Minimal Loss Of Information Research Articles

HADNet: A Novel Lightweight Approach for Abnormal Sound Detection on Highway Based on 1D Convolutional Neural Network and Multi-Head Self-Attention Mechanism

Video surveillance is an effective tool for traffic management and safety, but it may face challenges in extreme weather, low visibility, areas outside the monitoring field of view, or during nighttime conditions. Therefore, abnormal sound detection is used in traffic management and safety as an auxiliary tool to complement video surveillance. In this paper, a novel lightweight method for abnormal sound detection based on 1D CNN and Multi-Head Self-Attention Mechanism on the embedded system is proposed, which is named HADNet. First, 1D CNN is employed for local feature extraction, which minimizes information loss from the audio signal during time-frequency conversion and reduces computational complexity. Second, the proposed block based on Multi-Head Self-Attention Mechanism not only effectively mitigates the issue of disappearing gradients, but also enhances detection accuracy. Finally, the joint loss function is employed to detect abnormal audio. This choice helps address issues related to unbalanced training data and class overlap, thereby improving model performance on imbalanced datasets. The proposed HADNet method was evaluated on the MIVIA Road Events and UrbanSound8K datasets. The results demonstrate that the proposed method for abnormal audio detection on embedded systems achieves high accuracy of 99.6% and an efficient detection time of 0.06 s. This approach proves to be robust and suitable for practical applications in traffic management and safety. By addressing the challenges posed by traditional video surveillance methods, HADNet offers a valuable and complementary solution for enhancing safety measures in diverse traffic conditions.

Multifunctional GAN-based optimization for X-ray tomography under different conditions

Based on the generative adversarial network (GAN), we present a multifunctional X-ray tomographic protocol for artifact correction, noise suppression, and super-resolution of reconstruction. The protocol mainly consists of a data preprocessing module and multifunctional GAN-based loss function simultaneously dealing with ring artifacts and super-resolution. The experimental protocol removes ring artifacts and improves the contrast-to-noise ratio (CNR) and spatial resolution (SR) of reconstructed images successfully, which shows the capability to adaptively rectify ring artifacts with varying intensities and types while achieving super-resolution. Compared with the main existing deep learning models or conventional tomographic correction methods, it also enables higher processing speed and minimal information loss, especially for images of smaller dimensions. This study provides a robust optimization tool for the equivalent realization of large fields of view and high-resolution X-ray tomography. The experimental datasets were collected from a series of X-ray cone-beam computed tomography scans of biological samples.

A Decision-Making Model with Cloud Model, Z-Numbers, and Interval-Valued Linguistic Neutrosophic Sets

Interval-valued linguistic neutrosophic sets (IVLNSs), Z-numbers, and the trapezium cloud model are powerful tools for expressing uncertainty and randomness. This paper aims to combine these methodologies. First, we review relevant concepts and operators, introducing a novel combination of IVLNSs and Z-numbers, which establishes a new form of expression. Subsequently, we propose the Z-interval-valued linguistic neutrosophic set-trapezium–trapezium cloud (Z-IVLNS-TTC) model, designed to minimize information loss and distortion during quantification. A novel method for calculating the objective weight vector is then developed using multi-objective programming (MOP). Drawing inspiration from the TOPSIS method, we propose a new approach for calculating the distance between Z-IVLNS-TTCs based on the p-norm. Finally, a group decision-making problem is presented to demonstrate the practical application of the proposed method. To validate the effectiveness and feasibility of the method, sensitivity analysis and comparisons with existing approaches are conducted.

Single-Document Abstractive Text Summarization: A Systematic Literature Review

Abstractive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document summarization to gain insights into the challenges, widely used datasets, evaluation metrics, approaches, and methods. This study reviews research articles published between 2011 and 2023 from popular electronic databases. In total, 226 journal and conference publications were included in this review. The in-depth analysis of these papers helps researchers understand the challenges, widely used datasets, evaluation metrics, approaches, and methods. This paper identifies and discusses potential opportunities and directions, along with a generic conceptual framework and guidelines on abstractive summarization models and techniques for research in abstractive text summarization.

Improved YOLOv8n for Lightweight Ship Detection

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature.

Prospective selective embedding of radical prostatectomy specimens is not inferior to full embedding regarding established and new prognostic parameters.

The histopathological examination of radical prostatectomy specimens is essential for assessing critical tumor characteristics, including stage, grade, and margins, all of which impact patient prognosis. However, the extent of embedding the prostate has long been a subject of debate, with some advocating partial/selective embedding and others favoring complete embedding. This study establishes a standardized and time-efficient protocol for processing radical prostatectomy specimens with limited embedding while maintaining diagnostic accuracy. Two hundred twenty-six prostatectomy specimens were analyzed, and the results of a highly standardized selective embedding protocol, systematically embedding the apex, the base, the transition to the seminal vesicles, and selected horizontal sections, were compared with full embedding as the gold standard. Non-inferiority testing was conducted by one-sided binomial tests and Pearson-Clopper confidence intervals. Selective embedding provided consistent and accurate diagnostic information with up to 90-98% concordance in pT, margins, ISUP-grade groups, and presence of IDC-P and cribriform tumor growth. In summary, this study establishes an economical standardized protocol for selective embedding of radical prostatectomy specimens with only minimal loss of information.

Hybrid random projection: Integrating dense and sparse techniques for enhanced representation in high-dimensional data

This paper introduces a novel hybrid random projection method (HRP) that effectively combines the strengths of normal random projection (NRP) and plus-minus one random projection (PMRP). By incorporating a blending parameter, HRP optimises the contributions of the NRP and PMRP, reducing the dimensions of the data while ensuring that the structure of the data is preserved. Conventional methods, such as NRP and PMRP, are recognised for their benefits; however, they also have limitations. NRP is generally accurate but can encounter difficulties with certain data types or noise. The PMRP is simple, but occasionally fails to capture complex relationships within the data. The performance of HRP is investigated through comprehensive evaluations, including simulations and real-world data analyses from key machine learning datasets, such as period change, toxicity, MNIST, and human activity recognition (HAR). We examine various factors such as sample size, dimensions of the original and reduced data, and sparsity. Distance distortion is the primary metric utilised to measure performance, and indicates how well the dataset structure is maintained after dimension reduction. In every test, HRP consistently demonstrates the least distance distortion, performing superiorly to the NRP and PMRP. This is true for a range of scenarios and datasets. HRP represents a significant advancement in dimension reduction techniques. Its ability to minimise information loss during the reduction process demonstrates its potential as a powerful tool for managing complex high-dimensional data in practical applications. This renders HRP an important development in the fields of machine learning, intelligent systems, and artificial intelligence, offering improved efficiency and precision in data analyses.

FE-CFNER: Feature Enhancement-based approach for Chinese Few-shot Named Entity Recognition

Although significant progress has been made in Chinese Named Entity Recognition (NER) methods based on deep learning, their performance often falls short in few-shot scenarios. Feature enhancement is considered a promising approach to address the issue of Chinese few-shot NER. However, traditional feature fusion methods tend to lead to the loss of important information and the integration of irrelevant information. Despite the benefits of incorporating BERT for improving entity recognition, its performance is limited when training data is insufficient. To tackle these challenges, this paper proposes a Feature Enhancement-based approach for Chinese Few-shot NER called FE-CFNER. FE-CFNER designs a double cross neural network to minimize information loss through the interaction of feature cross twice. Additionally, adaptive weights and a top-k mechanism are introduced to sparsify attention distributions, enabling the model to prioritize important information related to entities while excluding irrelevant information. To further enhance the quality of BERT embeddings, FE-CFNER employs a contrastive template for contrastive learning pre-training of BERT, enhancing BERT’s semantic understanding capability. We evaluate the proposed method on four sampled Chinese NER datasets: Weibo, Resume, Taobao, and Youku. Experimental results validate the effectiveness and superiority of FE-CFNER in Chinese few-shot NER tasks.

Optimization of sampling design for soil total organic carbon assessment in the precision agriculture framework: Impact of different variogram models and potentiality of ground penetrating radar (GPR) covariate information

Assessing soil organic carbon (SOC) at the field scale is crucial for efficient environmental and agronomic management, especially within the precision agriculture framework. This enables the implementation of crop and soil management strategies to enhance soil quality, increase carbon sequestration, and improve crop yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both in time and labour. This aspect is particularly relevant in agronomic research, where the need to assess the spatial correlation structure of the investigated variables requires the collection of large datasets with georeferenced data and often measurements need to be repeated over time as in the case long-term field experiments − LTE. Methods for minimizing information loss while reducing the sampling scheme, such as spatial simulated annealing (SSA), are particularly valuable in the scope of SOC assessment, especially when faced with budget and time/labour constraints. Within the structure of the SSA method, two critical components can be identified: i) the inclusion of highly informative covariates for the primary variable (SOC); ii) the selection of the most appropriate variogram model for spatial variability assessment. Covariates strongly correlated with SOC, such as those obtained from ground penetrating radar (GPR) that can be collected at a higher spatial density compared to SOC data, along with a well-performing model, can significantly enhance the efficiency of the sampling scheme reduction process. We conducted a study using data from an agronomic field experiment, which included 71 georeferenced sampling locations and, through an iterative downsizing process utilizing SSA, we progressively reduced the number of sampling points by removing 10, 15, and 20 observations. The sampling scheme was refined according to two distinct variogram models: the spherical and Gaussian-Matérn models, both for the primary variable (SOC) and the covariate GPR variable. This process allowed us to identify the optimal variogram model, which has a key role in maximizing the reduction of redundant points while preserving those with valuable information, and to assess the role of the covariate variable both in improving the optimization of the sampling scheme for SOC and in replacing the primary variable to optimize the sampling scheme. Finally, to assess the impact of sampling scheme reduction, a validation process was performed by estimating the dropped points by means of the remaining points using ordinary kriging and regression kriging and analysing the accuracy of such estimation. Our analysis demonstrated that it was possible to reduce the original sampling scheme by approximately 20%, equivalent to eliminating 15 sampling points out of 71, without compromising its predictive capability.

Addressing immortal time bias in precision medicine: Practical guidance and methods development.

To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study. Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes. Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015. While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations. Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

In wastewater treatment systems, extracting meaningful features from process data is essential for effective monitoring and control. However, the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features. To solve this issue, a multi-timescale feature extraction method based on adaptive entropy is proposed. Firstly, the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data, which can illustrate various water quality parameters and the network of relationships among them. Secondly, multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth, which enables us to minimize information loss while uniformly optimizing the timescale. Thirdly, we harness partial least squares (PLS) for feature extraction, resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph. The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

DCANet: Dense Convolutional Attention Network for infrared small target detection

Infrared small target detection and tracking technology plays a essential role in systems like infrared warning, guidance, and missile defense. Efficiently and reliably detecting fast-moving, small targets is a key technological challenge. This study introduces a dense convolutional attention network as a potential solution to address the identified challenge. This network incorporates a dense convolutional interaction module that effectively minimizes information loss across different levels. Additionally, a simple, parameter-free attention module is introduced to adaptively extract features and enhance the emphasis on important information. Furthermore, a dense pixel contrastive learning module is introduced to capture more features of targets with similar sizes by constructing positive and negative sample pairs and designing a pixel contrastive loss function. Experimental validation was conducted on four representative datasets, demonstrating the success of our approach in terms of performance superiority over other methods.

The mental imagery scale for art students: Building and validating a short form

Mental imagery is a vital cognitive skill that significantly influences how reality is perceived while creating art. Its multifaceted nature reveals various dimensions of creative expression, amplifying the inherent complexities of measuring it. This study aimed to shorten the Mental Imagery Scale in Artistic Creativity (MISAC) via the Ant Colony Optimization algorithm (ACO), a metaheuristic methodology for developing psychometrically robust brief scales. Answering 63 items in the original version of MISAC demands a higher cognitive load and, consequently, more time. Therefore, our goal was to shorten it while preserving its psychometric properties. In this study, responses to the MISAC were obtained from 500 undergraduate students enrolled in an art education program. The items on the short form of the MISAC were selected based on pre-specified validity criteria and content representability. The 28-item short form of MISAC demonstrated comparable performance to the original version regarding construct validity, criteria-related validity, and reliability coefficients. Moreover, strict invariance was attained across both gender groups in the validation process of the short form. These results highlight the utility of the shortened version of the MISAC as a valid measure with minimal loss of information of scores compared to the full version.

WaveFusionNet: Infrared and visible image fusion based on multi-scale feature encoder–decoder and discrete wavelet decomposition

To merge complementary information from multimodal images, such as thermal saliency from infrared images and texture details from visible images, traditional multi-scale transform-based methods have been extensively studied, with deep learning-based methods gaining significant popularity in recent years. However, there has been limited research on optimally combining the advantages of these two categories in fusion. In this paper, we propose a novel infrared and visible image fusion (IVIF) framework, WaveFusionNet, which integrates precise frequency feature decomposition from the discrete wavelet transform (DWT) with the comprehensive feature extraction from the multi-scale encoder. Firstly, we train an encoder–decoder network for multi-scale feature extraction and image reconstruction. DWT is used for down-sampling with minimal information loss by decomposing extracted features into low and high-frequency sub-bands. Next, a dual-band feature fusion (DBFF) module is trained to merge these sub-bands by integrating a spatial feature transform-based sub-network for low-frequency fusion and a maximum absolute value selection strategy for fusing high-frequencies. Finally, all fused sub-bands are fed into the pre-trained decoder to reconstruct the final image. Experimental results on three benchmark datasets (TNO, Roadscene, and MSRS) demonstrate that the proposed fusion method outperforms recent IVIF methods in both quantitative assessment and visual perception while maintaining competitive time complexity.

Most frequent value analysis of distance measurements to M87

Abstract We reanalyze the recent compilation of distance measurements to M87 by collecting the data from published literature. Different from the traditional statistical methods, based on the principle of minimum information loss, we use a robust most frequent value (MFV) procedure to estimate the distance to M87, irrespective of the Gaussian or non-Gaussian distributions. The MFV-based robust estimate for the M87 distance modulus is given by $31.09^{+0.04}_{-0.03}$(statistical)$^{+0.05}_{-0.07}$ (systematic) mag corresponding to a 68.27% confidence interval, whereas the result of combining the two uncertainties in quadrature is $31.09^{+0.06}_{-0.08}$ mag. We also construct the error distributions of M87 distance moduli values according to the weighted mean, median, and MFV, which is non-Gaussian. This demonstrates that the MFV method offers a more accurate and robust estimate of the distance to M87 compared to methods that depend on the unfulfilled assumption of Gaussianity.

MSRA-Net: multi-channel semantic-aware and residual attention mechanism network for unsupervised 3D image registration

Objective. Convolutional neural network (CNN) is developing rapidly in the field of medical image registration, and the proposed U-Net further improves the precision of registration. However, this method may discard certain important information in the process of encoding and decoding steps, consequently leading to a decline in accuracy. To solve this problem, a multi-channel semantic-aware and residual attention mechanism network (MSRA-Net) is proposed in this paper. Approach. Our proposed network achieves efficient information aggregation by cleverly extracting the features of different channels. Firstly, a context-aware module (CAM) is designed to extract valuable contextual information. And the depth-wise separable convolution is employed in the CAM to alleviate the computational burden. Then, a new multi-channel semantic-aware module (MCSAM) is designed for more comprehensive fusion of up-sampling features. Additionally, the residual attention module is introduced in the up-sampling process to extract more semantic information and minimize information loss. Main results. This study utilizes Dice score, average symmetric surface distance and negative Jacobian determinant evaluation metrics to evaluate the influence of registration. The experimental results demonstrate that our proposed MSRA-Net has the highest accuracy compared to several state-of-the-art methods. Moreover, our network has demonstrated the highest Dice score across multiple datasets, thereby indicating that the superior generalization capabilities of our model. Significance. The proposed MSRA-Net offers a novel approach to improve medical image registration accuracy, with implications for various clinical applications. Our implementation is available at https://github.com/shy922/MSRA-Net.

Ancient paintings inpainting based on dual encoders and contextual information

Deep learning-based inpainting models have achieved success in restoring natural images, yet their application to ancient paintings encounters challenges due to the loss of texture, lines, and color. To address these issues, we introduce an ancient painting inpainting model based on dual encoders and contextual information to overcome the lack of feature extraction and detail texture recovery when restoring ancient paintings. Specifically, the proposed model employs a gated encoding branch that aims to minimize information loss and effectively capture semantic information from ancient paintings. A dense multi-scale feature fusion module is designed to extract texture and detail information at various scales, while dilated depthwise separable convolutions are utilized to reduce parameters and enhance computational efficiency. Furthermore, a contextual feature aggregation module is incorporated to extract contextual features, enhancing the overall consistency of the inpainting results. Finally, a color loss function is introduced to ensure color consistency in the restored area, harmonizing it with the surrounding region. The experimental results indicate that the proposed model effectively restores the texture details of ancient paintings, outperforming other methods both qualitatively and quantitatively. Additionally, the model is tested on real damaged ancient paintings to validate its practicality and efficacy.

Flare Removal Model Based on Sparse-UFormer Networks.

When a camera lens is directly faced with a strong light source, image flare commonly occurs, significantly reducing the clarity and texture of the photo and interfering with image processing tasks that rely on visual sensors, such as image segmentation and feature extraction. A novel flare removal network, the Sparse-UFormer neural network, has been developed. The network integrates two core components onto the UFormer architecture: the mixed-scale feed-forward network (MSFN) and top-k sparse attention (TKSA), creating the sparse-transformer module. The MSFN module captures rich multi-scale information, enabling the more effective addressing of flare interference in images. The TKSA module, designed with a sparsity strategy, focuses on key features within the image, thereby significantly enhancing the precision and efficiency of flare removal. Furthermore, in the design of the loss function, besides the conventional flare, background, and reconstruction losses, a structural similarity index loss has been incorporated to ensure the preservation of image details and structure while removing the flare. Ensuring the minimal loss of image information is a fundamental premise for effective image restoration. The proposed method has been demonstrated to achieve state-of-the-art performance on the Flare7K++ test dataset and in challenging real-world scenarios, proving its effectiveness in removing flare artefacts from images.

An ECG denoising technique based on AHIN block and gradient difference max loss

The electrocardiogram (ECG) signal is susceptible to interference from unknown noises during the acquisition process due to their low frequency and amplitude, resulting in the loss of significant information in the signals. Recent advancements in deep learning models have shown promising results in signal processing. However, these models lack robustness against various types of noise and often overlook the gradient difference between denoised and original signals. In this study, we propose a novel deep learning denoising method based on an attention half instance normalization block (AHIN block) and a gradient difference max loss function (GDM Loss). Our approach consists of two stages: firstly, we input the noisy ECG signal to obtain a denoised version; secondly, we reconstruct the denoised signal by fusing preliminary results from the first stage while correcting waveform distortions caused by initial denoising to minimize information loss. Additionally, we introduce a new loss function that considers differences between slopes of the denoised ECG signal and clean ECG signal. To validate our proposed method's effectiveness, extensive experiments were conducted on both our model architecture and loss function compared with other state-of-the-art methods. Results demonstrate that our approach achieves excellent performance in terms of both signal-to-noise ratio (SNR) and root-mean-square error (RMSE). The proposed noise reduction method improves 8.86%, 12.05% and 10.50% respectively under BW, MA and EM noise.

A novel facial expression recognition model based on harnessing complementary features in multi-scale network with attention fusion

This paper presents a novel method for facial expression recognition using the proposed feature complementation and multi-scale attention model with attention fusion (FCMSA-AF). The proposed model consists of four main components: the shallow feature extractor module, parallel structured two-branch multi-scale attention module (MSA), feature complementing module (FCM), and attention fusion and classification module. The MSA module contains multi-scale attention modules in a cascaded fashion in two paths to learn diverse features. The upper and lower paths use left and right multi-scale blocks to extract and aggregate the features at different receptive fields. The attention networks in MSA focus on salient local regions to extract features at granular levels. The FCM uses the correlation between the feature maps in two paths to make the multi-scale attention features complementary to each other. Finally, the complementary features are fused through an attention network to form an informative holistic feature which includes subtle, visually varying regions in similar classes. Hence, complementary and informative features are used in classification to minimize information loss and capture the discriminating finer aspects of facial expression recognition. Experimental evaluation of the proposed model carried out on AffectNet and CK+ datasets achieve accuracies of 64.59% and 98.98%, respectively, outperforming some of the state-of-the-art methods.