Data Point Research Articles

Generalized and Robust Least Squares Regression.

As a simple yet effective method, least squares regression (LSR) is extensively applied for data regression and classification. Combined with sparse representation, LSR can be extended to feature selection (FS) as well, in which l1 regularization is often applied in embedded FS algorithms. However, because the loss function is in the form of squared error, LSR and its variants are sensitive to noises, which significantly degrades the effectiveness and performance of classification and FS. To cope with the problem, we propose a generalized and robust LSR (GRLSR) for classification and FS, which is made up of arbitrary concave loss function and the l2,p -norm regularization term. Meanwhile, an iterative algorithm is applied to efficiently deal with the nonconvex minimization problem, in which an additional weight to suppress the effect of noises is added to each data point. The weights can be automatically assigned according to the error of the samples. When the error is large, the value of the corresponding weight is small. It is this mechanism that allows GRLSR to reduce the impact of noises and outliers. According to the different formulations of the concave loss function, four specific methods are proposed to clarify the essence of the framework. Comprehensive experiments on corrupted datasets have proven the advantage of the proposed method.

Transductive classification via patch alignment

In this paper, a novel approach for transductive classification is proposed. Unlike existing methods that heavily rely on constructing the Laplacian matrix to capture data distribution, the proposed approach takes a unique path. It employs a linear transformation model to create local patches for each data point and then unifies them in an objective function to build the Laplacian matrix. Incorporating this Laplacian matrix into the transductive classification framework allows us to assign optimal class labels globally. The experimental results from toy data and real world databases demonstrate that the proposed approach achieves more efficient and stable performance, while this approach is insensitive to the parameters. Notably, our method exhibits robustness to parameter variations, making it highly adaptable to practical applications.

Anomaly Detection Algorithms for Low-Dimensional and High-Dimensional Data: A Critical Study

Suspicious events or objects that differ from the norm in data can be discovered using anomaly identification. Identifying anomalies is critical for many applicable domains of life, e.g., preventing credit card theft and spotting intrusions into networks. It is possible to detect anomalies on a global scale as well as at the local level. A global outlier is a data point beyond the norm for the entire dataset, while a local outlier may be inside the norm for the entire dataset but outside the surrounding data points. Data outlier identification methods that are performed locally are inadequate. Therefore, better algorithms are required to investigate the high velocity of data and identify local outliers. Machine learning and data mining techniques need to be investigated to determine the pros and cons of anomaly identification residing inside data. The density based LOF method can be applied as the best choice to identify local outliers. While many variants of LOF exist for low-dimensional data, none are suitable for high-dimensional data. This research study discusses LOF, COF, and CBLOF methods for spotting local outliers in low and high-dimensional data. Regarding the size of the dimension, the performance of density-based algorithms is examined based on accuracy and time complexity. In this scenario, CBLOF achieves outstanding results due to its distinctive method of employing cluster-based local outlier detection.

On logistic regression versus support vectors machine using vaccination dataset

The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.

Development and Assessment of SNP Genotyping Arrays for Citrus and Its Close Relatives.

Rapid advancements in technologies provide various tools to analyze fruit crop genomes to better understand genetic diversity and relationships and aid in breeding. Genome-wide single nucleotide polymorphism (SNP) genotyping arrays offer highly multiplexed assays at a relatively low cost per data point. We report the development and validation of 1.4M SNP Axiom® Citrus HD Genotyping Array (Citrus 15AX 1 and Citrus 15AX 2) and 58K SNP Axiom® Citrus Genotyping Arrays for Citrus and close relatives. SNPs represented were chosen from a citrus variant discovery panel consisting of 41 diverse whole-genome re-sequenced accessions of Citrus and close relatives, including eight progenitor citrus species. SNPs chosen mainly target putative genic regions of the genome and are accurately called in both Citrus and its closely related genera while providing good coverage of the nuclear and chloroplast genomes. Reproducibility of the arrays was nearly 100%, with a large majority of the SNPs classified as the most stringent class of markers, "PolyHighResolution" (PHR) polymorphisms. Concordance between SNP calls in sequence data and array data average 98%. Phylogenies generated with array data were similar to those with comparable sequence data and little affected by 3 to 5% genotyping error. Both arrays are publicly available.

Identification of flutter boundary of a bridge deck section using CFD and flutter margin analysis

Aeroelastic flutter identification of a two-dimensional bridge deck section using time domain techniques is studied. Aeroelastic simulations are performed using the aeroelastic solver available in open source OpenFOAM 5.0, wherein aerodynamic loads are computed using Unsteady Reynolds Average Naiver–Stokes (URANS) solver. The bridge deck section, modelled as a rigid body, exhibits pitch and heave degrees of freedom. Time domain system identification techniques, which include VAR and ERA (Vector auto-regressive and Eigensystem realization algorithm), are used to extract frequency and damping parameters from the aeroelastic response for various wind speeds.Two approaches are used for identification of the flutter boundary. The first consists of tracking the behaviour of damping and identifying the speed at which damping vanishes. The second approach is based on the flutter margin for discrete-time system (FMDS), which exhibits a linear behaviour with dynamic pressure in the vicinity of flutter.Results presented indicate that the ERA yielded slightly better estimates of damping compared to VAR. Furthermore, the FMDS parameter provided a reasonable estimate of the flutter boundary using only two data points, suggesting that this is an efficient strategy (as compared to tracking the damping) for flutter identification when the cost per aeroelastic response data point is high.

Multi-objective Feature Attribution Explanation For Explainable Machine Learning

The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model’s output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although various metrics have been proposed to evaluate the explanation quality, no single metric could capture different aspects of the explanations. Different conclusions might be drawn using different metrics. Moreover, during the processes of generating explanations, existing FAE methods either do not consider any evaluation metric or only consider the faithfulness of the explanation, failing to consider multiple metrics simultaneously. To address this issue, we formulate the problem of creating FAE explainable models as a multi-objective learning problem that considers multiple explanation quality metrics simultaneously. We first reveal conflicts between various explanation quality metrics, including faithfulness, sensitivity, and complexity. Then, we define the considered multi-objective explanation problem and propose a multi-objective feature attribution explanation (MOFAE) framework to address this newly defined problem. Subsequently, we instantiate the framework by simultaneously considering the explanation’s faithfulness, sensitivity, and complexity. Experimental results comparing with six state-of-the-art FAE methods on eight datasets demonstrate that our method can optimize multiple conflicting metrics simultaneously and can provide explanations with higher faithfulness, lower sensitivity, and lower complexity than the compared methods. Moreover, the results have shown that our method has better diversity, i.e., it provides various explanations that achieve different tradeoffs between multiple conflicting explanation quality metrics. Therefore, it can provide tailored explanations to different stakeholders based on their specific requirements.

Feasibility of Powder‐Based High‐Throughput Synthesis for Ceramics Development: Case Study on the Influence of Calcination Temperature in BiFeO3‐BaTiO3

Recent advances in machine learning capabilities have increased interest in materials research to improve the efficiency of materials discovery and optimization as well as to better understand the underlying phenomena responsible for the observed physical properties. While combinatorial chemistry and compositional engineering is well established in the development of pharmaceutical and chemical products, its use in the field of bulk functional polycrystalline ceramics is far from mature. In this work, a critical review of a high‐throughput powder‐based dispensing system is provided and the challenges involved with the transition from a conventional, human resources intensive workflow to a fully automated process are highlighted. Based on the lead‐free piezoelectric BiFeO3–BaTiO3 binary system, the applicability and robustness of high‐throughput engineering is investigated to increase data point density in phase diagrams, including the composition variations of the resulting materials. This work presents 16 different BiFeO3–BaTiO3 compositions at four different calcination temperatures, both demonstrating the potential of the system. This is coupled to automated crystal structure analysis, which will be used to investigate the role of calcination temperature on the resulting compositions at room temperature.

Constraining cosmic reionization by combining the kinetic Sunyaev–Zel’dovich and the 21 cm power spectra

ABSTRACT During the Epoch of Reionization (EoR), the ultraviolet radiation from the first stars and galaxies ionized the neutral hydrogen of the intergalactic medium, which can emit radiation through its 21 cm hyperfine transition. Measuring the 21 cm power spectrum is a key science goal for the future Square Kilometre Array (SKA); however, observing and interpreting it is a challenging task. Another high-potential probe of the EoR is the patchy kinetic Sunyaev–Zel’dovich (pkSZ) effect, observed as a foreground to the cosmic microwave background temperature anisotropies on small scales. Despite recent promising measurements, placing constraints on reionization from pkSZ observations is a non-trivial task, subject to strong model dependence. We propose to alleviate the difficulties in observing and interpreting the 21 cm and pkSZ power spectra by combining them. With a simple yet effective parametric model that establishes a formal connection between them, we can jointly fit mock 21 cm and pkSZ data points. We confirm that these observables provide complementary information on reionization, leading to significantly improved constraints when combined. We demonstrate that with as few as two measurements of the 21 cm power spectrum with 100 h of observations with the SKA, as well as a single ℓ = 3000 pkSZ data point, we can reconstruct the reionization history of the universe and its morphology. We find that the reionization history (morphology) is better constrained with two 21 cm measurements at different redshifts (scales). Therefore, a combined analysis of the two probes will give access to tighter constraints on cosmic reionization even in the early stages of 21 cm detections.

Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph

During the tracking of moving targets in dynamic scenes, efficiently handling outliers in the optical flow and maintaining robustness across various motion amplitudes represents a critical challenge. So far, studies have used thresholding and local consistency based approaches to deal with optical outliers. However, there is subjectivity through expert-defined thresholds or delineated regions, and therefore these methods do not perform consistently enough under different target motion amplitudes. Other studies have focused on complex statistical-mathematical modeling which, although theoretically valid, requires significant computational resources. Aiming at the above problems this paper proposes a new method to calculate the optical outliers by using stochastic neighborhood graph combined with the Borda counting method, which reduces the computation amount on the basis of objectively eliminating the outliers. Sparse optical flow (SOF) values are used as the overall population and the outlier and inlier SOF values are used as samples. Analyze the dissimilarity between SOF data points, obtaining the dissimilarity matrix, introducing the Gaussian function to smooth and reduce the dimensionality of the dissimilarity matrix, and then normalizing the smoothing matrix to generate the binding matrix, where the probability sum of each node to other nodes in the matrix is equal to 1. Stochastic neighborhood graphs are then generated based on a binding matrix to obtain the outlier probabilities of data points in different neighborhood graphs, and outlier samples are obtained based on the probability. To avoid the subjectivity of the expert thresholds, the outlier probabilities are weighted and ranked to calculate the data point Borda scores to obtain accurate optical outliers. The experimental results show that the method in this paper is robust to different amplitude motions and real scenarios, and the accuracy, precision and recall of outliers elimination are better than the current mainstream algorithms.

From Clustering to Cluster Explanations via Neural Networks.

A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems, however, the label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters. While powerful methods exist for extracting the cluster structure in data, they typically do not answer the question why a certain data point has been assigned to a given cluster. We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks-or "neuralized." Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations.

Relaxation linear regression with spectral–spatial constrained locality adaptive regularization for hyperspectral image classification

AbstractRecently, relaxation linear regression has received increasing attention in image analysis. However, the current relaxation linear regression methods fail to consider the local geometrical structure and spatial information while they are applied for hyperspectral image (HSI) classification. To address the above problems, this letter proposes a novel relaxation linear regression with spectral–spatial constrained locality adaptive regularization (SSLA‐RLR) method for HSI classification. The SSLA‐RLR method not only integrates the locality adaptive graph with relaxation linear regression to adaptively exploit the local geometrical structure for relieving the side effects of noise corruptions, but also takes the spatial correlations between each data point and its neighbours into consideration via spectral–spatial constraint. Finally, extensive experiments are conducted on three benchmark HSI datasets to demonstrate the effectiveness of the proposed method.

Multi-Label Contrastive Learning for Abstract Visual Reasoning.

For a long time, the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence. Recent advances in the application of deep learning (DL) methods led to surpassing human abstract reasoning performance, specifically in the most popular type of such problems-Raven's progressive matrices (RPMs). While the efficacy of DL systems is indeed impressive, the way they approach the RPMs is very different from that of humans. State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on the identification of the rules/concepts underlying the RPM to be solved. Motivated by this cognitive difference, this work aims at combining DL with the human way of solving RPMs. Specifically, we cast the problem of solving RPMs into a multilabel classification framework where each RPM is viewed as a multilabel data point, with labels determined by the set of abstract rules underlying the RPM. For efficient training of the system, we present a generalization of the noise contrastive estimation algorithm to the case of multilabel samples and a new sparse rule encoding scheme for RPMs. The proposed approach is evaluated on the two most popular benchmark datasets [I-RAVEN and procedurally generated matrices (PGM)] and on both of them demonstrate an advantage over the state-of-the-art results.

Pervasive User Data Collection from Cyberspace: Privacy Concerns and Countermeasures

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks.

Subsurface geology detection from application of the gravity-related dimensionality constraint

Geophysics aims to locate bodies with varying density. We discovered an innovative approach for estimation of the location, in particular depth of a causative body, based on its relative horizontal dimensions, using a dimensionality indicator (I). The method divides the causative bodies into two types based on their horizontal spread: line of poles and point pole (LOP–PP) category, and line of poles and plane of poles (LOP–POP) category; such division allows for two distinct solutions. The method’s depth estimate relates to the relative variations of the causative body’s horizontal extent and leads to the solutions of the Euler Deconvolution method in specific cases. For causative bodies with limited and small depth extent, the estimated depth (z^0) corresponds to the center of mass, while for those with a large depth extent, z^0 relates to the center of top surface. Both the depth extent and the dimensionality of the causative body influence the depth estimates. As the depth extent increases, the influence of I on the estimated depth is more pronounced. Furthermore, the behavior of z^0 exhibits lower errors for larger values of I in LOP–POP solutions compared with LOP–PP solutions. We tested several specific model scenarios, including isolated and interfering sources with and without artificial noise. We also tested our approach on real lunar data containing two substantial linear structures and their surrounding impact basins and compared our results with the Euler deconvolution method. The lunar results align well with geology, supporting the effectiveness of this approach. The only assumption in this method is that we should choose between whether the gravity signal originates from a body within the LOP–PP category or the LOP–POP category. The depth estimation requires just one data point. Moreover, the method excels in accurately estimating the depth of anomalous causative bodies across a broad spectrum of dimensionality, from 2 to 3D. Furthermore, this approach is mathematically straightforward and reliable. As a result, it provides an efficient means of depth estimation for anomalous bodies, delivering insights into subsurface structures applicable in both planetary and engineering domains.

Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India

Machine Learning (ML) has been used in the prediction of geolocation with improved accuracies in this work. The pre-processed data was subjected to prediction analytics using 22 machine learning algorithms over regression mode. It was observed that Extra Trees Regressor performed well with better accuracies in predicting latitude, longitude, and Haversine distance, respectively. Regression models like CatBoost, Extreme Gradient boosting, Light Gradient boosting machine, and Gradient boosting regressor were also tested. The R2 values were computed for each case, and we obtained 0.96 (Longitude), 0.98 (Latitude), and 0.96 (Haversine), respectively. The evaluation of models was done using metrics like MAE, MASE, RMSE, R2, RMSLE, and MAPE and R2 is considered most important than others. The effect of data point was calculated using Cooks’ distance, and the variable fluoride has a significant impact on the prediction accuracy of Longitude followed by RSC, Cl, SO4, SAR, NO3, NA, Ca, EC and pH variables. In the prediction of latitude, the SAR variable played a significant role, followed by Na and TH. According to the t-SNE manifold, three longitude values were quite different from the others. This work is supported by some of the manifests like Cooks’ distance outlier detection, feature importance plot, t-SNE manifold, prediction error plot, residuals plot, RFECV plot, and validation curve. This work is done to report that the challenge of predicting both latitude and longitude on a common ground is solved partially, if not completely, and machine learning tools can be used for this purpose. Haversine distance can be obtained from latitude and longitude and can be used in the prediction of geolocation.

Optimization method of the cam curve for acontinuous zoom optical system.

To make the driving force of a zoom cam cylinder stable and easy to control, the correlation between the cam curves of the zoom group and the compensation group is established effectively by reasonably selecting and arranging the coordinate system.Two optimization methods of cam curve are proposed. First, the original data are processed directly by the function of the target cam curve, and then the dynamic parameter such as the pressure angle is verified again to obtain the expected cam curve. This optimization method is simple and convenient, and a variety of optimization results can be constructed. Second, a physical model of multiple cam curves and the driving force with only one variable is constructed. The step search optimization method is used to process the original data point by point, and the best cam curve matching the model is obtained. Through dynamic simulation, it is determined that the cam curves designed by this optimization method can drive the zoom group and compensation group to complete the whole continuous zoom movement with a stable torque.

Hybrid Clustering Algorithm Based on Improved Density Peak Clustering

In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a ‘chain reaction’, i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.

Trends in Distributions of Hearing Threshold Levels by Ages: A Comparison of the ISO 7029 and Newly Available Country-Specific Data.

Hearing thresholds provide essential information and references about the human auditory system. This study aimed to identify changing trends in distributions of hearing threshold levels across ages by comparing the International Organization for Standardization (ISO) 7029 and newly available data after publishing ISO 7029. To compare ISO 7029 and newly available hearing threshold data after publishing ISO 7029, four country-specific datasets that presented average hearing threshold levels under conditions similar to ISO 7029 were utilized. For frequencies between 125 Hz and 8,000 Hz, the deviations of hearing threshold values by ages from the hearing threshold of the youngest age group for each data point were utilized. For frequencies from 9,000 Hz to 12,500 Hz, the median threshold information was utilized. Hearing threshold data reported after publishing ISO 7029 from the four countries were mostly similar to the ISO 7029 data but tended to deviate in some age groups and sexes. As national hearing threshold trends change, the following ISO 7029 revision suggests the need to integrate hearing threshold data from different countries.

A reinforcement learning model with choice traces for a progressive ratio schedule.

The progressive ratio (PR) lever-press task serves as a benchmark for assessing goal-oriented motivation. However, a well-recognized limitation of the PR task is that only a single data point, known as the breakpoint, is obtained from an entire session as a barometer of motivation. Because the breakpoint is defined as the final ratio of responses achieved in a PR session, variations in choice behavior during the PR task cannot be captured. We addressed this limitation by constructing four reinforcement learning models: a simple Q-learning model, an asymmetric model with two learning rates, a perseverance model with choice traces, and a perseverance model without learning. These models incorporated three behavioral choices: reinforced and non-reinforced lever presses and void magazine nosepokes, because we noticed that male mice performed frequent magazine nosepokes during PR tasks. The best model was the perseverance model, which predicted a gradual reduction in amplitudes of reward prediction errors (RPEs) upon void magazine nosepokes. We confirmed the prediction experimentally with fiber photometry of extracellular dopamine (DA) dynamics in the ventral striatum of male mice using a fluorescent protein (genetically encoded GPCR activation-based DA sensor: GRABDA2m). We verified application of the model by acute intraperitoneal injection of low-dose methamphetamine (METH) before a PR task, which increased the frequency of magazine nosepokes during the PR session without changing the breakpoint. The perseverance model captured behavioral modulation as a result of increased initial action values, which are customarily set to zero and disregarded in reinforcement learning analysis. Our findings suggest that the perseverance model reveals the effects of psychoactive drugs on choice behaviors during PR tasks.