Linearly Separable Research Articles

GWECS: An indicator to describe the energy absorption characteristics of wave energy converter arrays in real sea states

Grouping wave energy converters (WECs) into an array has become a trend to improve power production and achieve economies of scale. Therefore, the performance of a WEC array in real sea states is of vital importance. To intuitively reflect an array's frequency-based energy absorption characteristics instead of only the total extractable power in given irregular wave conditions, a new indicator of group wave energy capture spectrum (GWECS) is proposed by extending the concept of wave energy capture spectrum (WECS) to an array. A practical method to measure the indicator by estimating the absorbed wave surface from the array's power signal is also given. Simulations in two-dimensional conditions with a 2-cuboid WEC array and in three-dimensional conditions with a 2-cylinder WEC array are conducted to study the concept. The results examine the consistency in capture efficiency between the newly proposed indicator and conventional ones, and present the effect of various parameters, including linear power take-off (PTO) damping, buoy permutation, separation distance, and incident wave angle on the GWECS.

An Interpretable Deep-Stacked TSK Fuzzy Classifier for High-Dimensional Problems

This study proposes a novel deep-stacked fully interpretable and more generalization Takagi-Sugeno-Kang fuzzy system (D-FIMG-TSK) to obtain short fuzzy rules with high interpretability in each layer and simultaneously earn more generalization capability for high-dimensional data classification tasks. With the help of both discriminative and residual information, D-FIMG-TSK combines several fully interpretable TSK classifier FIMG-TSK in a stacked manner to conveniently move apart the manifolds existing in the original data space to achieve better linear separability. Besides, a feature selection method is designed to make feature selection from original feature sets on all layers to guarantee the classification performances. On the training process of D-FIMG-TSK, the importance of all original features and the antecedent and consequent parts of all fuzzy rules in each FIMG-TSK can be determined at the same time. The effectiveness of D-FIMG-TSK is manifested by the experimental results on eight binary high-dimensional classification datasets.

Separation and Release Devices for Aeronautical and Astronautical Systems: A Review

AbstractThis review presents an in-depth examination of the advancements, current state, and future trends of separation and release devices in aeronautical and astronautical systems, focusing on their classification into one-point and linear devices as well as the move from pyrotechnic to nonexplosive methods. For one-point separation devices, this review discusses the evolution from traditional explosive bolts, which use high explosives to operate, to nonexplosive actuators such as shape memory alloy devices that offer resettable, low-shock options. It also highlights devices that rely on pyrotechnic initiators without using high explosives as key developments for reducing shock and increasing safety. In the section on linear separation devices, the review examines flexible linear shaped charges and mild-detonating fuses, which are crucial for precise separations such as stage and fairing detachments. Introducing expanding tubes as a new method to avoid the usual debris and damage from pyrotechnics is a big improvement. In addition, the use of clamp-band joint systems for satellite separation, secured by either pyrotechnic bolts or nonexplosive dual-initiator devices, is a common approach. The recent focus on developing pneumatic linear separation devices marks a shift toward systems that can be reused and reduce shock without explosives. Moreover, the emergence of nonexplosive satellite separation systems represents a shift toward safer and more adaptable deployment technologies. These fully mechanical actuation methods facilitate shock-free separations, which are crucial for the integrity of sensitive payloads. This review suggests that continuous innovation in nonexplosive separation technology will significantly change mission planning and system design in the aeronautics and astronautics fields.

Fuzzy Clustering Guided by Spectral Rotation and Scaling.

Representing data in different spaces becomes more powerful and suitable for solving downstream learning tasks. The membership degrees obtained through fuzzy C -means (FCM) clustering cannot capture data structures sufficiently, as they represent samples from a single Euclidean geometrical perspective. To address this issue, we propose a novel fuzzy clustering model guided by spectral rotation and scaling (FCSR). In FCSR, both spectral embeddings and membership degrees are considered as new representations of data. They can complement each other from different perspectives which enables the model to engage more structural properties of the data. The process of solving the problem of membership degrees not only inherits the merits of traditional FCM but also preserves data neighborhood structures revealed by the spectral decomposition based on an affinity matrix. Furthermore, to improve the adaptability and extensibility of FCSR, the projected and kernel versions of FCSR (FCSR-P and FCSR-K) are formed. We demonstrate that FCSR-P is suitable for high-dimensional scenarios and FCSR-K can improve the linear separability among data. Extensive experiments conducted on various well-known data sets illustrate the validity of the proposed ideas.

Serum miRNA improves the accuracy of a multivariate index assay for triage of an adnexal mass

ObjectiveTo determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass. MethodsSerum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls). ResultsThe total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81–0.95) and external validation sets (AUC = 0.95; 95% CI 0.90–0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers. ConclusionsA multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.

Constructing neural networks with pre-specified dynamics

A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm—termed generalised Firing-to-Parameter (gFTP)—provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.

Development of a comprehensive framework for quantifying the collective and individual influence of climate change and human activities on hydrological regimes

Previous research rarely identified the synergistic effects of climate change and human activities on hydrological regimes, thus leading to an incomplete attribution of hydrological regime changes. To address this issue, this paper presents a complete approach for accurately quantifying these impacts. The framework integrates various methodologies, including the runoff abrupt change point judgment method for distinguishing pre- and post-impact periods, The Soil and Water Assessment Tool (SWAT) is used to isolate runoff processes influenced solely by climate change, the linear separation method for identifying runoff processes affected solely by human activities, and the Indicator of Hydrologic Alteration (IHA) for quantifying changes in hydrological regimes. Application of this framework to the Ganjiang River Basin (GRB) unveils significant alterations in hydrological regimes. This has resulted in a significant decline in aquatic organism species compared to the prior-impact period levels. Notably, climate change and human activities exert opposing effects on minimum flow indicators and flow pulse indicators in the GRB. Moreover, these factors exhibit a substantial synergistic effect on numerous indicators, resulting in hydrological regimes combined influenced by climate change and human activities being considerably different with the cumulative impact on hydrological regimes solely influenced by climate change and those solely influenced by human activities. Consequently, in future water management, it is crucial to recognize the positive role of prudent human activities in mitigating the adverse impacts of climate change.

Spontaneous path tracing in task-irrelevant mazes: Spatial affordances trigger dynamic visual routines.

Given a maze (e.g., in a book of puzzles), you might solve it by drawing out paths with your pencil. But even without a pencil, you might naturally find yourself mentally tracing along various paths. This "mental path tracing" may intuitively seem to depend on your (overt, conscious, voluntary) goal of wanting to get out of the maze, but might it also occur spontaneously-as a result of simply seeing the maze, via a kind of dynamic visual routine? Here, observers simply had to compare the visual properties of two probes presented in a maze. The maze itself was entirely task irrelevant, but we predicted that simply seeing the maze's visual structure would "afford" incidental mental path tracing (à la Gibson). Across four experiments, observers were slower to compare probes that were further from each other along the paths, even when controlling for lower level properties (such as the probes' brute linear separation, ignoring the maze "walls"). These results also generalized beyond mazes to other unfamiliar displays with task-irrelevant circular obstacles. This novel combination of two prominent themes from our field-affordances and visual routines-suggests that at least some visual routines may not require voluntary goals; instead, they may operate in an automatic (incidental, stimulus-driven) fashion, as a part of visual processing itself. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

The classification of B cell lymphomas-mainly based on light microscopy evaluation by a pathologist-requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples-nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)-alongside with 291 control samples. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL-that has a dominant background of non-malignant bystander cells-a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 samples from all three lymphoma entities and 58 samples from healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model.

Forward-backward wavefront manipulation of orthogonal linearly polarized waves based on a metasurface zone plate.

Metasurface zone plates exhibit stronger optical control capabilities than traditional Fresnel zone plates, especially in polarization transformation and multiplexing. However, there are still few studies on metasurface zone plates that can be used for simultaneous control of forward and backward waves. In this work, we propose what is to our knowledge a new scheme that utilizes metasurface zone plates for orthogonal linear polarization separation and wavefront manipulation at the same time. We demonstrate the separation of linearly polarized components and transmission-reflection focusing by using the destructive and constructive interference between different meta-atoms in the super-cell, as well as the phase difference between the super-cells. The metasurface not only needs a simple binary phase design but also shows a working bandwidth more than 30 nm with a central wavelength of 875 nm. This scheme can be extended to other electromagnetic bands such as visible and terahertz ones, providing an important way for the multi-dimensional light field manipulations.

A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example.

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

Ensemble One-Class Support Vector Machine for Sea Surface Target Detection Based on k-Means Clustering

Sea surface target detection is a key stage in a typical target detection system and directly influences the performance of the whole system. As an effective discriminator, the one-class support vector machine (OCSVM) has been widely used in target detection. In OCSCM, training samples are first mapped to the hypersphere in the kernel space with the Gaussian kernel function, and then, a linear classification hyperplane is constructed in each cluster to separate target samples from other classes of samples. However, when the distribution of the original data is complex, the transformed data in the kernel space may be nonlinearly separable. In this situation, OCSVM cannot classify the data correctly, because only a linear hyperplane is constructed in the kernel space. To solve this problem, a novel one-class classification algorithm, referred to as ensemble one-class support vector machine (En-OCSVM), is proposed in this paper. En-OCSVM is a hybrid model based on k-means clustering and OCSVM. In En-OCSVM, training samples are clustered in the kernel space with the k-means clustering algorithm, while a linear decision hyperplane is constructed in each cluster. With the combination of multiple linear classification hyperplanes, a complex nonlinear classification boundary can be achieved in the kernel space. Moreover, the joint optimization of the k-means clustering model and OCSVM model is realized in the proposed method, which ensures the linear separability of each cluster. The experimental results based on the synthetic dataset, benchmark datasets, IPIX datasets, and SAR real data demonstrate the better performance of our method over other related methods.

Coreference Resolution Based on High-Dimensional Multi-Scale Information.

Coreference resolution is a key task in Natural Language Processing. It is difficult to evaluate the similarity of long-span texts, which makes text-level encoding somewhat challenging. This paper first compares the impact of commonly used methods to improve the global information collection ability of the model on the BERT encoding performance. Based on this, a multi-scale context information module is designed to improve the applicability of the BERT encoding model under different text spans. In addition, improving linear separability through dimension expansion. Finally, cross-entropy loss is used as the loss function. After adding BERT and span BERT to the module designed in this article, F1 increased by 0.5% and 0.2%, respectively.

Enhancing Unsupervised Outlier Model Selection: A Study on IREOS Algorithms

Outlier detection stands as a critical cornerstone in the field of data mining, with a wide range of applications spanning from fraud detection to network security. However, real-world scenarios often lack labeled data for training, necessitating unsupervised outlier detection methods. This study centers on Unsupervised Outlier Model Selection (UOMS), with a specific focus on the family of Internal, Relative Evaluation of Outlier Solutions (IREOS) algorithms. IREOS measures outlier candidate separability by evaluating multiple maximum-margin classifiers and, while effective, it is constrained by its high computational demands. We investigate the impact of several different separation methods in UOMS in terms of ranking quality and runtime. Surprisingly, our findings indicate that different separability measures have minimal impact on IREOS’ effectiveness. However, using linear separation methods within IREOS significantly reduces its computation time. These insights hold significance for real-world applications where efficient outlier detection is critical. In the context of this work, we provide the code for the IREOS algorithm and our separability techniques.

SAR target recognition through adaptive kernel sparse representation model based on local contrast perception

Sparse representation-based methods have shown promising prospects in synthetic aperture radar (SAR) target recognition due to their robustness under complex application conditions. However, these methods are still susceptible to the feature representation of SAR images. In this paper, we propose a new SAR target recognition method using an adaptive kernel sparse representation model based on local contrast perception (LCP). The LCP method combines a specially designed random projection matrix with two-dimensional filtering to capture extensive intensity contrasts between local regions in SAR images. A response map reconstruction technique shows that LCP can obtain discriminative properties between target and background regions and improve feature representation. The LCP features are then transformed into the kernel representation to improve linear separability by defining a reproducing kernel Hilbert space (RKHS) and using a nonlinear kernel function. The kernel representation is utilized to construct a new kernel sparse representation model for classification. Furthermore, neighborhood analysis (NA) is proposed to establish the relationship between the parameter and feature distribution, with an adaptive regularization parameter selection strategy to mitigate the parameter sensitivity. Experiments conducted on the moving and stationary target acquisition and recognition dataset demonstrate the superiority and robustness of the proposed method under different application conditions.

A New Sufficient & Necessary Condition for Testing Linear Separability Between Two Sets.

As a fundamental mathematical problem in the field of machine learning, the linear separability test still lacks a theoretically complete and computationally efficient method. This paper proposes and proves a sufficient and necessary condition for linear separability test based on a sphere model. The advantage of this test method is two-fold: (1) it provides not only a qualitative test of linear separability but also a quantitative analysis of the separability of linear separable instances; (2) it has low time cost and is more efficient than existing test methods. The proposed method is validated through a large number of experiments on benchmark datasets and artificial datasets, demonstrating both its correctness and efficiency.

1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation; (2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability.

Quantum metric learning with fuzzy-informed learning

By constructing a quantum circuit with adjustable parameters, variational quantum circuits are capable of embedding input data into Hilbert space and facilitating the execution of complex deep learning tasks. Recently, a method known as quantum metric learning has emerged. It adaptively adjusts quantum embedding circuits to enable clusters of classical data from different categories to form linear separations in Hilbert space. This approach combines the advantages of kernel methods and is implementable on near-term quantum devices. However, applying quantum metric learning to more complex data presents challenges, largely due to the inherent fuzziness, uncertainty, and noise in the data itself and in the classical neural network preprocessing. To address these challenges, this paper proposes a fuzzy information-driven framework for quantum metric learning. This framework leverages fuzzy learning to accurately describe the uncertainty of data characteristics and adaptively integrates features, fuzziness, and uncertainty. Numerical experiments demonstrate that, compared to standard quantum metric learning, this framework achieves competitive performance and promising results. Furthermore, the interpretability provided by fuzzy logic aids in understanding the intrinsic mechanisms of the selected features during the quantum feature mapping process, offering new perspectives and approaches for better utilization of current quantum information processors.

Efficacy of Machine Learning Techniques in Diagnosing Urinary Tract Infections: A Study Utilizing a Philippine Clinical Dataset

This research delves into the potential of machine learning models, namely Support Vector Machine (SVM), XGBoost, and LightGBM, to enhance the diagnosis of Urinary Tract Infections (UTIs) based on a comprehensive dataset collected from a local clinic in Northern Mindanao, Philippines, spanning from April 2020 to January 2023. The study integrates clinical variables such as age, gender, and various urine test results including color, transparency, and the presence of substances like glucose, protein, and cells, to determine the most accurate diagnostic model. The dataset presented unique preprocessing challenges, such as converting infant ages into decimal numbers. The SVM with a linear kernel showed remarkable test accuracy of 98.25%, indicating its robustness in handling linear separability in the data. Meanwhile, XGBoost and LightGBM, both with optimal hyperparameter configurations, achieved comparable accuracies of 97.95%. These results underscore the significance of machine learning in medical diagnostics, particularly in settings where swift and reliable decision-making is crucial. Our findings suggest that while ensemble methods like XGBoost and LightGBM are powerful tools for complex datasets, a well-tuned SVM can provide superior accuracy, thus advocating for a data-centric approach in model selection.

Detection and Defense of Unlearnable Examples

Privacy preserving has become increasingly critical with the emergence of social media. Unlearnable examples have been proposed to avoid leaking personal information on the Internet by degrading the generalization abilities of deep learning models. However, our study reveals that unlearnable examples are easily detectable. We provide theoretical results on linear separability of certain unlearnable poisoned dataset and simple network-based detection methods that can identify all existing unlearnable examples, as demonstrated by extensive experiments. Detectability of unlearnable examples with simple networks motivates us to design a novel defense method. We propose using stronger data augmentations coupled with adversarial noises generated by simple networks, to degrade the detectability and thus provide effective defense against unlearnable examples with a lower cost. Adversarial training with large budgets is a widely-used defense method on unlearnable examples. We establish quantitative criteria between the poison and adversarial budgets, which determine the existence of robust unlearnable examples or the failure of the adversarial defense.