Dimensionality Reduction Techniques Research Articles

An optimised scattering power decomposition method oriented to ship detection in polarimetric synthetic aperture radar imagery

AbstractThis paper describes an optimised scattering power decomposition method suitable for ship detection in polarimetric synthetic aperture radar imagery. Based on the principal component analysis technique for dimension reduction and the scattering power decompositions with strong physical interpretation, a novel optimised scattering power decomposition model is proposed which comprises surface scattering, double‐bounce scattering, ±45° oriented dipole, and volume scattering components. Wherein, the oriented dipole component can be used to characterise the compounded scattering of the target which is composed of even‐bounce and odd‐bounce reflectors. Furthermore, pocket algorithm and support vector machine are adopted to solve linear non‐separable problems under complex experimental conditions in this study. Extensive experiments carried out on RADARSAT‐2 data and NASA/JPL AIRSAR data show that the oriented dipole component significantly contributes to the optimised four‐component decomposition method and can play a vital role in ship detection. Compared to other scattering power decompositions, the optimised decomposition method not only achieves better performance but is more in accordance with the real scattering mechanisms, which is more applicable to ship detection.

Near-optimal quantum kernel principal component analysis

Abstract Kernel principal component analysis (kernel PCA) is a nonlinear dimensionality reduction technique that employs kernel functions to map data into a high-dimensional feature space, thereby extending the applicability of linear PCA to nonlinear data and facilitating the extraction of informative principal components. However, kernel PCA necessitates the manipulation of large-scale matrices, leading to high computational complexity and posing challenges for efficient implementation in big data environments. Quantum computing has recently been integrated with kernel methods in machine learning, enabling effective analysis of input data within intractable feature spaces. Although existing quantum kernel PCA proposals promise exponential speedups, they impose stringent requirements on quantum hardware that are challenging to fulfill. In this work, we propose a quantum algorithm for kernel PCA by establishing a connection between quantum kernel methods and block encoding, thereby diagonalizing the centralized kernel matrix on a quantum computer. The query complexity is logarithmic with respect to the size of the data vector, $D$, and linear with respect to the size of the dataset. An exponential speedup could be achieved when the dataset consists of a few high-dimensional vectors, wherein the dataset size is polynomial in $\log(D)$, with $D$ being significantly large. In contrast to existing work, our algorithm enhances the efficiency of quantum kernel PCA and reduces the requirements for quantum hardware. Furthermore, we have also demonstrated that the algorithm based on block encoding matches the lower bound of query complexity, indicating that our algorithm is nearly optimal. Our research has laid down new pathways for developing quantum machine learning algorithms aimed at addressing tangible real-world problems and demonstrating quantum advantages within machine learning.

Analysis of behavioral flow resolves latent phenotypes.

The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal's behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice-including stress exposures, pharmacological and brain circuit interventions-to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal's future behavior.

Entropy of Neuronal Spike Patterns

Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes. Here, we review basic entropy metrics and then we provide examples of recent advancements in using entropy as a tool to improve our understanding of neuronal processing. It focuses especially on studies on critical dynamics in neural networks and the relation of entropy to predictive coding and cortical communication. We highlight the necessity of expanding entropy measures from single neurons to encompass multi-neuronal activity patterns, as cortical circuits communicate through coordinated spatiotemporal activity patterns, called neuronal packets. We discuss how the sequential and partially stereotypical nature of neuronal packets influences the entropy of cortical communication. Stereotypy reduces entropy by enhancing reliability and predictability in neural signaling, while variability within packets increases entropy, allowing for greater information capacity. This balance between stereotypy and variability supports both robustness and flexibility in cortical information processing. We also review challenges in applying entropy to analyze such spatiotemporal neuronal spike patterns, notably, the “curse of dimensionality” in estimating entropy for high-dimensional neuronal data. Finally, we discuss strategies to overcome these challenges, including dimensionality reduction techniques, advanced entropy estimators, sparse coding schemes, and the integration of machine learning approaches. Thus, this work summarizes the most recent developments on how entropy measures contribute to our understanding of principles underlying neural coding.

Research on oil and gas pipeline leak detection method based on 1DCNN-DBO-LSTM

Abstract Detecting leaks in oil and gas pipelines is crucial for the safe operation of these systems. Current techniques for identifying oil and gas pipeline breaches are insufficient and fail to utilize the temporal attributes of the leak signal. This research proposes a fusion recognition model that integrates a long short-term memory network (LSTM) optimized using a one-dimensional convolutional neural network (1DCNN) with the dung beetle optimization algorithm (DBO). The model initially tackles the issue of excessive depth in the 1DCNN network, which paradoxically results in decreased accuracy and increased computational load. It develops a lightweight 1DCNN network via structural experiments that adaptively extracts data features. Subsequently, to mitigate the effects of the LSTM model's high stochasticity and the challenges associated with manual hyperparameter selection on detection accuracy, an enhanced LSTM model is proposed. This model is optimized through the integration of the Dung Beetle Optimization Algorithm (DBO) to refine the hyperparameters in LSTM. A 1DCNN-DBO-LSTM recognition model is developed, whereby the data features retrieved adaptively from the 1DCNN are fed into the enhanced DBO-LSTM for classification, and the T-SNE dimensionality reduction technique is utilized to visualize the network at each output layer. The model is assessed utilizing the precision and duration measures. The suggested model enhances recognition accuracy and reduces detection time compared to existing advanced models. The approach presented in this work can extract pipeline data features more swiftly and accurately, thus enhancing classification precision.

A Comprehensive Approach to Fault Classification of Helicopter Engines with Adaboost Ensemble Model

This work is based on the PHM North America 2024 Conference Data Challenge’s datasets of Helicopter turbine engine performance measurements. These datasets were large and moderately imbalanced. For dealing with these challenges, we demonstrate significant tools covering feature engineering, augmentation and selection, model exploration, visualizations, model explainability and confidence margin estimation. This work was performed in its entirety using MATLAB. All these tools will be generally applicable to data-driven modeling and prediction of health to real life applications. Initially, we explored the 742k observations in the training set, noting a 60-40 split between healthy and faulty labels, and identified two major operational clusters within the data. We enhanced the dataset by removing duplicates and engineered new features based on domain knowledge, expanding the feature set to 242 dimensions. However for the torque margin estimation, we trained a regression model on a limited subset (18 features), which includes engineered features using domain knowledge, quadratic terms and linear interaction between all the terms. For the final submission, we utilized a stepwise linear regression model to optimize feature selection. This approach achieved a perfect regression score on test data, validated by a consistent torque margin residual range of +/- 0.5%. The model's RMSE and MAE metrics were optimal for employing a normal distribution probability density function. For , we reduced the feature set to 58 using dimensionality reduction techniques and balanced the data with upsampling and down-weighing the minority class. We employed ASHA (Asynchronous Successive Halving Algorithm) in conjunction with AutoML to efficiently determine the most suitable model family, significantly saving compute time. Subsequently, we trained ensemble models, including bagged tree and AdaBoost (Adaptive Boosting), which minimized false negatives and positives, achieving robust classification performance. This was particularly critical given the high penalty for false negatives in the data challenge. The MathWorks team score on Testing Data was 0.9686 at the close of competition. This was further improved to 0.9867. Our approach demonstrates the effectiveness of combining strategic data processing, feature engineering, and model selection to enhance predictive accuracy in complex operational datasets.

RlaNet: A Residual Convolution Nested Long-Short-Term Memory Model with an Attention Mechanism for Wind Turbine Fault Diagnosis

This paper proposes a new fault diagnosis model for wind power systems called residual convolution nested long short-term memory network with an attention mechanism (rlaNet). The method first preprocesses the SCADA data through feature engineering, uses the Hermite interpolation method to handle missing data, and uses the mutual information-based dimensionality reduction technique to improve data quality and eliminate redundant information. rlaNet combines residual networks and nested long short-term memory networks to replace traditional convolutional neural networks and standard long short-term memory architectures, thereby improving feature extraction and ensuring the abstractness and depth of the extracted features. In addition, the model emphasizes the weighted learning of spatiotemporal features in the input data, enhances the focus on key features, and improves training efficiency. Experimental results show that rlaNet achieves an accuracy of more than 90% in wind turbine fault diagnosis, showing good robustness. Furthermore, noise simulation experiments verify the model’s resistance to interference, providing a reliable solution for wind turbine fault diagnosis under complex operating conditions.

Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient

In the current age of chemical science, chemical graph theory has significantly advanced our understanding of the characteristics of chemical compounds. To simulate the mathematical, chemical, and physical aspects of networks, a topological index, a numerical measure obtained from the graph of a chemical network, employed. Recent work has explored the topological properties of boron oxide using chemical graph theory. In this work, we conduct a Pearson correlation analysis of boron oxide to assess the correlations between the Van and S indices and entropy metrics. We analyze the Pearson correlation coefficients between the entropy values and the calculated indices using a heatmap. In this article, a significant positive correlation between the Van, and S indices, and entropy values, which is represented by the heatmap of the strong linear correlations. To avoid duplication, a dimensionality reduction technique should be used for highly connected variables. Additionally, this study gives a detailed explanation of the link between the indices and entropy, which will form the basis of further statistical investigations.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ERROR PREVENTION AND QUALITY CONTROL IN MIG/MAG WELDING PROCESSES: A COMPREHENSIVE REVIEW

Welding techniques, particularly MIG/MAG (Metal Inert Gas/Metal Active Gas) processes, are essential for various industries, including automotive, construction, aerospace, and defense. Ensuring the quality and accuracy of welds is crucial for efficient production and product reliability. However, numerous issues can arise during MIG/MAG welding, often resulting in suboptimal quality. This study presents a comprehensive review of research efforts employing AI techniques to address errors and defects in MIG/MAG welding. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets employed, methodologies and approaches adopted, and performance metrics reported. The findings reveal a significant adoption of both machine learning and deep learning techniques, with the choice of approach dependent on factors such as the nature of input data, welding process dynamics, and computational requirements. Deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated superior performance in tasks like image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often in conjunction with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE), with the selection contingent upon the specific task (classification or regression) and desired trade-off between different performance aspects. While many studies have reported promising results, with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings, where factors such as noise, occlusions, and rapidly changing welding conditions can pose significant hurdles. This review serves as a comprehensive guide for researchers and practitioners working in the field of AI-assisted error prevention and quality control for MIG/MAG welding processes, highlighting current trends, methodologies, and future research directions.

Sparse discriminant manifold projections for automatic depression recognition

In recent years, depression has become an increasingly serious problem globally. Previous research have shown that EEG-based depression recognition is a promising technique to serve as auxiliary diagnosis methods that provide assistance to clinicians. Typically, in clinical studies, due to the multichannel nature of EEG, the extracted features usually are high-dimensional and contain many redundant information. Therefore, it is necessary to perform dimensionality reduction before classification to improve the performance of machine learning algorithms. However,existing dimensionality reduction techniques do not design the objective function based on the characteristics of EEG signal and the goal of depression recognition, so they are less suitable for dimensionality reduction of EEG features. To solve this problem, in this paper we propose a novel dimensionality reduction technique called sparse discriminant manifold projections(SDMP) for depression recognition. Specifically, the use of the ℓ2-norm instead of the squared ℓ2-norm as a similarity measure in the objective function reduces sensitivity to noise and outliers. Moreover, the local geometric structure and global discriminative properties of data are integrated, which makes the extracted features more discriminative. Finally, the ℓ2,1-norm regularization is introduced to achieve feature selection. Furthermore, The formulation is extended to the ℓ2,p-norm regularization case, which is more likely to offer better sparsity when 0<p<1. Extensive experiments on EEG data show that the SDMP achieves the competitive performance compared with other state-of-the-art dimensionality reduction methods. It also shows the practical application value of our method in detecting depression.

Toward aerodynamic surrogate modeling based on β-variational autoencoders

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using β-variational autoencoder (β-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The β-VAE model, enhanced with principal component analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through β requires careful tuning to improve overall performance, while PCA preprocessing helps to construct an effective latent space, improving autoencoder training and performance. Gaussian process regression is used to predict latent space variables from flight conditions, showing robust behavior independent of β, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Furthermore, a fine-tuning process of the decoder further refines the model, reducing the dependence on β and enhancing accuracy. Structured latent space, robust regression performance, and significant improvements in fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of β-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction.

Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search.

Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sparked significant interest since the abnormal functional connectivity is discovered in certain functional magnetic resonance imaging (fMRI)-based brain regions compared to typical developmental control (TC) individuals. However, existing models for modeling FBNs generally use dimensionality reduction techniques to process the high dimensional input data, which results in confusion and an inaccurate representation of voxel interactions between spatially close brain regions, causing misdiagnosis of the disease. To address these issues, we propose a spatial preservation-based neural architecture search (SP-NAS) for FBNs modeling in ADHD. The main work includes three-fold: 1) A spatial preservation module is designed to embed original spatial information into dimensionality reduction data, addressing the challenge of a large number of parameters in the original data and mitigating disease misdiagnosis resulting from voxel confusion between different brain regions caused by dimensionality reduction. 2) A search space using more suitable search operations is constructed to efficiently extract spatial-temporal interaction characteristics of fMRI data in ADHD while narrowing the search space. 3) Cross-regional association differences between ADHD and TC groups are explored for ADHD auxiliary diagnosis since the abnormal activation regions of ADHD relative to TC on the brain regions and the abnormal connectivity between the lesion brain regions are identified. Model validation results on the ADHD-200 dataset show that the FBNs obtained from SP-NAS not only achieve competitive results in ADHD diagnosis but also reveal abnormal connections in the lesion regions of ADHD consistent with clinical diagnosis.

Enhancing Skin Cancer Diagnosis through the Integration of Deep Learning and Machine Learning Approaches

Skin cancer is a disease characterized by the uncontrolled proliferation of skin cells, typically manifesting as lesions or abnormal growths. Early diagnosis is critical for improving treatment outcomes. This study proposes an innovative approach to skin cancer diagnosis by integrating modern deep learning models with traditional machine learning algorithms. A three-phase methodology was developed. In the first phase, meaningful features were extracted from skin lesion images using various transfer learning models, including Xception, VGG16, ResNet152V2, InceptionV3, InceptionResNetV2, MobileNetV2, EfficientNetB2, and DenseNet201. In the second phase, dimensionality reduction was performed using Principal Component Analysis (PCA). In the final phase, the reduced feature sets were classified using K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms. Experimental results demonstrated that the highest accuracy of 91.28% was achieved through the combination of DenseNet201 for feature extraction, PCA for dimensionality reduction, and Random Forest for classification. These findings highlight the effectiveness of integrating transfer learning models, dimensionality reduction techniques, and machine learning algorithms in enhancing the accuracy of skin cancer diagnosis.

Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke

This study employs machine learning and single-cell transcriptome sequencing (scRNA-seq) analysis to unearth novel biomarkers and delineate the immune characteristics of ischemic stroke (IS), thereby contributing fresh insights into IS treatment strategies.Our research leverages gene expression data sourced from the GEO database. We undertake weighted gene co-expression network analysis (WGCNA) to filter pertinent genes and subsequently employ machine learning algorithms for the identification of feature genes. Concurrently, we rigorously execute quality control measures, dimensionality reduction techniques, and cell annotation on the scRNA-seq data to pinpoint differentially expressed genes (DEGs). The identification of core genes, denoted as Hub genes, among the feature genes and DEGs, is achieved through meticulous overlapping analysis. We illuminate the immune characteristics of these Hub genes using a suite of analytical tools, encompassing CIBERSORT, MCPcounter, and pseudotemporal analysis, all based on immune cell annotations and single-cell transcriptome data.Subsequently, we harness the CMap database to prognosticate potential therapeutic drugs and scrutinize their associations with the identified Hub genes. Our findings unveil robust linkages between three pivotal Hub genes—namely, RNF13, VASP, and CD163—and specific immune cell types such as T cells and neutrophils. These Hub genes predominantly manifest in macrophages and microglial cells within the scRNA-seq immune cell population, exhibiting variances across different stages of cellular differentiation. In conclusion, this study unearths highly pertinent biomarkers for IS diagnosis and elucidates IS-induced immune infiltration characteristics, thus providing a firm foundation for a comprehensive exploration of potential immune mechanisms and the identification of novel therapeutic targets for IS.

Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques

In the field of medical imaging, accurately classifying brain tumors remains a significant challenge because of the visual similarities among different tumor types. This research addresses the challenge of multiclass categorization by employing Support Vector Machine (SVM) as the core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP), as well as the dimensionality reduction technique, Principal Component Analysis (PCA). The study utilizes a dataset sourced from Kaggle, comprising MRI images classified into four classes, with images captured from various anatomical planes. Initially, the SVM model alone attained an accuracy(acc_val) of 86.57% on unseen test data, establishing a baseline for performance. To enhance this, PCA was incorporated for dimensionality reduction, which improved the acc_val to 94.20%, demonstrating the effectiveness of reducing feature dimensionality in mitigating overfitting and enhancing model generalization. Further performance gains were realized by applying feature extraction techniques—HOG and LBP—in conjunction with SVM, resulting in an acc_val of 95.95%. The most substantial improvement was observed when combining SVM with both HOG, LBP, and PCA, achieving an impressive acc_val of 96.03%, along with an F1 score(F1_val) of 96.00%, precision(prec_val) of 96.02%, and recall(rec_val) of 96.03%. This approach will not only improves categorization performance but also improves efficacy of computation, making it a robust and effective method for multiclass brain tumor prediction.

Integrating canonical correlation analysis with machine learning for power quality disturbance classification

Abstract Recently, the rapid growth of Renewable Energy Resources (RER) in power generation has resulted in the frequent occurrence of Power Quality Disturbances (PQDs) within the power system. The timely and accurate detection of these PQDs is critical for maintaining good power quality while integrating RER into hybrid power systems to make them more robust and stable. In this paper, a multi-view dimensionality reduction approach based on Canonical Correlation Analysis (CCA) is proposed to differentiate different types of PQDs. Here, a dataset of 29 types of PQDs which include nine single types and twenty multiple types of PQDs have been generated using their mathematical model in MATLAB for experimentation. CCA being a multi-view dimensionality reduction technique maximizes the correlation between two different views of the data. Here two cases of datasets have been considered for further exploration, Case 1: PQDs without noise and with 20 dB noise, Case 2: PQDs with 20 dB and 30 dB noise. Furthermore, to test the efficacy of CCA in both cases, the extracted features have been tested using four different classifiers, i.e., K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF). The performance of each of the classifiers has been tested on five different performance metrics such as precision, recall, F1 score, hamming loss, and accuracy, and the results show that the proposed technique of multi-view dimensionality reduction is capable of classifying the PQDs with two different views at a time.

Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments.

Optimizing Machine Learning-Based Intrusion Detection Systems for Enhanced Security in Cloud Computing Environments.

In today’s cloud computing environments, robust network security is crucial due to the inherently distributed and dynamic nature of cloud systems. Traditional Network Intrusion Detection Systems (NIDS) often face challenges in handling large-scale, rapidly evolving data and sophisticated attack vectors unique to cloud settings. This paper presents an optimized Machine Learning (ML)-based NIDS framework designed specifically for cloud infrastructures, leveraging feature selection, dimensionality reduction, and algorithmic tuning techniques to enhance detection accuracy and reduce false positives. Integrating supervised and unsupervised learning methods, this NIDS system can detect both known and novel attack types efficiently. By utilizing cloud-specific datasets and real-time traffic monitoring, it adapts well to the scalability and elasticity requirements of cloud environments. Additionally, the system incorporates automated optimization techniques, such as hyperparameter tuning and ensemble learning, to further enhance performance. Experimental results show improvements in detection rates, reduced computational overhead, and better scalability, making it a promising solution for modern cloud-based infrastructures.

A universal hippocampal memory code across animals and environments.

How learning is affected by context is a fundamental question of neuroscience, as the ability to generalize learning to different contexts is necessary for navigating the world. An example of swift contextual generalization is observed in conditioning tasks, where performance is quickly generalized from one context to another. A key question in identifying the neural substrate underlying this ability is how the hippocampus (HPC) represents task-related stimuli across different environments, given that HPC cells exhibit place-specific activity that changes across contexts (remapping). In this study, we used calcium imaging to monitor hippocampal neuron activity as animals performed a conditioning task across multiple spatial contexts. We investigated whether hippocampal cells, which encode both spatial locations (place cells) and task-related information, could maintain their task representation even when their spatial encoding remapped in a new spatial context. To assess the consistency of task representations, we used advanced dimensionality reduction techniques combined with machine learning to develop manifold representations of population level HPC activity. The results showed that task-related neural representations remained stable even as place cell representations of spatial context changed, thus demonstrating similar embedding geometries of neural representations of the task across different spatial contexts. Notably, these patterns were not only consistent within the same animal across different contexts but also significantly similar across different animals, suggesting a standardized neural encoding or 'neural syntax' in the hippocampus. These findings bridge a critical gap between memory and navigation research, revealing how the hippocampus maintains cognitive consistency across different spatial environments. These findings also suggest that hippocampal function is governed by a neural framework shared between animals, an observation that may have broad implications for understanding memory, learning, and related cognitive processes. Looking ahead, this work opens new avenues for exploring the fundamental principles underlying hippocampal encoding strategies.

A Review of Artificial Intelligence-Based Dyslexia Detection Techniques.

Dyslexia is a learning disorder affecting an individual's ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or incorrect diagnosis. Timely identification is essential to provide personalized treatment and improve the individual's quality of life. The development of artificial intelligence techniques offers a platform to identify dyslexia using behavior and neuroimaging data. However, the limited datasets and black-box nature of ML models reduce the generalizability and interpretability of dyslexia detection (DD) models. The dimensionality reduction technique (DRT) plays a significant role in providing dyslexia features to enhance the performance of machine learning (ML)- and deep learning (DL)-based DD techniques. In this review, the authors intend to investigate the role of DRTs in enhancing the performance of ML- and DL-based DD models. The authors conducted a comprehensive search across multiple digital libraries, including Scopus, Web of Science, PubMed, and IEEEXplore, to identify articles associated with DRTs in identifying dyslexia. They extracted 479 articles using these digital libraries. After an extensive screening procedure, a total of 39 articles were included in this review. The review findings revealed various DRTs for identifying critical dyslexia patterns from multiple modalities. A significant number of studies employed principal component analysis (PCA) for feature extraction and selection. The authors presented the essential features associated with DD. In addition, they outlined the challenges and limitations of existing DRTs. The authors emphasized the need for the development of novel DRTs and their seamless integration with advanced DL techniques for robust and interpretable DD models.