Robust Vector Research Articles

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

Addressing the rise of autochthonous vector-borne diseases in a warming Europe

The perspective explores the emergence of autochthonous tropical diseases in Europe, driven by climate change and the associated increase in vector-borne diseases. Rising temperatures, along with changes in humidity and rainfall patterns, have altered the activity, distribution, and diversity of vectors such as mosquitoes and ticks. Species like Aedes albopictus and Aedes aegypti, which are primary vectors of dengue, have established self-sustaining populations across Europe. This spread has led to a surge in cases of dengue fever, West Nile virus, and tick-borne encephalitis in temperate regions. The complexity of predicting these outbreaks is compounded by factors like vector diapause, serological cross-reactivity, and land-use changes. The perspective calls for the implementation of enhanced surveillance, weather-linked predictive models, and robust vector control strategies to mitigate the public health risks posed by the spread of these diseases. As climate change accelerates, Europe faces increasing health threats previously confined to tropical regions, emphasizing the need for proactive public health measures to protect populations from this growing threat.

Spatial heterogeneity in the potential distribution of Aedes mosquitoes in India under current and future climatic scenarios

Aedes is the most globally distributed mosquito genus in the 21st century and transmits various arboviral diseases. The rapid expansion of Ae. Aegypti and Ae. albopictus breeding habitats is a significant threat to global public health, driven by temperature and precipitation changes. In this study, bioclimatic variables were employed to predict the spatial distribution of Ae. aegypti and Ae. albopictus in India. The reference coordinate points of (n = 583) Aedes occurrences at a scale of ∼1 km and nineteen bioclimatic factors were retrieved to train SDM (Species Distribution Models) for both species. Maximum entropy modelling was used to predict the species’ fundamental climatic niche distributions. Future projections were made using global climate models for 2021–2040 and 2081–2100 separately. The models performed reasonably well (AUC > 0.77). Both species thrived in reduced diurnal temperature and higher annual mean temperatures, with suitability increasing alongside precipitation. Ae. aegypti’s projected present and future distribution was broader than that of Ae. Albopictus. The expansion of Aedes suitability varied under different future climatic scenarios. Suitability for Ae. aegypti could expand from between 17.6 and 41.1 % in 2100 under SSP (shared socioeconomic pathways) scenarios 1 and 3, respectively, whereas for Ae. albopictus suitability increased from between 10.2 and 25 % under SSP scenarios 1 and 3 respectively. Preparing for future epidemics and outbreaks requires robust vector distribution models to identify high-risk areas, allocate resources for surveillance and control, and implement prevention strategies.

Robust Support Vector Data Description with Truncated Loss Function for Outliers Depression.

Support vector data description (SVDD) is widely regarded as an effective technique for addressing anomaly detection problems. However, its performance can significantly deteriorate when the training data are affected by outliers or mislabeled observations. This study introduces a universal truncated loss function framework into the SVDD model to enhance its robustness and employs the fast alternating direction method of multipliers (ADMM) algorithm to solve various truncated loss functions. Moreover, the convergence of the fast ADMM algorithm is analyzed theoretically. Within this framework, we developed the truncated generalized ramp, truncated binary cross entropy, and truncated linear exponential loss functions for SVDD. We conducted extensive experiments on synthetic and real-world datasets to validate the effectiveness of these three SVDD models in handling data with different noise levels, demonstrating their superior robustness and generalization capabilities compared to other SVDD models.

Human-Environment Interactions Shape Mosquito Seasonal Population Dynamics.

Mosquito species, including the Asian tiger mosquito, can transmit disease-causing pathogens such as dengue, Zika, and chikungunya, with their population dynamics influenced by a variety of factors including climate shifts, human activity, and local environmental conditions. Understanding these dynamics is vital for effective control measures. Our study, conducted in Jardí Botanic Marimurtra from May to November 2021, monitored Ae. albopictus activity using BG-Traps and investigated larval control effects. We employed Generalized Linear Mixed Models to analyze variables like weather, human presence, and larvicidal control on adult mosquito abundance. Adults of Ae. albopictus exhibited a seasonal pattern influenced by temperature but with bimodal peaks linked to cumulative rainfall. Proximity to stagnant water and visitor influx directly affected mosquito captures. Additionally, the effectiveness of larvicide treatments depended on interactions between preceding rainfall levels and treatment timing. Our research emphasizes the significance of studying vector ecology at local scales to enhance the efficacy of control programs and address the escalating burden of vector-borne diseases. Considering the impacts of extreme weather events and climate shifts is essential for the development of robust vector control strategies. Furthermore, our distinct findings serve as a prime illustration of utilizing statistical modeling to gain mechanistic insights into ecological patterns and processes.

Distributionally robust chance-constrained kernel-based support vector machine

Support vector machine (SVM) is a powerful model for supervised learning. This article addresses the nonlinear binary classification problem using kernel-based SVM with uncertainty involved in the input data specified by the first- and second-order moments. To achieve a robust classifier with small probabilities of misclassification, we investigate a distributionally robust chance-constrained kernel-based SVM model. Since the moment information in the original problem becomes unclear/unavailable in the feature space via kernel transformation, we develop a data-driven approach utilizing empirical moments to provide a second-order cone programming (SOCP) reformulation for efficient computation. To speed up the required computations for solving large-size problems in higher dimensional space and/or with more sampling points involved in estimating empirical moments, we further design an alternating direction multipliers-based algorithm for fast computations. Extensive computational results support the effectiveness and efficiency of the proposed model and solution method. Results on public benchmark datasets without any moment information indicate that the proposed approach still works and, surprisingly, outperforms some commonly used state-of-the-art kernel-based SVM models.

SpecEncoder: deep metric learning for accurate peptide identification in proteomics.

Tandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. We evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%-2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%-15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%-12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder's potential to enhance peptide identification for proteomic data analyses. The source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu.

Robust Bayesian target vector optimization for multi-stage manufacturing processes

This research focuses on optimizing multi-stage manufacturing processes using Bayesian optimization (BO) with a robust Expected Improvement (EI) acquisition function. The aim is to optimize towards pre-selected target vectors, not to just minimize or maximize a function. To achieve this, we minimize the Euclidean distance between the actual and target output vectors, which requires transforming the Gaussian surrogate model posterior distribution into a non-central χ2(NCχ2) distribution. Furthermore, the distance measure additionally uses aleatoric uncertainty estimates of the actual output vectors to achieve robustness. We use a cascaded method that also considers the optimization results of intermediate stages, whereby optimization results are propagated from the last stage towards the first stage in each optimization iteration. By considering intermediate process outputs and aleatoric effects, our approach provides a robust optimization method for multi-stage manufacturing processes. To validate our method and to evaluate its properties, we use two artificial use cases. Moreover, we evaluate our approach in an industrial multi-stage forging process for the manufacturing of a nickel basis superalloy turbine disk, where involved stages are represented by corresponding 2D finite element method (FEM) DEFORM simulations. Evaluation suggests that our approach is superior in optimizing multi-stage manufacturing processes by considering all stage outcomes, robust distance measures, and the use of appropriate uncertainty distributions.

Does fintech matter for financial inclusion and financial stability in BRICS markets?

We investigate whether fintech development expedites financial inclusion and affects the stability of the financial sector in BRICS economies. We first seek to identify the linkage between Fintech development and financial inclusion in the BRICS economies. We then explore if Fintech poses any threat to financial stability by studying the impact of Fintech on three main factors of financial stability; Country risk, Liquidity, and Price volatility to see any possible threat to financial stability. We apply the robust Global Vector Autoregressive (GVAR) model with Bayesian framework to analyze a monthly dataset ranging from 2015 to 2022. By applying a positive shock to Fintech over financial inclusion and financial stability, we reveal a positive dynamic relationship between fintech development and financial inclusion, with fintech exhibiting a long-term influence on financial inclusion in some BRICS countries than others. However, we found no significant evidence that fintech presents any threat to financial stability; at least not in the short term. We offer several policy implications and future research directions.

Distributed robust support vector ordinal regression under label noise

Ordinal regression (OR) methods are designed for a type of classification problems where data labels have natural orders. In practice, data may be corrupted by label noise, which affects the training process thus degrading the generalization performance of OR methods. In OR, data are usually assumed to have latent variables underlying the ordinal labels, and label noise exhibits a special characteristic that it usually causes large latent variable value deviations. However, there are few existing works on OR considering label noise, and the existing works do not utilize the above-mentioned characteristic. Besides, most of the existing OR methods are centralized, which are inapplicable in some realistic distributed applications. In this paper, we utilize the characteristic of label noise in OR to develop a distributed robust support vector ordinal regression method (dRSVOR) under label noise. Specifically, after analyzing the characteristic of label noise in OR, we take the form of SVOR with explicit constraints to achieve robustness to one type of mislabeled samples. Then, we adopt correntropy, an information-theoretic measure, to achieve robustness to the other type of mislabeled samples. Theoretically, we analyze the consensus and convergence of dRSVOR. Experimentally, we conduct experiments on both synthetic data and real OR datasets to illustrate the effectiveness of the proposed method. The results show that the centralized version of dRSVOR outperforms several state-of-the-art OR methods considering label noise in centralized circumstances with label noise, and dRSVOR could approach the performance of the centralized version despite additional constraints in distributed scenarios.

Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches-Audio Branch, Video Branch, and Text Branch-each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks-reading and interviewing-implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation

This paper introduces an innovative unified deep learning (DL) model, “reduced deep convolutional stack autoencoder (RDCSAE)-robust kernel-based random vector functional link network (RKRVFLN)” (RDCSAE-RKRVFLN), for addressing practical challenges like phase and gain uncertainty, mutual coupling effects, and element position errors in direction of arrival (DOA) estimation. The RDCSAE utilizes deep convolutional neural network (DCNN) and stack autoencoder (SAE) for robust feature extraction. The RKRVFLN integrates a concise SAE output with kernel-based learning. Thus, the integration of RDCSAE (suitable for unsupervised feature extraction) and RKRVFLN (for supervised learning) in the developed model results in efficient use of limited training data. Performance metrics of this method include among others the accuracy (separation between sources), root mean square error (RMSE), signal-to-noise ratio (SNR), etc. The method performs better than existing data driven methods in terms of computational efficiency, faster learning speed, enhanced model generalization, higher accuracy, and shorter event regression time and empirical methods. In the presence of above mentioned four perturbations, this method successfully resolves multiple sources with a resolution of 0.3° with a RMSE of 0.00376. For real time application, the paper implements RDCSAE-RKRVFLN on a high-speed reconfigurable Xilinx Virtex-5 field-programmable gate array (FPGA) realizing a digital DOA estimator for one source. The hardware estimates the DOA with a maximum error of 0.01953125 which follows closely the simulation results indicating the efficiency and feasibility of the method.

An ecotoxicological view on malaria vector control with ivermectin-treated cattle

Malaria remains an enduring challenge in sub-Saharan Africa, affecting public health and development. Control measures can include the use of insecticides that target adult Anopheles mosquitoes transmitting the malaria-causing Plasmodium parasite. Such mosquitoes can also bite livestock, allowing vector populations to be maintained at levels that enable parasite transmission. Thus, one way to control the spread of malaria includes the use of endectocide-treated livestock which renders the blood of cattle toxic to such mosquito populations. Here we present an ecotoxicological perspective on malaria vector control, using cattle treated with the endectocide ivermectin to target zoophagic and opportunistic Anopheles coluzzii mosquitoes. Our study employs an innovative, long-acting injectable ivermectin formulation with over 6 months of sustained mosquitocidal activity. Robust vector population modelling underscores its promising field effectiveness. Environmental implications (soil sorption and dissipation) of excreted ivermectin and potential ecotoxicological risks to non-target dung organisms in West Africa are discussed, in addition to actionable, locally inspired risk mitigation measures to protect sub-Saharan soils and agroecosystems from chemical pollution. We highlight how ecotoxicology and environmental chemistry improve livestock-based vector control with ivermectin for effective and more sustainable malaria management.

Sparse and robust support vector machine with capped squared loss for large-scale pattern classification

Support vector machine (SVM), being considered one of the most efficient tools for classification, has received widespread attention in various fields. However, its performance is hindered when dealing with large-scale pattern classification tasks due to high memory requirements and running very slow. To address this challenge, we construct a novel sparse and robust SVM based on our newly proposed capped squared loss (named as Lcsl-SVM). To solve Lcsl-SVM, we first focus on establishing optimality theory of Lcsl-SVM via our defined proximal stationary point, which is convenient for us to efficiently characterize the Lcsl support vectors of Lcsl-SVM. We subsequently demonstrate that the Lcsl support vectors comprise merely a minor fraction of entire training data. This observation leads us to introduce the concept of the working set. Furthermore, we design a novel subspace fast algorithm with working set (named as Lcsl-ADMM) for solving Lcsl-SVM, which is proven that Lcsl-ADMM has both global convergence and relatively low computational complexity. Finally, numerical experiments show that Lcsl-ADMM has excellent performances in terms of getting the best classification accuracy, using the shortest time and presenting the smallest numbers of support vectors when solving large-scale pattern classification problems.

Life Function Analysis of a Two-Parameter Exponential Distribution Using Robust Rank Regression

In this research, a proposed method is presented to estimate the life function of the two-parameter exponential distribution by using the robust rank regression on the dependent variable, which depends on the robust variance-covariance matrix (the FMCD and OGK method) and the robust mean vector through which the rank regression coefficients are estimated using the method of ordinary least squares and employing it in estimating the scale and location parameters of the exponential distribution and thus analyzing the life function on its basis. A comparison was also made between the proposed method and the classical method of rank regression on the dependent variable of life data at different sample sizes (from 10 to 25) to show the efficiency of the proposed method based on the mean square error of the estimated parameters, through a program prepared by the researcher in MATLAB language dedicated to this purpose. The practical application results concluded that the proposed robust method of rank regression on the dependent variable in estimating the parameters of the exponential distribution was more efficient than the classical method. Paper type: Research paper.

Herb Extracellular Vesicle-Chitosan-PEGylated Graphene Oxide Conjugate Delivers Estrogen Receptor α Targeting siRNA to Breast Cancer Cells.

Herb-based extracellular vesicles (EV), inherently replete with bioactive proteins, RNA, lipids, and other medicinal compounds, are noncytotoxic and uniquely capable of cellular delivery to meet the ever-stringent challenges of ongoing clinical applications. EVs are abundant in nature, affordable, and scalable, but they are also incredibly fragile and stuffed with many biomolecules. To address the low drug binding abilities and poor stability of EVs, we demonstrated herb-based EVs (isolated from neem, mint, and curry leaves) conjugated with chitosan (CS) and PEGylated graphene oxide (GP) that led to their transformation into robust and efficient vectors. The designed conjugates successfully delivered estrogen receptor α (ERα1)-targeting siRNA to breast cancer MCF7 cells. Our data revealed that neem-based EV-CS-GP conjugates were most efficient in cellular siRNA delivery, which could be attributed to hyaluronic acid-mediated recognition of neem EVs by MCF7 cells via CD44 receptors. Our approach shows a futuristic direction in designing clinically viable, sustainable, nontoxic EV-based vehicles that can deliver a variety of functional siRNA cargos.

Enhancing class imbalance solutions: A projection-based fuzzy LS-TSVM approach

Class imbalance and noise present significant challenges in numerous real-world classification tasks. The prevalence of an uneven distribution of samples typically results in a bias towards the majority class in Support Vector Machine (SVM) classifiers, compounded by the often inherent noise within these samples. Addressing both class imbalance and noise, we introduce two fuzzy-based methodologies. The first method employs intuitionistic fuzzy membership, resulting in the development of the Robust Energy-based Intuitionistic Fuzzy Least Squares Twin Support Vector Machine (IF-RELSTSVM), a model specifically designed for class imbalance learning. The IF-RELSTSVM model is distinguished by its use of intuitionistic fuzzy scores for both classes, significantly attenuating the detrimental effects of noise and outliers. A distinctive attribute of IF-RELSTSVM is its proficiency in processing noisy data points, whether proximate to or distant from the hyperplane. Additionally, we introduce a novel concept of hyperplane-based fuzzy membership, calculating fuzzy memberships through a projection-based approach. This foundation supports the formulation of a Robust Energy-based Fuzzy Least Squares Twin Support Vector Machine (F-RELSTSVM), also aimed at class imbalance learning. The efficacy of the proposed IF-RELSTSVM and F-RELSTSVM algorithms is rigorously evaluated across several benchmark and synthetic datasets, employing the Area Under the ROC Curve (AUC) as a performance metric. Experimental findings indicate that these algorithms surpass baseline models in the majority of datasets tested. Statistical analyses further validate the significance of our proposed methods, demonstrating their suitability for application in environments characterized by noise and class imbalance. A case study in credit card fraud detection showcases the F-RELSTSVM algorithm achieving an impressive average AUC of 90.84%, thereby outperforming comparable algorithms and highlighting the practical applicability of our approaches in tackling challenging datasets.

Prototype-Based Soft Feature Selection Package

This paper presents a prototype-based soft feature selection package (Sofes) wrapped around the highly interpretable Matrix Robust Soft Learning Vector Quantization (MRSLVQ) and the Local MRSLVQ algorithms. The process of assessing feature relevance with Sofes aligns with a comparable approach established in the Nafes package, with the primary distinction being the utilization of prototype-based induction learners influenced by a probabilistic framework. The numerical evaluation of test results aligns Sofes’ performance with the Nafes package's.

Forecasting peak electric load: Robust support vector regression with smooth nonconvex ϵ‐insensitive loss

AbstractPeak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non‐convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave‐convex programming algorithm is used to solve the non‐convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non‐convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non‐convex support vector regression in robustness and generalization ability.

Shots segmentation-based optimized dual-stream framework for robust human activity recognition in surveillance video

Nowadays, for controlling crime, surveillance cameras are typically installed in all public places to ensure urban safety and security. However, automating Human Activity Recognition (HAR) using computer vision techniques faces several challenges such as lowlighting, complex spatiotemporal features, clutter backgrounds, and inefficient utilization of surveillance system resources. Existing attempts in HAR designed straightforward networks by analyzing either spatial or motion patterns resulting in limited performance while the dual streams methods are entirely based on Convolutional Neural Networks (CNN) that are inadequate to learning the long-range temporal information for HAR. To overcome the above-mentioned challenges, this paper proposes an optimized dual stream framework for HAR which mainly consists of three steps. First, a shots segmentation module is introduced in the proposed framework to efficiently utilize the surveillance system resources by enhancing the lowlight video stream and then it detects salient video frames that consist of human. This module is trained on our own challenging Lowlight Human Surveillance Dataset (LHSD) which consists of both normal and different levels of lowlighting data to recognize humans in complex uncertain environments. Next, to learn HAR from both contextual and motion information, a dual stream approach is used in the feature extraction. In the first stream, it freezes the learned weights of the backbone Vision Transformer (ViT) B-16 model to select the discriminative contextual information. In the second stream, ViT features are then fused with the intermediate encoder layers of FlowNet2 model for optical flow to extract a robust motion feature vector. Finally, a two stream Parallel Bidirectional Long Short-Term Memory (PBiLSTM) is proposed for sequence learning to capture the global semantics of activities, followed by Dual Stream Multi-Head Attention (DSMHA) with a late fusion strategy to optimize the huge features vector for accurate HAR. To assess the strength of the proposed framework, extensive empirical results are conducted on real-world surveillance scenarios and various benchmark HAR datasets that achieve 78.6285%, 96.0151%, and 98.875% accuracies on HMDB51, UCF101, and YouTube Action, respectively. Our results show that the proposed strategy outperforms State-of-the-Art (SOTA) methods. The proposed framework gives superior performance in HAR, providing accurate and reliable recognition of human activities in surveillance systems.