coefficients of this algorithm model reach 96.58% and 94.48% respectively, which is better than U-net benchmark model and other mainstream semantic segmentation models.

The Impact of M&A Delistings on the Information Environment of Industry Peer Firms

ABSTRACT This study documents that M&A delistings are associated with a deterioration in the quality of analysts’ information environment for industry peer firms, measured by an increase in analysts’ absolute forecast errors and dispersion. This effect persists for six quarters and is larger when the delisting target firm contributes relatively more to the industry information environment. Further, we find that, among analysts forecasting earnings for an industry peer firm, those who also followed the delisted target firm in the pre-M&A period experience a larger increase in their absolute forecast errors. A comparison based on public versus private target firms also suggests that the loss of target firms’ public disclosures plays a role in the deterioration in the quality of analysts’ information environment. In additional analyses, we find evidence consistent with this effect resulting from a deterioration in analysts’ ability to exploit across-firm information complementarities. JEL Classifications: G14; G18; G34; M40.

DBNet-SI: Dual branch network of shift window attention and inception structure for skin lesion segmentation

The U-shaped convolutional neural network (CNN) has attained remarkable achievements in the segmentation of skin lesion. However, given the inherent locality of convolution, this architecture cannot capture long-range pixel dependencies and multiscale global contextual information effectively. Moreover, repeated convolutions and downsampling operations can readily result in the omission of intricate local fine-grained details. In this paper, we proposed a U-shaped network (DBNet-SI) equipped with a dual-branch module that combines shift window attention and inception structures. First, we proposed a dual-branch module that combines shift window attention and inception structures (MSI) to better capture multiscale global contextual information and long-range pixel dependencies. Specifically, we have devised a cross-branch bidirectional interaction module within the MSI module to enable information complementarity between the two branches in the channel and spatial dimensions. Therefore, MSI is capable of extracting distinguishing and comprehensive features to accurately identify the skin lesion boundaries. Second, we have devised a progressive feature enhancement and information compensation module (PFEIC), which progressively compensates for fine-grained features through reconstructed skip connections and integrated global context attention modules. The results of the experiment show the superior segmentation performance of DBNet-SI compared with other deep learning models for skin lesion segmentation in the ISIC2017 and ISIC2018 datasets. Ablation studies demonstrate that our model can effectively extract rich multiscale global contextual information and compensate for the loss of local details.

Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study.

Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records. Does free text increase informational value in comparison with structured data? This study aims to compare information from unstructured text-based chief complaints harvested and processed by a natural language processing (NLP) algorithm with clinician-entered structured diagnoses in terms of their potential utility for automated improvement of patient workflows. Electronic health records of 293,298 patient visits at the emergency department of a Swiss university hospital from January 2014 to October 2021 were analyzed. Using emergency department overcrowding as a case in point, we compared supervised NLP-based keyword dictionaries of symptom clusters from unstructured clinical notes and clinician-entered chief complaints from a structured drop-down menu with the following 2 outcomes: hospitalization and high Emergency Severity Index (ESI) score. Of 12 symptom clusters, the NLP cluster was substantial in predicting hospitalization in 11 (92%) clusters; 8 (67%) clusters remained significant even after controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 (75%) remained significant when controlling for clinician-determined chief complaints. The correlation between NLP clusters and chief complaints was low (r=-0.04 to 0.6), indicating complementarity of information. The NLP-derived features and clinicians' knowledge were complementary in explaining patient outcome heterogeneity. They can provide an efficient approach to patient flow management, for example, in an emergency medicine setting. We further demonstrated the feasibility of creating extensive and precise keyword dictionaries with NLP by medical experts without requiring programming knowledge. Using the dictionary, we could classify short and unstructured clinical texts into diagnostic categories defined by the clinician.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Background:The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).Material and methodsWe analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.ResultsThe AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.ConclusionsOur results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

An object detection algorithm based on infrared-visible dual modal feature fusion

Dual modal image pairs can provide complementary feature information and overcome the limitations of single modal object detection algorithms, thus improving detection performance. In order to fully utilize the features of different modalities, this paper proposes a dual-branch fusion detection network that can simultaneously input both infrared and visible modalities for object detection. The method is based on YOLOv5-s, and two attention modules are proposed according to the characteristics of different modalities to enhance the expression capability of infrared and visible image feature information respectively; At the same time, a dual modal fusion module is designed for cross-modal information complementarity by fusing features from corresponding scales of both modalities; Finally, a feature enhancement module is proposed to improve the multi-scale feature fusion capability. The algorithm is validated on the KAIST, FLIR, and GIR datasets and compared with classic single modal and dual modal detection algorithms. Experimental results show that, compared with the baseline algorithm YOLOv5-s alone detecting visible and infrared images separately, the proposed algorithm improves the detection accuracy by 17.6% and 5.9% on the KAIST dataset respectively, and 19.1% and 13% on the FLIR dataset respectively. The proposed algorithm also has a significant advantage in detection accuracy on the self-built GIR dataset.

Interactive residual coordinate attention and contrastive learning for infrared and visible image fusion in triple frequency bands

The auto-encoder (AE) based image fusion models have achieved encouraging performance on infrared and visible image fusion. However, the meaningful information loss in the encoding stage and simple unlearnable fusion strategy are two significant challenges for such models. To address these issues, this paper proposes an infrared and visible image fusion model based on interactive residual attention fusion strategy and contrastive learning in the frequency domain. Firstly, the source image is transformed into three sub-bands of the high-frequency, low-frequency, and mid-frequency for powerful multiscale representation from the prospective of the frequency spectrum analysis. To further cope with the limitations of the straightforward fusion strategy, a learnable coordinate attention module in the fusion layer is incorporated to adaptively fuse representative information based on the characteristics of the corresponding feature maps. Moreover, the contrastive learning is leveraged to train the multiscale decomposition network for enhancing the complementarity of information at different frequency spectra. Finally, the detail-preserving loss, feature enhancing loss and contrastive loss are incorporated to jointly train the entire fusion model for good detail maintainability. Qualitative and quantitative comparisons demonstrate the feasibility and validity of our model, which can consistently generate fusion images containing both highlight targets and legible details, outperforming the state-of-the-art fusion methods.

Research on cross-modal emotion recognition based on multi-layer semantic fusion.

Multimodal emotion analysis involves the integration of information from various modalities to better understand human emotions. In this paper, we propose the Cross-modal Emotion Recognition based on multi-layer semantic fusion (CM-MSF) model, which aims to leverage the complementarity of important information between modalities and extract advanced features in an adaptive manner. To achieve comprehensive and rich feature extraction from multimodal sources, considering different dimensions and depth levels, we design a parallel deep learning algorithm module that focuses on extracting features from individual modalities, ensuring cost-effective alignment of extracted features. Furthermore, a cascaded cross-modal encoder module based on Bidirectional Long Short-Term Memory (BILSTM) layer and Convolutional 1D (ConV1d) is introduced to facilitate inter-modal information complementation. This module enables the seamless integration of information across modalities, effectively addressing the challenges associated with signal heterogeneity. To facilitate flexible and adaptive information selection and delivery, we design the Mask-gated Fusion Networks (MGF-module), which combines masking technology with gating structures. This approach allows for precise control over the information flow of each modality through gating vectors, mitigating issues related to low recognition accuracy and emotional misjudgment caused by complex features and noisy redundant information. The CM-MSF model underwent evaluation using the widely recognized multimodal emotion recognition datasets CMU-MOSI and CMU-MOSEI. The experimental findings illustrate the exceptional performance of the model, with binary classification accuracies of 89.1% and 88.6%, as well as F1 scores of 87.9% and 88.1% on the CMU-MOSI and CMU-MOSEI datasets, respectively. These results unequivocally validate the effectiveness of our approach in accurately recognizing and classifying emotions.

Foreground Capture Feature Pyramid Network-Oriented Object Detection in Complex Backgrounds.

Feature pyramids are widely adopted in visual detection models for capturing multiscale features of objects. However, the utilization of feature pyramids in practical object detection tasks is prone to complex background interference, resulting in suboptimal capture of discriminative multiscale foreground semantic features. In this article, a foreground capture feature pyramid network (FCFPN) for multiscale object detection is proposed, to address the problem of inadequate feature learning in complex backgrounds. FCFPN consists of a foreground dual attention (FDA) module and a pathway aggregation (PA) structure. Specifically, the FDA mechanism activates top-down foreground channel responses and lateral spatial foreground location features, so that channel and spatial foreground features are adequately captured. Then, the PA module adaptively learns the fusion weights of multiscale features at different levels of the feature pyramid, which enhances the complementarity of semantic information between different levels of the foreground feature maps. Since the fusion weights are learned adaptively based on different pyramid levels, the detection model accordingly retains the gained information of feature sizes and suppresses the conflicting information. The evaluations on public datasets and the self-built complex background dataset demonstrate that the detection average precision (AP) and the feature learning performance of the proposed method are superior compared with other FPNs, which proves the effectiveness of the proposed FCFPN.

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma from Multi-sequence Magnetic Resonance Imaging based on Deep Fusion Representation Learning.

Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preoperative MVI using diverse medical images. These existing methods rely on human experience, attribute selection or clinical trial testing, which is often time-consuming and labor-intensive. Leveraging the advantages of deep learning, this study presents a novel end-to-end algorithm for predicting MVI prior to surgery. We devised a series of data preprocessing strategies to fully extract multi-view features from the data while preserving peritumoral information. Notably, a new multi-branch deep fused feature algorithm based on ResNet (DFFResNet) is introduced, which combines Magnetic Resonance Images (MRI) from different sequences to enhance information complementarity and integration. We conducted prediction experiments on a dataset from the Radiology Department of the First Hospital of Lanzhou University, comprising 117 individuals and seven MRI sequences. The model was trained on 80% of the data using 10-fold cross-validation, and the remaining 20% were used for testing. This evaluation was processed in two cases: CROI, containing samples with a complete region of interest (ROI), and PROI, containing samples with a partial ROI region. The robustness results from repeated experiments at both image and patient levels demonstrate the superior performance and improved generalization of the proposed method compared to alternative models. Our approach yields highly competitive prediction results even when the ROI region outline is incomplete, offering a novel and effective multi-sequence fused strategy for predicting preoperative MVI.

Deep learning-based multifrequency ground penetrating radar data merging

Ground-penetrating radar systems with a single central frequency suffer limitations due to the unavoidable trade-off between resolution and penetration depth that multifrequency equipments can overcome. A new semisupervised multifrequency merging algorithm was developed based on deep learning and specifically on bi-directional long-short term memory to automatically merge varying numbers of data sets collected at different frequencies. A new training strategy, based only on the data set of interest, without synthetic or real training data sets was implemented. The proposed methodology is tested on synthetic and field data, to evaluate its performance and robustness. The merging algorithm can manage the complementarity of information at different central frequencies, properly merging different types of data. Results indicate not only a smooth transition in time, but, even more important, a remarkable broadening of the bandwidth thus increasing the overall resolution. Our approach is not limited to specific frequency components or geologic settings but can be potentially exploited to merge any type of data set with different spectral components.

On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization

At present, sensor recognition technology has become a research hotspot in welding process monitoring. However, most of the existing defect recognition methods are based on single-sensor information, which hinders the further improvement of recognition accuracy and weakens the anti-interference ability under complex conditions. To overcome the shortcomings of single-sensor recognition technology, multi-source information fusion technology can exploit the advantages of different types of sensors to achieve more comprehensive and accurate monitoring of the welding process. However, given the process of fusion of different information, the feature mining mechanism of the deep learning model is still under-explained, and the direct complementarity of the different information remains unclear. In this paper, we propose a hybrid model that uses the fusion of molten pool image and voltage signals to quickly and accurately identify three types of defects in lap MIG welding: burn-through, weld bias, and uneven width. Firstly, a synchronized trigger device is used to acquire molten pool images and weld voltage information, and the voltage data is sliced and matched with the molten pool image data. Secondly, the Short-Time Fourier Transform (STFT) is applied to explore the frequency variation components in the voltage data, and the resulting time-frequency matrix is transformed into a time spectrogram. Finally, the molten pool image and the time spectrogram are simultaneously fed into the hybrid CNN model for online monitoring of welding defect states. The test results show that the average accuracy of the hybrid CNN model in detecting five different welding states reaches 98.87 %, which is significantly higher than three other models based on single sensor information. The visualization of the confusion matrix and Grad-CAM shows that the hybrid CNN model based on multi-source heterogeneous information has a higher accuracy for detecting the transition states that are difficult to detect in welding, and has a more reasonable mechanism for allocating attention, so it can achieve a more accurate and balanced recognition effect in the whole welding manufacturing process. These studies can provide guidance for quality monitoring of welding processes based on multi-source sensor technology, which can be extended to the field of arc additive monitoring.

Information Complementarities and the Dynamics of Transparency Shock Spillovers

ABSTRACTWe show that information complementarities play an important role in the spillover of transparency shocks. We exploit the revelation of financial misconduct by S&P 500 firms, and in a “Stacked Difference‐in‐Differences” design, find that the implied cost of capital increases for “close” industry peers of the fraudulent firms relative to “distant” industry peers. The spillover effect is particularly strong when the close peers and the fraudulent firm share common analyst coverage and common institutional ownership, which have been shown to be powerful proxies for fundamental linkages and information complementarities. We provide evidence that increase in the cost of capital of peer firms is due, at least in part, to “beta shocks.” Disclosure by close peers—especially those with co‐coverage and co‐ownership links—also increases following fraud revelation. Although disclosure remains high in the following years, the cost of equity starts to decrease.