Adaptive Modulation Research Articles

Causal-relationship representation enhanced joint extraction model for elements and relationships

The joint extraction of elements such as entities, relations, events, and their arguments, along with their specific interrelationships, is a critical task in natural language processing. Existing research often employs implicit handling of interactions between tasks through techniques like shared encodings or parameter sharing, lacking explicit modeling of the specific relationships between tasks. This limitation hinders the full utilization of inter-task correlation information and impacts effective collaboration between tasks. To address this, we propose a model for joint extraction of elements and relations based on causal relationship representation enhancement (CRE). This model aims to capture specific relationships between tasks in multiple stages, facilitating finer adjustments and optimization of subtasks and thereby enhancing the overall model performance. Specifically, CRE comprises three key modules: feature adaptation, feature interaction, and feature fusion. The feature adaptation module selects and adjusts features from shared encodings based on the requirements of specific tasks to better adapt to semantic differences between different tasks. The feature interaction module employs causal reasoning to comprehensively capture the causal relationships between tasks while mitigating negative transfer brought about by interference from semantic information. The feature fusion module further integrates features to obtain optimized task-specific representations. Ultimately, CRE exhibits a significant improvement in average performance across multiple information extraction tasks.

Feature Adaptive Modulation and Prototype Learning for Domain Generalization Intelligent Fault Diagnosis

Feature Adaptive Modulation and Prototype Learning for Domain Generalization Intelligent Fault Diagnosis

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

CDSG-SAM: A cross-domain self-generating prompt few-shot brain tumor segmentation pipeline based on SAM

In clinical practice, accurate segmentation of brain tumor regions is essential for patient treatment and survival. Accurate brain tumor segmentation is an important task and it is based on a large amount of labeled data. Clinical data are from different cities and device protocols, which leads to the poor generalization ability of the model. In addition, the small amount of clinical data also leads to the decline of the segmentation performance. In this paper, a cross-domain self-generating prompt few-shot brain tumor segmentation pipeline called CDSG-SAM is developed. This pipeline is based on the Segment Anything Model (SAM), which integrates a new Cross-domain self-attention (CDS) Adapter module and Self-Generating (SG) Prompt module. The SAM-based segmentation model is more suitable for few-shot medical image segmentation. A dynamic fuzzy support mask decoder module (DFSMD) is proposed in the decoder stage to enhance the accuracy of brain tumor segmentation edges. Furthermore, a Joint Loss function (JL) is designed to comprehensively consider multiple aspects of performance indicators to improve the accuracy and robustness of the model. Experimental results evaluated on the BraTS2021 glioma dataset, Clinical glioma brain tumor data (CBTD), and BraTS2023 metastatic tumor dataset show that the proposed pipeline has achieved excellent performance with an average dice coefficient of 0.918, 0.828, and 0.868 which outperforms than existing methods.

A Hybrid Approach to Modeling Heart Rate Response for Personalized Fitness Recommendations Using Wearable Data

Heart rate (HR) is a key indicator of fitness and cardiovascular health, and accurate HR monitoring and prediction are essential for enhancing personalized fitness experiences. The rise of wearable technology has significantly improved the ability to track personal health, including HR metrics. Accurate modeling of HR response during workouts is crucial for providing effective fitness recommendations, which help users achieve their goals while maintaining safe workout intensities. Although several HR monitoring and prediction models have been developed for personalized fitness recommendations, many remain impractical for real-world applications, and the domain of personalization in fitness applications still lacks sufficient research and innovation. This paper presents a hybrid approach to modeling HR response to workout intensity for personalized fitness recommendations. The proposed approach integrates a physiological model using Dynamic Bayesian Networks (DBNs) to capture heart rate dynamics during workout sessions. DBNs, combined with Long Short-Term Memory (LSTM) networks, model the evolution of HR over time based on workout intensity and individual fitness characteristics. The DBN parameters are dynamically derived from flexible neural networks that account for each user’s personalized health state, enabling the prediction of a full HR profile for each workout, while incorporating factors such as workout history and environmental factors. An adaptive feature selection module further enhances the model’s performance by focusing on relevant data and ensuring responsiveness to new data. We validated the proposed approach on the FitRec dataset, and experimental results show that our model can accurately predict HR responses to workout intensity in future sessions, achieving an average mean absolute error of 5.2 BPM per workout—significantly improving upon existing models. In addition to HR prediction, the model provides real-time fitness personalized recommendations based on individual’s observed workout intensity to an exercise. These findings demonstrate the model’s effectiveness in delivering precise, user personalized heart response to exercise with potential applications in fitness apps for personalized training and health monitoring.

Research on carotid artery plaque anomaly detection algorithm based on ultrasound images

Carotid artery plaque is a key factor in stroke and other cardiovascular diseases. Accurate detection and localization of carotid artery plaque are essential for early prevention and treatment of diseases. However, current carotid artery ultrasound image anomaly detection algorithms face several challenges, such as scarcity of anomaly data in carotid arteries and traditional convolutional neural networks (CNNs) overlooking long-distance dependencies in image processing. To address these issues, we propose an anomaly detection algorithm for carotid artery plaques based on ultrasound images. The algorithm innovatively introduces an anomaly sample pair generation method to increase dataset diversity. Moreover, it employs an improved adaptive recursive gating pyramid pooling module to extract image features. This module significantly enhances the model’s capacity for high-order spatial interactions and adaptive feature fusion, thereby greatly improving the neural network’s feature extraction ability. The algorithm uses a Sigmoid layer to map each pixel’s feature vector to a probability distribution between 0 and 1, and anomalies are detected through probability threshold binarization. Experimental results show that our algorithm’s AUROC index reached 90.7% on a carotid artery dataset, improving by 2.1% compared to the FPI method. This research is expected to provide robust support for the early prevention and treatment of cardiovascular diseases.

Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes

In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.

MS-UNet: Multi-Scale Nested UNet for Medical Image Segmentation with Few Training Data Based on an ELoss and Adaptive Denoising Method

Traditional U-shape segmentation models can achieve excellent performance with an elegant structure. However, the single-layer decoder structure of U-Net or SwinUnet is too “thin” to exploit enough information, resulting in large semantic differences between the encoder and decoder parts. Things get worse in the field of medical image processing, where annotated data are more difficult to obtain than other tasks. Based on this observation, we propose a U-like model named MS-UNet with a plug-and-play adaptive denoising module and ELoss for the medical image segmentation task in this study. Instead of the single-layer U-Net decoder structure used in Swin-UNet and TransUNet, we specifically designed a multi-scale nested decoder based on the Swin Transformer for U-Net. The proposed multi-scale nested decoder structure allows for the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features. In addition, ELoss could improve the attention of the model to the segmentation edges, and the plug-and-play adaptive denoising module could prevent the model from learning the wrong features without losing detailed information. The experimental results show that MS-UNet could effectively improve network performance with more efficient feature learning capability and exhibit more advanced performance, especially in the extreme case with a small amount of training data. Furthermore, the proposed ELoss and denoising module not only significantly enhance the segmentation performance of MS-UNet but can also be applied individually to other models.

Multi-focus image fusion method based on adaptive weighting and interactive information modulation

Multi-focus image fusion method based on adaptive weighting and interactive information modulation

ACA-Net: adaptive context-aware network for basketball action recognition.

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.

Estimation of Fractal Dimension and Segmentation of Body Regions for Deep Learning-Based Gender Recognition

There are few studies utilizing only IR cameras for long-distance gender recognition, and they have shown low recognition performance due to their lack of color and texture information in IR images with a complex background. Therefore, a rough body segmentation-based gender recognition network (RBSG-Net) is proposed, with enhanced gender recognition performance achieved by emphasizing the silhouette of a person through a body segmentation network. Anthropometric loss for the segmentation network and an adaptive body attention module are also proposed, which effectively integrate the segmentation and classification networks. To enhance the analytic capabilities of the proposed framework, fractal dimension estimation was introduced into the system to gain insights into the complexity and irregularity of the body region, thereby predicting the accuracy of body segmentation. For experiments, near-infrared images from the Sun Yat-sen University multiple modality re-identification version 1 (SYSU-MM01) dataset and thermal images from the Dongguk body-based gender version 2 (DBGender-DB2) database were used. The equal error rates of gender recognition by the proposed model were 4.320% and 8.303% for these two databases, respectively, surpassing state-of-the-art methods.

Domain Adaptive Thermal Object Detection with Unbiased Granularity Alignment

Domain Adaptive Object Detection (DAOD) alleviates the challenge of labor-intensive annotations by transferring semantic information from a labeled source domain to an unlabeled target domain. However, the DAOD suffers from biased discrimination and negative transfer in the thermal domain due to the inherent heterogeneity between the RGB and thermal images. To address the above issues, we propose the Unbiased Granularity Alignment (UGA) framework to facilitate the unified alignment for RGB-Thermal DAOD. Specifically, we devise a Channel Self-encoding Adaptation (CSA) module to mitigate biased discrimination from the discriminative enhancement perspective. CSA aligns the intra-domain channel subspace for inter-domain channel harmonizing. Upon revisiting instance alignment, we uncovered inaccuracies proposals and unstable positive sample phenomena. Therefore, we propose the Relative Relationship Adaptation (RRA) module to mitigate negative transfer. RRA ensures inter-domain semantic consistency through sparse instance alignment. Extensive experiments are conducted on visible-to-thermal and visible-to-visible benchmarks to validate the effectiveness, and our UGA framework outperforms state-of-the-art by a remarkable margin. The code of our UGA is available at https://github.com/zyfone/UGA .

ASIFusion: An Adaptive Saliency Injection-Based Infrared and Visible Image Fusion Network

The purpose of infrared and visible image fusion (IVIF) is to acquire a more informative fused image by leveraging complementary information, facilitating human perception and machine vision. Among the existing fusion methods, the saliency-based methods conform to human perception characteristics and achieve relatively advantageous fusion performance. However, such methods fail to adaptively maintain the edge and intensity of salient objects, resulting in fixed fusion performance. To address these issue, we present ASIFusion, an adaptive saliency injection-based IVIF network. First, source images are inputted to the feature extraction encoder for fully extracting features. Meanwhile, the proposed adaptive saliency injection module detects salient objects in the infrared image and then learns the fusion weights of each channel, which serve as supplementary information for further fusion. These learned weights are utilized to merge the source images’ extracted features. Finally, the feature reconstruction decoder produces a fused image with injected saliency. The fused image maintains the intensity and edge of the salient objects and fully preserves the complementary information. Extensive experiments demonstrate that our proposed network outperforms state-of-the-art (SOTA) approaches with regard to fusion performance and computational efficiency.

Advancing semantic segmentation of two-dimensional materials using a semantic-adaptive transformer model

Accurate detection and characterization of two-dimensional (2D) materials are essential for their effective utilization in various applications. Traditional techniques, such as chemical vapor deposition, often produce materials with high defect density, while mechanical exfoliation is hindered by its labor-intensive and time-consuming nature. In this Letter, we propose a semantic-adaptive transformer model, termed Semptive, designed specifically for the precise detection of monolayer MoS2. Our approach integrates a semantic adaptation module with a multi-head self-attention mechanism, incorporating deep supervision and leveraging prior knowledge to enhance model performance. The model was trained on a dataset obtained through mechanical exfoliation and validated using photoluminescence spectroscopy. The experimental results reveal that Semptive significantly enhances segmentation performance compared to conventional models, achieving higher Intersection over Union and Dice scores while reducing computational demands. This method represents a notable advancement in the efficient and precise identification of 2D materials, providing substantial improvements for material characterization and device fabrication processes.

Elucidating maternal provisioning for bivalve larvae under ocean acidity extreme events

Ocean acidity extreme (OAX) events, triggered by climate change and anthropogenic activities, are projected to become more intense and frequent in coastal ecosystems, devastating marine bivalves and ecosystems they support. Maternal effects adaptively modulate offspring performance in response to climatic stressors, but whether and to what extent they can confer offspring resistance to OAX remain largely unknown. Here, we investigated impacts of OAX on the parental and larval lipidomes of Manila clams (Ruditapes philippinarum) to add further insights into the energetic nature of maternal effects. A total of 177 significantly down-regulated lipid components (categorized into glycerolipids mainly) were shown in OAX-stressed adults compared with those reared under ambient conditions, and following parental conditioning, larvae also exhibited a further decreasing down-regulation of the glycerolipid components. Triacylglycerols were identified as the predominant composition of glycerolipids and the primary sources of energy for gonadal maturation and larvae development. Yet, larvae spawn from adults exposed to OAX had significantly lower contents of triacylglycerols than those without a prior history of parental conditioning, with the carbon chain length and unsaturation degree of the triacylglycerol components being significantly affected. The latter was also in line with significant increases in the production of triacylglycerol byproducts (diacylglycerols). Overall, our findings suggest that when OAX prevailed during reproductive seasons of Manila clams, maternal effects could be maladaptive by depressing the energetic deposition of larvae, and may not be a potential adaptive modulator of marine bivalves to cope with unprecedented environmental change.

MSTAN: multi-scale spatiotemporal attention network with adaptive relationship mining for remaining useful life prediction in complex systems

Abstract In the era of smart manufacturing and advanced industrial systems, the high degree of integration and intelligence of equipment demands higher reliability and safety from systems. Existing methods often rely on historical data for Remaining Useful Life (RUL) prediction to achieve Prognostic and Health Management (PHM). However, the internal units of complex equipment exhibit significant spatial correlation and temporal diversity, making PHM for complex equipment a multidimensional challenge involving both temporal and spatial information, thereby severely limits the effectiveness of RUL prediction for complex systems. Addressing these challenges, this study introduces a multi-scale spatiotemporal attention network with adaptive relationship mining, specifically designed for the remaining useful life (RUL) prediction of such equipment. The core of the proposed method lies in the multi-scale feature perception module, which adeptly extracts varied scale features from multidimensional sensor data. Following this, an innovative adaptive relationship mining module is integrated to uncover multi-order coupling relationships between diverse sensors, enhancing the model’s predictive accuracy. Furthermore, a spatiotemporal attention module is employed to discern and emphasize crucial spatiotemporal correlations. To validate the effectiveness and superiority of the proposed method, the Commercial Modular Aero-propulsion System Simulation (C-MAPSS) dataset is employed for comprehensive performance evaluation, the IEEE 2012 PHM bearing dataset is also adopted to demonstrate the generalization and robustness of the proposed method. The results not only show a notable improvement over existing methods but also offer a more intuitive understanding through visual representations, marking a significant stride in enhancing the safety and efficiency of complex systems.

A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases.

Denoising Diffusion Implicit Model for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.

Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach.

Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions with high robustness and generalization presents a formidable challenge. This study draws inspiration from cellular functional characteristics and natural selection, proposing a novel medical segmentation model named the vital characteristics cellular neural network. This model incorporates vital characteristics observed in multicellular organisms, including memory, adaptation, apoptosis, and division. Memory module enables the network to rapidly adapt to input data during the early stages of training, accelerating model convergence. Adaptation module allows neurons to select the appropriate activation function based on varying environmental conditions. Apoptosis module reduces the risk of overfitting by pruning neurons with low activation values. Division module enhances the network's learning capacity by duplicating neurons with high activation values. Experimental evaluations demonstrate the efficacy of this model in enhancing the performance of neural networks for medical image segmentation. The proposed method achieves outstanding results across numerous publicly available datasets, indicating its potential to contribute significantly to the field of medical image analysis and facilitating accurate and efficient segmentation of medical imagery. The proposed method achieves outstanding results across numerous publicly available datasets, with an F1 score of 0.901, Intersection over Union of 0.841, and Dice coefficient of 0.913, indicating its potential to contribute significantly to the field of medical image analysis and facilitating accurate and efficient segmentation of medical imagery.

AFIRE: Adaptive FusionNet for illumination-robust feature extraction in heterogeneous imaging environments

The fusion of infrared and visible images aims to synthesize a fused image that incorporates richer information by leveraging the distinct characteristics of each modality. However, the disparate quality of input images in terms of infrared and visible light significantly impacts fusion performance. To address this issue, we propose a novel deep adaptive fusion method called Adaptive FusionNet for Illumination-Robust Feature Extraction (AFIRE). This method involves the interactive processing of two input features and dynamically adjusts the fusion weights based on varying illumination conditions. Specifically, we introduce a novel interactive extraction structure during the feature extraction stage for both infrared and visible light, enabling the capture of more complementary information. Additionally, we design a Deep Adaptive Fusion module to assess the quality of input features and perform weighted fusion through a channel attention mechanism. Finally, a new loss function is formulated by incorporating the entropy and median of input images to guide the training of the fusion network. Extensive experiments demonstrate that AFIRE outperforms state-of-the-art methods in preserving pixel intensity distribution and texture details. Source code is available at: https://www.github.com/ISCLab-Bistu/AFIRE.