Feature Compression Research Articles

Impact of anatomical features of non-thrombotic left iliac venous compression on the development of venous leg ulcers based on CT venography.

To explore the anatomical features of left iliac vein (LIV) in non-thrombotic venous leg ulcers (VLUs) and to identify the impact of these anatomical features on VLUs based on computed tomography venography (CTV). This is a retrospective, single-center study of a database (2021-2023) of 431 patients with non-thrombotic chronic venous insufficiency. According to CEAP clinical (C) classifications, cases of C6 and C2 were included for analysis as case and control groups. Based on CTV data, variables that reflected the anatomical characteristics of LIV and lower lumbar degenerative changes were compared between the two groups. Multivariate logistic regression analysis was performed to evaluate impact of the anatomical features of LIV on VLUs. A total of 116 patients (group C6 (n = 33) and C2 (n = 83)) were included. Variables including anterior lumbar vertebral osteophytes (ALVO), minimum diameter of LIV, the number of sites of LIV stenosis, proportion of cases with percentage compression of LIV ≥ 75%, proportion of cases with triple or dual LIV stenosis showed significant difference between the two groups (all P ˂0.05). Logistic regression analysis showed ALVO [odds ratio (OR) = 3.852, P = 0.025], number of sites of LIV stenosis [OR = 2.654, P = 0.027] and minimum diameter of LIV [OR = 0.585, P = 0.010] were all significant for VLUs. The factors of ALVO and number of sites of LIV stenosis were risk factors, whereas minimum diameter of LIV was a protective factor for VLUs. This provided a direction for preoperative planning of non-thrombotic LIV stenting.

Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood forecasting accuracy by integrating geo-spatiotemporal analyses, cascading dimensionality reduction, and SageFormer-based multi-step-ahead predictions. The framework efficiently processes satellite-derived data, addressing the curse of dimensionality and focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- and intra-dependencies within a compressed feature space, making it particularly effective for long-term forecasting. Performance evaluations against LSTM, Transformer, and Informer across three data fusion scenarios reveal substantial improvements in forecasting accuracy, especially in data-scarce basins. The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. The proposed framework combines cascading dimensionality reduction with advanced deep learning, enhancing both interpretability and precision in capturing complex dependencies. By offering a straightforward and reliable approach, this study advances remote sensing applications in hydrological modeling, providing a robust tool for mitigating the impacts of hydroclimatic extremes.

Robust underwater object tracking with image enhancement and two-step feature compression

Developing a robust algorithm for underwater object tracking (UOT) is crucial to support the sustainable development and utilization of marine resources. In addition to open-air tracking challenges, the visual object tracking (VOT) task presents further difficulties in underwater environments due to visual distortions, color cast issues, and low-visibility conditions. To address these challenges, this study introduces a novel underwater target tracking framework based on correlation filter (CF) with image enhancement and a two-step feature compression mechanism. Underwater image enhancement mitigates the impact of visual distortions and color cast issues on target appearance modeling, while the two-step feature compression strategy addresses low-visibility conditions by compressing redundant features and combining multiple compressed features based on the peak-to-sidelobe ratio (PSR) indicator for accurate target localization. The excellent performance of the proposed method is demonstrated through evaluation on two public UOT datasets.

Comprehensive Study of Sub-micron Porous Titanium-zirconium Scaffolds Fabricated by Liquid Metal Dealloying

This article investigates the microstructure, mechanical properties and biocompatibility of ultrafine porous titanium-zirconium (TiZr) alloys synthesized by the liquid metal dealloying (LMD) at a temperature of 1023K for various immersion times. The dealloying of Ti20Zr20Cu60 precursors in molten magnesium causes to self-organize three-dimensional hcp-α structures by surface diffusion mechanism, wherein the ligament size, density and porosity range from 130 to 280nm, 2.8 to 3.2g/cm3 and 0.43 to 0.5, respectively. The compression test results of porous products indicate a perfect combination of high compressive strength (415−620MPa) and low elastic modulus (2.1−3.2GPa) matching that of the human bone. The maximum ligament size (280nm), density (3.2g/cm3) and compressive strength (620MPa) corresponds to immersion time of 600s; thereby, the mechanical characteristics can be tailored by modifying the process parameters. Meanwhile, the elastic modulus and compressive strength enhance almost linearly with increasing the relative density in accordance with the Gibson and Ashby model. The in-vitro experiments with MG-63 osteoblast cells also suggests the immersion time of 600s to better support the bone tissue ingrowth. The porous TiZr alloy fabricated by the new dealloying method can be considered as a novel biomaterial for dental implants owing to its unique microstructure, compressive features and biocompatibility.

HEVC Video Adversarial Samples Detection via Joint Features of Compression and Pixel Domains

HEVC Video Adversarial Samples Detection via Joint Features of Compression and Pixel Domains

Neural network embedding of functional microconnectome

Abstract Our brains operate as a complex network of interconnected neurons. To gain a deeper understanding of this network architecture, it is essential to extract simple rules from its intricate structure. This study aimed to compress and simplify the architecture, with a particular focus on interpreting patterns of functional connectivity in 2.5 hr of electrical activity from a vast number of neurons in acutely sliced mouse brains. Here, we combined two distinct methods together: automatic compression and network analysis. Firstly, for automatic compression, we trained an artificial neural network named NNE (neural network embedding). This allowed us to reduce the connectivity to features, be represented only by 13% of the original neuron count. Secondly, to decipher the topology, we concentrated on the variability among the compressed features and compared them with 15 distinct network metrics. Specifically, we introduced new metrics that had not previously existed, termed as indirect-adjacent degree and neighboring hub ratio. Our results conclusively demonstrated that these new metrics could better explain approximately 40%–45% of the features. This finding highlighted the critical role of NNE in facilitating the development of innovative metrics, because some of the features extracted by NNE were not captured by the currently existed network metrics.

Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization.

Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature of legal documentation. Despite the recent progress in reinforcement learning for natural language text summarization, its application in the legal domain has been less effective. This paper introduces SAC-VAE, a novel reinforcement learning framework specifically designed for legal text summarization. We leverage a Variational Autoencoder (VAE) to condense the high-dimensional state space into a more manageable lower-dimensional feature space. These compressed features are subsequently utilized by the Soft Actor-Critic (SAC) algorithm for policy learning, facilitating the automated generation of summaries from legal texts. Through comprehensive experimentation, we have empirically demonstrated the effectiveness and superior performance of the SAC-VAE framework in legal text summarization.

Detection of mild sensory hearing loss using a joint reflection-distortion otoacoustic emission profile.

Measuring and analyzing both nonlinear-distortion and linear-reflection otoacoustic emissions (OAEs) combined creates what we have termed a "joint-OAE profile." Here, we test whether these two classes of emissions have different sensitivities to hearing loss and whether our joint-OAE profile can detect mild-moderate hearing loss better than conventional OAE protocols have. 2f1-f2 distortion-product OAEs and stimulus-frequency OAEs were evoked with rapidly sweeping tones in 300 normal and impaired ears. Metrics included OAE amplitude for fixed-level stimuli as well as slope and compression features derived from OAE input/output functions. Results show that mild-moderate hearing loss impacts distortion and reflection emissions differently. Clinical decision theory was applied using OAE metrics to classify all ears as either normal-hearing or hearing-impaired. Our best OAE classifiers achieved 90% or better hit rates (with false positive rates of 5%-10%) for mild hearing loss, across a nearly five-octave range. In summary, results suggest that distortion and reflection emissions have distinct sensitivities to hearing loss, which supports the use of a joint-OAE approach for diagnosis. Results also indicate that analyzing both reflection and distortion OAEs together to detect mild hearing loss produces outstanding accuracy across the frequency range, exceeding that achieved by conventional OAE protocols.

Global–Local Query-Support Cross-Attention for Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) models aim to segment unseen target objects in a query image with scarce annotated support samples. This challenging task requires an effective utilization of support information contained in the limited support set. However, the majority of existing FSS methods either compressed support features into several prototype vectors or constructed pixel-wise support-query correlations to guide the segmentation, which failed in effectively utilizing the support information from the global–local perspective. In this paper, we propose Global–Local Query-Support Cross-Attention (GLQSCA), where both global semantics and local details are exploited. Implemented with multi-head attention in a transformer architecture, GLQSCA treats every query pixel as a token, aggregates the segmentation label from the support mask values (weighted by the similarities with all foreground prototypes (global information)), and supports pixels (local information). Experiments show that our GLQSCA significantly surpasses state-of-the-art methods on the standard FSS benchmarks PASCAL-5i and COCO-20i.

Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete

Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland's algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.

High-precision detection of dibutyl hydroxytoluene in edible oil via convolutional autoencoder compressed Fourier-transform near-infrared spectroscopy

The quality of edible oils is closely related to their chemical compositions. Antioxidants have widespread application in edible oil production. In this study, a pioneering detection approach involving the use of a one-dimensional convolutional autoencoder (1D-CAE) was introduced to compress spectral data for assessing antioxidant levels in edible oils. Fourier-transform near-infrared (FT-NIR) characterisation of edible oil samples with varying antioxidant concentrations was also conducted. An 1D-CAE model was developed to compress different pre-processed spectra into a condensed representation. These compressed features were then integrated with a support vector machine and partial least squares regression models to establish correlations for each target. The study examined the influence of pre-processing steps and feature engineering methods on near-infrared spectral analysis through independent or combined model analysis. The findings revealed that features derived from the 1D-CAE model demonstrated remarkable repeatability and can be utilised to construct robust detection models. The experimental results showed that the optimal detection model derived based on the 1D-CAE compression features has an average R2, RPD and RMSE of 0.9953, 15.1664 and 1.2035, respectively, on the prediction set. FT-NIR spectroscopy can be used to accurately detect butylated hydroxytoluene in edible oils. Therefore, autoencoders are an effective tool in spectroscopic analysis, offering promising avenues for future research and application.

Automatic identification and classification of surface defects in small-sized optics

Abstract Precision optics are widely used in lighting systems, imaging systems, and high-precision inspection equipment. The presence of surface defects in optics can seriously affect the design performance of the equipment, so the optics must be rigorously inspected to eliminate defective optics. Currently, engineering products are moving toward smaller sizes, resulting in smaller optics in them also moving towards smaller sizes. However, realizing the surface defect detection of small-sized optics is a great challenge because it requires micrometer-scale high-resolution image acquisition and automatic detection of defects on their surfaces. A machine learning-based automatic surface defect detection method is proposed for surface defects with many types and small sizes. A dark-field micro-scattering imaging system is used to acquire dark-field images. Then the image segmentation and feature analysis are performed on each image to extract the original feature data, and the original feature data are compressed by reliefF algorithm. Based on the compressed feature data, a classification model is built using a support vector machine. The detection method can classify three kinds of defects, namely, dig, scratch, and scuff mark. The accuracy of the method is up to 98%. The experimental results of this study show that the method can automatically and accurately detect a wide range of micro defects occurring in small-sized optics, thus providing valuable insights into the realization of mass production of small-sized optics. In addition, our proposed method provides some ideas for further research in the field of defect detection in small-sized optics.

Analog Front-End for the Readout of LGAD-Based Particle Detectors

This paper presents an analog front-end channel for the readout of Low Gain Avalanche Diodes (LGADs) based particle detectors for the next generation of space-borne experiments. The circuit has been designed in a 65 nm CMOS technology. The charge generated by the detector is integrated by a Charge Sensitive Amplifier (CSA) with dynamic signal compression feature to achieve improved noise performance, along with enhanced time resolution capabilities over the wide range of input charge to be detected (from 45fC to 16pC). An RC-CR shaper with selectable shaping time, a fast discriminator and a Time-to-Amplitude Converter (TAC) complete the readout circuit. An SNR around 300, together with a time resolution of slightly below 60ps, is achieved in post-layout simulations.

Deep Neural Network with a Characteristic Analysis for Seal Stroke Recognition

Seal characters are derived from ancient Chinese pictographs, naturally inheriting pictographic characteristics and complex structures. As the essential components of seal characters, seal strokes play a vital role in seal character recognition, composition and writing, so accurate recognition of seal strokes can greatly promote the investigation of seal characters. Inspired by curve fitting, we propose a new model called the characteristic analysis neural network (CANN) for seal stroke recognition. Instead of indiscriminate grasping of feature information in regular neural networks, we design an efficient approximation technique based on the piecewise Bezier curves that can effectively facilitate structural compression and lossless feature extraction. The feature extraction capability of Bezier approximation helps the methodology achieve impressive recognition accuracy not only on the seal strokes but also on any curve-based symbols. Furthermore, the hierarchical structure of the deep learning strategy is inherited and improved for better performance with high generalisation. Experiments conducted on different types of strokes verify that CANN obtains superior performance on both seal strokes and other smooth symbols. The robustness and the effectiveness of CANN are also demonstrated with minimal learning cost compared to other state-of-art models.

Radio Signature of the Strong Compression between a Streamer and a Coronal Hole Boundary

We present evidence of the first detection of the radio signature at metric wavelengths of the strong compression between a helmet streamer (HS) and the boundary of a coronal hole (CH) using radio observations from the Callisto MEXICO-LANCE and ALASKA-HAARP systems and white-light observations obtained by the STEREO-A/COR1-COR2 coronagraphs. The event occurred very close to the Sun (∼3.4 solar radii) and produced an intense and unusually broad drifting radio feature at metric wavelengths after a downward-drifting band of emission related to a metric Type II radio burst. The compression is caused by the interaction between an expanding structure (coronal mass ejection/shock) and the HS against the CH boundary. Observations in white light show a sharp compressive feature that propagates radially outward, while STEREO-A/EUVI images show loop oscillations at the same position angle, indicating that the interaction occurs across a range of heights. The loop oscillations cease when the compressive front loses its sharp boundary. This transition indicates a reduction of the density compression at the front and the cessation of the radio emission.

Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients.

A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models.

A SoC-FPGA based readout platform for the free-running AMBER data acquisition system

Owing to a change in the scope of physics research, the AMBER spectrometer at CERN is undergoing an update on its instrumentation and trigger strategy for the data acquisition system. One of the key updates is the adoption of a free-running and trigger-less operation, which differs from the predecessor of AMBER, COMPASS, by working in a continuous mode but in an event-based triggered way.In this article, we present a multichannel data-acquisition platform developed in line with the next generation of trigger-less and free-running data acquisition systems. The platform is based on a pre-existing Mezzanine Sampling ADC board for the analog-to-digital conversion and a Xilinx Zynq Ultrascale+ System on Module for online real-time data processing. We also present the evaluation of the system operating in continuous mode, taking data from a 25-element electromagnetic calorimeter prototype with a muon beam, during the first AMBER pilot run. The acquired data were used for noise and pulse shape studies required for the design of the algorithms for lossless compression and data feature extraction needed for trigger-less operation.

A New Dual-Branch Embedded Multivariate Attention Network for Hyperspectral Remote Sensing Classification

With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications.

ICC-BiFormer: A Deep-Learning Model for Near-Earth Asteroid Detection via Image Compression and Local Feature Extraction

Detecting near-Earth asteroids (NEAs) is crucial for research in solar system and planetary science. In recent year, deep-learning methods have almost dominated the task. Since NEAs represent only one-thousandth of the pixels in images, we proposed an ICC-BiFormer model that includes an image compression and contrast enhancement block and a BiFormer model to capture local features in input images, which is different from previous models based on Convolutional Neural Network (CNN). Furthermore, we utilize a larger input size of the model, which corresponds to the side length of the input image matrix, and design a cropping algorithm to prevent NEAs from being truncated and better divide NEAs and satellites. We apply our ICC-BiFormer model into a dataset of approximately 20,000 streak and 40,000 non-streak images to train a binary classification model. The ICC-BiFormer achieves 99.88% accuracy, which is superior to existing models. Focusing on local features has been proven effective in detecting NEAs.

Nondestructive evaluation of harvested cabbage texture quality using 3D scanning technology

Current nondestructive methods for evaluating the texture quality of leafy vegetables have limitations due to their complicated leafy structure. In this study, a promising solution was proposed using 3D scanning technology to nondestructively assess the texture quality of leafy vegetables, and the harvested cabbages were chosen as the experimental samples. The cabbages were scanned to extract the morphological traits, especially for surface features of vein distribution. Results demonstrated that morphological traits exhibited better correlations with texture indices compared to traditional compression features. Texture indices were well predicted based on the XGBoostR algorithm with high R2 values of over 0.89 and low RMSE values. The texture quality of cabbages at different harvesting times analyzed by linear discrimination analysis also showed well-discriminative results with an accuracy exceeding 98.3%. These results successfully indicated that 3D scanning technology was effective in evaluating the texture quality of cabbages, showcasing its potential in the nondestructive texture evaluation of leafy vegetables.