Vector-based Methods Research Articles

Rapid computation of survival signature for dynamic fault tree based on sequential binary decision diagram and multidimensional array

Many practical safety-critical systems typically exhibit sequence-dependent failure behaviors, limiting the efficiency of analyzing these systems. Although the survival signature-based method can address this problem to a certain extent, the dependence on Boolean states constrains its application to large systems. In this study, we present a novel method that leverages the sequential binary decision diagram (SBDD) and multidimensional array to rapidly compute survival signatures for dynamic fault trees (DFTs) of these systems. These dynamic nodes in the SBDD are represented through multidimensional arrays, which are then utilized as inputs for the subsequent computations. Ultimately, survival signatures are obtained by iteratively computing the multidimensional arrays. Additionally, two practical engineering cases are examined to highlight the superiority of the proposed methods over other methods. Compared with Boolean state vector-based methods, the proposed method achieves a 689-fold and 209-fold increase in efficiency for calculating survival signatures in their respective cases. Compared with the Monte Carlo (MC) simulation, the simulation efficiency for the reliability results improve by 60-fold and 201-fold in their respective cases.

A simulation-based techno-economic-safety-social-environmental decision-making framework for prioritizing plastic waste treatment processes

Chemical recycling technologies hold promising potential in converting mixed plastic waste into energy and chemicals. This study proposed a simulation-based quantitative life cycle sustainability assessment framework for the potential plastic waste valorization processes. Sustainability metrics including environment, economy, safety, society, and energy were quantified and comprehensively investigated. Based on the Bayesian best-worst weighting and vector-based ranking methods, the normalized and weighted life cycle sustainability scores were used to prioritize the best process. Gasification-based technology was found superior than incineration-based technology, especially in the economic, techno-energetic, and social aspects. However, incinerated-based process with direct CO2 emissions performed better in the environmental aspects compared with the process integrating with carbon capture and utilization unit. Additional environmental burdens were brought to the system when incorporating the carbon capture and methanol synthesis process. Increased system complexity might bring unexpected environmental burdens, and sometimes even outweighed the benefits. Various weights were assigned to the multi-criteria decision-making model, and the gasification-based process consistently ranked first across all scenarios.

Compositionality and Sentence Meaning: Comparing Semantic Parsing and Transformers on a Challenging Sentence Similarity Dataset

Abstract One of the major outstanding questions in computational semantics is how humans integrate the meaning of individual words into a sentence in a way that enables understanding of complex and novel combinations of words, a phenomenon known as compositionality. Many approaches to modelling the process of compositionality can be classified as either ‘vector-based’ models, in which the meaning of a sentence is represented as a vector of numbers, or ‘syntax-based’ models, in which the meaning of a sentence is represented as a structured tree of labelled components. A major barrier in assessing and comparing these contrasting approaches is the lack of large, relevant datasets for model comparison. This paper aims to address this gap by introducing a new dataset, STS3k, which consists of 2,800 pairs of sentences rated for semantic similarity by human participants. The sentence pairs have been selected to systematically vary different combinations of words, providing a rigorous test and enabling a clearer picture of the comparative strengths and weaknesses of vector-based and syntax-based methods. Our results show that when tested on the new STS3k dataset, state-of-the-art transformers poorly capture the pattern of human semantic similarity judgments, while even simple methods for combining syntax- and vector-based components into a novel hybrid model yield substantial improvements. We further show that this improvement is due to the ability of the hybrid model to replicate human sensitivity to specific changes in sentence structure. Our findings provide evidence for the value of integrating multiple methods to better reflect the way in which humans mentally represent compositional meaning.

Parsimonious Tensor Dimension Reduction

Tensor data is emerging in many scientific applications, such as multi-tissue transcriptomics. In such cases, the covariates for each individual are no longer a vector. To apply traditional vector-based methods to this type of data, we need to either do the vectorization or analyze data marginally, which suffers a significant information loss. We propose a novel parsimonious tensor dimension reduction (pTDR) approach to directly link the response and tensor covariate through an unknown function g. In pTDR, the response variable, continuous or discrete, depends on K rank-one projections of the covariates, with the projections estimated via a sequential iterative dimension reduction algorithm. We further propose an asymptotic sequential statistical test to select the correct number of rank-one tensors. In contrast to the classic low-rank tensor regression, pTDR model is not restricted to the linear relationship between response and covariates. We apply pTDR to two modern genomic studies. We find that the gene expression of multiple tissues has a stronger association with aging and obesity than was apparent using previous approaches. Numerical results demonstrate the advantages of pTDR over competitors in terms of prediction accuracy and computing efficiency. Our software is publicly available on GitHub (https://github.com/BioAlgs/pTDR).

Large-scale integration of remotely sensed and GIS road networks: A full image-vector conflation approach based on optimization and deep learning

Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot utilize rich raster information. Further more, the vector and raster data can have discrepancies for various reasons. Efficient road data production requires an image-vector conflation process that can match and combine raster and vector-based road data automatically.In this study, we propose a full image-vector conflation framework that directly integrates image and vector road data by appropriately transforming extracted roads from imagery and establishing a match relation between these roads and a credible target GIS road dataset. Based on analyzing these match relations, we propose new metrics for measuring the degree of agreement between the raster and vector road data. The proposed framework combines state-of-the-art deep learning methods for image segmentation and optimization-based models for object matching. We prepared a large-scale high-resolution road dataset covering two counties in Kansas, US. Using trained models from one of the two counties, we were able to extract road segments in the other county and match them to the TIGER/Line roads.Our experiments show that conventional performance metrics for road extraction (e.g. IoU) are insufficient for measuring the degree of agreement between image and vector roads as they are pixel-based and are too sensitive to spatial displacement. Instead, the newly defined vector-based agreement metrics are needed for image-vector conflation purposes. Experiments show that, by the vector-based metrics, nearly 90% of GIS road lengths in the study area were extracted and over 90% of extracted roads matched the target GIS roads. The new framework streamlines raster-vector conflation of roads and can potentially expedite relevant geospatial analyses regarding change detection, disaster monitoring and GIS data production, among others.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space

Background and objectivesCompared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra. MethodsTo fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI. ResultsThe proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE. ConclusionsThe proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.

Three-Dimensional Engineering Geological Model and Its Applications for a Landslide Site: Combination of Grid- and Vector-Based Methods

A three-dimensional engineering geological model (EGM), which provides an approximation of the geological conditions, is a key element in any engineering project. The slope at Huafan University, Mt. Dalun, in the Western Foothills of northern Taiwan, is a dip slope that has been assumed to be unstable. The bedrock is mainly composed of intercalated sandstone and shale, where the thickness of the sandstone varies from thin to massive, interbedded with shale from the Miocene age. By interpolating the thickness of the colluvium derived from borehole data and analyzing the contours of the interpolation surface result, we find that the landslide material accumulates at the slope foot, towards the southwest in the direction of movement. Due to tectonic control—in particular, considering the two local faults that pass through the study area—the strata’s orientation significantly changes over the studied slope. As a basis for the 3D EGM, polynomial surface fitting is applied for detailed analysis of the sub-surface geological structure, as well as to compute the regressive orientation of the bedding plane derived from the borehole data. Based on the calculated regression plane passing through the elevations of the geological interface (key bed), the results indicate that the regression plane’s direction is consistent with the outcrop measurements. Moreover, several cross-sectional profiles are considered to visualize and clarify the 3D EGM. Finally, surface and sub-surface monitoring data are compared with the result, in order to refine the 3D EGM. The proposed geological model is expected to contribute to the comprehensive understanding of gravitational slope deformation, and may serve as a guideline to minimize potential disasters.

A hexagon-based method for polygon generalization using morphological operators

Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins.

A raster-based typification method for multiscale visualization of building features considering distribution patterns

ABSTRACT In map multiscale visualization, typification is the process of replacing original objects, such as buildings, using a smaller number of objects while maintaining initial geometrical and distribution characteristics. During the past few decades, many vector-based methods for building typification have been developed, whereas raster-based methods have received less attention. In this paper, a new method for the typification of buildings with different distribution patterns called superpixel building typification (SUBT) is developed based on raster data. Using this method, buildings with different distribution patterns, such as linear, grid and irregular patterns, are first grouped by image connected component detection and superpixel analysis. Then, the new positions for building typification are determined by superpixel resegmentation. Finally, a new representation of the buildings is determined through analysis of the orientation and shape of the buildings in each superpixel. To test the proposed SUBT method, buildings from both cities and countrysides in China are applied to perform typification. The experimental results show that the proposed SUBT method can realize typification for buildings with linear, grid and irregular distributions while effectively maintaining the original distribution characteristics of the buildings.

Two-dimensional jointly sparse robust discriminant regression

Ridge regression is an important method in feature extraction and has been extended in many different versions. Robust discriminant regression aims to solve the Small Sample Size Problem (SSSP) in ridge regression form by designing a novel regression model and imposing on L2,1-norm as the main metric instead of the regularized term. However, when the dimensions of data are very high, RDR will be faced with the problem of the curse of dimensionality and high memory space cost. The computation cost of RDR will be extremely high in the iterative procedures. To address this problem, we propose an improved method called Two-dimensional jointly sparse RDR (2DJSRDR) for image-based feature extraction. Unlike previous vector-based methods which stretch the data into a high-dimensional vector as input, the proposed 2DJSRDR uses the two-dimensional image matrix directly as the computational unit so that the drawbacks in RDR can be naturally avoided. Besides, we also introduce L2,1-norm as regularization term to obtain jointly sparse projections for feature selection, which is helpful to improve the performance of the model. Experiments on some benchmark datasets demonstrate the superior performance of the proposed method.

The potential and problems of volumetric 3D modeling in archaeological stratigraphic analysis: A case study from Chlorakas-Palloures, Cyprus

3D digital recording technologies have become increasingly popular for rapid, cost-effective, and accurate archaeological documentation in the field. Despite improvements in 3D GIS software, three-dimensional reconstruction of archaeological stratigraphy has been slow to develop and is rarely utilized as a basis for spatial analysis post-excavation. This is partially owing to the technical and methodological challenges of creating accurate reconstructions of excavated stratigraphy, which restricts the analytical potential of 3D datasets. Volumetric 3D modeling could vastly improve how we understand and analyze archaeological deposits and stratigraphies by enabling a quantitative comparative analysis of per-volume density of artifact types between stratigraphic units.The presented case study examines the spatial articulation of the recorded stratigraphic units from three adjacent trenches excavated during the 2015–2017 field season at the Chalcolithic settlement site of Chlorakas-Palloures in Cyprus. Three vector-based 3D modeling methods using the existing 3D Analyst toolkit within the proprietary software ESRI ArcScene are evaluated for their effectiveness in volumetric representation of stratigraphy using total station (TS) data and photogrammetry models of archaeological features.The results of this study indicate that volumetric modeling can enhance the analytical potential of spatial data by visualizing and identifying patterns in excavated deposits and organizing and disseminating information about the excavation process in an intuitive, user-friendly interface. However, accurate volumetric modeling of all types of archaeological features requires a re-evaluation of conventional documentation procedures to capture the volume of excavated deposits rather than just the interfaces between them. Furthermore, efficient workflows using cost-effective and open source software must be developed to make this approach more accessible and effective for a wider archaeological community.

Extending the usefulness of the verbal memory test: The promise of machine learning

The evaluation of verbal memory is a core component of neuropsychological assessment in a wide range of clinical and research settings. Leveraging story recall to assay neurocognitive function could be made more useful if it were possible to administer frequently (i.e., would allow for the collection of more patient data over time) and automatically assess the recalls with machine learning methods. In the present study, we evaluated a novel story recall test with 24 parallel forms that was deployed using smart devices in 94 psychiatric inpatients and 80 nonpatient adults. Machine learning and vector-based natural language processing methods were employed to automate test scoring, and performance using these methods was evaluated in their incremental validity, criterion validity (i.e., convergence with trained human raters), and parallel forms reliability. Our results suggest moderate to high consistency across the parallel forms, high convergence with human raters (r values ~ 0.89), and high incremental validity for discriminating between groups. While much work remains, the present findings are critical for implementing an automated, neuropsychological test deployable using remote technologies across multiple and frequent administrations.

Motion vector modification distortion analysis-based payload allocation for video steganography

Video steganography forms a covert communication channel by data embedding in cover elements. To consider inter-frame mutual embedding impacts, this paper proposes a payload allocation strategy in video steganography based on motion vector modification distortion analysis. Firstly, the motion vector modification distortion caused by data embedding is analyzed. Then, a rate–distortion model reflecting the residue deviation propagation in successive inter-coded frames is derived. According to this model, the residue deviation propagation weight of each inter-coded frame can be computed. Finally, an inter-frame payload allocation strategy is designed in order to restrain the residue deviation propagation. Experimental results demonstrate that the proposed payload allocation strategy can enhance existing motion vector-based video steganographic methods in terms of undetectability and video coding performance. Besides, the lower computational complexity can be achieved.

Low-latency localization in the spherical harmonics domain using an iterative search method

Higher-order ambisonic microphones have attained considerable interest for their ability to allow spherical harmonic decomposition of soundfields. In taking advantage of this property, myriad methods have been produced for directional filtering and subsequent analysis, as well as localization and tracking of soundfields. This study introduces a technique to estimate direction of arrival of an acoustic signal by generating a set of spatial filters corresponding to randomly chosen orientations over the sphere, then iteratively reducing the region for orientation selection based on the maximum power obtained from the filter response. This method is distinct from existing mapping- and vector-based methods in that it produces accurate, low-latencyresults in the presence of moderate reverberation. Results obtained using both simulated and experimental data demonstrate viability of this technique for localization in large, reverberant spaces as well as accurate source identification under real-time conditions. [Work supported by NSF grants #1631674 and #1909229, and the RPI Cognitive and Immersive Systems Laboratory.]

Isolation of Adult Human Dermal Fibroblasts from Abdominal Skin and Generation of Induced Pluripotent Stem Cells Using a Non-Integrating Method

Induced pluripotent stem cells (iPSCs) could be considered, to date, a promising source of pluripotent cells for the management of currently untreatable diseases, for the reconstitution and regeneration of injured tissues and for the development of new drugs. Despite all the advantages related to the use of iPSCs, such as the low risk of rejection, the lessened ethical issues, and the possibility to obtain them from both young and old patients without any difference in their reprogramming potential, problems to overcome are still numerous. In fact, cell reprogramming conducted with viral and integrating viruses can cause infections and the introduction of required genes can induce a genomic instability of the recipient cell, impairing their use in clinic. In particular, there are many concerns about the use of c-Myc gene, well-known from several studies for its mutation-inducing activity. Fibroblasts have emerged as the suitable cell population for cellular reprogramming as they are easy to isolate and culture and are harvested by a minimally invasive skin punch biopsy. The protocol described here provides a detailed step-by-step description of the whole procedure, from sample processing to obtain cell cultures, choice of reagents and supplies, cleaning and preparation, to cell reprogramming by the means of a commercial non-modified RNAs (NM-RNAs)-based reprogramming kit. The chosen reprogramming kit allows an effective reprogramming of human dermal fibroblast to iPSCs and small colonies can be seen as early as 24 h after the first transfection, even with modifications with the respect to the standard datasheet. The reprogramming procedure used in this protocol offers the advantage of a safe reprogramming, without the risk of infections caused by viral vector-based methods, reduces the cellular defense mechanisms, and allows the generation of xeno-free iPSCs, all critical features that are mandatory for further clinical applications.

Location Instruction-Based Motion Generation for Sequential Robotic Manipulation

Deep learning-based visuomotor control for manipulation are studied recently, which uses a neural network to learn the mapping from images to robotic actions directly. Most previous methods assume there is only one target object in the image. When multiple target objects are present, they are normally regarded as multi-task problems. One-hot vectors are often used to denote different tasks in the visuomotor framework. Nonetheless, these one-hot vector-based methods are easily disturbed and non-extendable. In this paper, a location instruction is used to guide the robot to generate different trajectories in the situation with multiple targets. The proposed framework mainly consists of three modules: the AutoEncoder (AE) network, the motion generation network and the location detection network. AE tries to process the perception information into small-scale feature maps. The location detection network gives the pixel coordinates of the center of the target object in the image. The motion generation network combines the location instruction and the preprocessed perception information and generates an entire motion trajectory to finish the specified manipulation task. For the object stacking tasks with distractors, the proposed method could obtain a success rate of 98%, while the one-hot vector-based method only has a success rate of 56%.

Patch Tensor-Based Multigraph Embedding Framework for Dimensionality Reduction of Hyperspectral Images

Graph-based dimensionality reduction (DR) techniques are of great interest in the field of image processing and especially on the analysis of hyperspectral images (HSIs). Considering the characteristics of hyperspectral data, many different types of graphs were designed to describe the structure of HSIs. Generally, the algorithms based on these graphs achieved promising performance. However, most of them only focus on how to improve the measurement of similarity between the data points by a single graph. Specifically, vector-based graph methods fail to capture the spatial information, while tensor-based graph methods assume that the pixels in each patch tensor belong to the same class, which is not exactly correct in practice. To overcome these shortcomings, this article proposes a patch tensor-based multigraph embedding (PTMGE) framework for the DR of HSIs, in which three different types of subgraphs are constructed to comprehensively describe the intrinsic geometrical structures of HSIs. First, a tensor subgraph is constructed to capture the spatial information and local geometrical structure. Second, for each two neighboring patch tensors in the tensor graph, a bipartite graph is designed to characterize the pixel-based relationships between the patch tensors. Then, considering that the diversity of pixels may be existed in each patch tensor, a pixel-based subgraph is built to describe the inner geometrical structures of every patch tensor. Finally, a novel graph fusion strategy is designed to calculate a final similarity matrix for projection learning. Experiments on three real hyperspectral data sets are conducted, and comparison with some state-of-the-art algorithms validated the effectiveness of our proposed PTMGE method.

A sparse tensor-based classification method of hyperspectral image

Previous studies have demonstrated that spatial information can provide significant improvement for the accuracy hyperspectral image (HSI) classification. However, it remains a challenging task to extract three-dimensional (3-D) features from HSI directly. In this paper, a sparse tensor-based classification (STC) method for HSI is proposed. Different from the traditional vector-based or matrix-based methods, the STC utilizes tensor technique to extract the joint spatial-spectral tensor features. We exploit the principal component analysis (PCA) and the 3-D intrinsic spatial-spectral tensors of HSI to alleviate within-class spatial-spectral variation, and to improve the classification performance simultaneously. First, the HSI is segmented into a number of overlapping 3-D tensor patches, which are modelled as summation of intrinsic spatial-spectral tensors and corresponding variation tensors in the next step. Second, the intrinsic spatial-spectral tensor is decomposed into three matrices and a core tensor by the Tucker decomposition (TKD). Sparsity constraint is enforced on the core tensor to extract joint sparse spatial-spectral features. We utilize tensor-based dictionary learning algorithm to train three dictionaries, in order to extract more discriminative tensor features for classification. Finally, we use the support vector machine (SVM) to perform the pixel-wise classification. Experimental results on real HSI datasets demonstrate the proposed method can achieve accurate and robust classification results, and can provide competitive results to state-of-the-art methods.

ELM-Based Large-Scale Genetic Association Study via Statistically Significant Pattern

Genetic association study (GAS) is a promising tool for detecting and analyzing the cause of complex diseases. The extreme learning machine (ELM) has been successfully applied in a variety of research fields. Yet, as a black box method, it could not measure up to the task of GAS by itself, because it cannot tell us what causes the diseases, which is very crucial to the biologists. In this paper, we propose an ELM-based statistically significant pattern classification framework, which combines ELM with feature vector-based methods to solve the GAS problem efficiently and effectively. In particular: 1) a statistically significant pattern considering in terms of both family wise error rate (FWER) and false discovery rate (FDR) is proposed to control false positives in multiple hypothesis tests, which is necessary in GAS, but ignored by most of the existing methods; 2) an upper bound of the significance of a pattern is deduced to speed up FWER-constrained statistically significant pattern mining in a row enumeration way. Further, a space-effective grid index is devised to dramatically improves the efficiency of FDR-constrained pattern discovery; and 3) an ELM classifier is constructed based on the significant patterns. Comprehensive empirical studies on four real genotype datasets demonstrate much higher efficiency and effectiveness of our proposed framework with respect to the compared methods.

Automatic Identification of Overpass Structures: A Method of Deep Learning

The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns.