Approximate Matching Research Articles

An Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale-Invariant Features in Electric Power Scenes

In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognised by a curvature scale space (CSS) corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalised with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest-neighbour matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results.

Assessment of interfacial properties in a two-layered plate using quasi-static component generation during primary Lamb wave propagation.

This study delves into the feasibility of leveraging quasi-static component (QSC) generation during primary Lamb wave propagation to discern subtle alterations in the interfacial properties of a two-layered plate. Unlike the second-harmonic generation of Lamb waves, QSC generation doesn't necessitate precise phase-velocity matching but rather requires an approximate matching of group velocities to ensure the emergence of cumulative growth effects. This unique characteristic empowers the QSC-based nonlinear ultrasonic method to effectively surmount the limitations associated with inherent dispersion and multimode traits of Lamb wave propagation. Modeling the QSC generation reveals that the integrated amplitude of the QSC pulse, derived from the propagation of the primary Lamb wave tone burst, progressively amplifies with increasing propagation distance. Finite element simulations illustrate an overall decline in the efficiency of QSC generation amidst interfacial degradation. Experimental outcomes obtained via a nonlinear ultrasonic measurement-based setup vividly showcase the cumulative growth effect of QSC generation, evident with the propagation distance under approximate group velocity matching. To substantiate the influence of interfacial properties on QSC generation efficiency, varying thermal fatigue durations under cyclic temperature conditions are employed to simulate subtle changes in the two-layered plate's interfacial properties. The relative nonlinear acoustic parameter exhibits a distinctly sensitive and monotonically decreasing behavior with escalating thermal fatigue durations, corroborating the impact of alterations in interfacial properties on QSC generation efficiency. The alignment between experimental findings and numerical analysis predictions suggests that the effect of QSC generation in primary Lamb wave propagation provides a promising means for sensitively assessing the early-stage degradation of interfacial properties in layered plates.

Long short-term memory (LSTM) neural networks for in situ particle velocity determination in material strength experiments under ramp wave compression

In the experiments of measuring the strength of materials under ramp compression, accurately determining in situ particle velocity is crucial for calculating material sound speed during loading–unloading path and materials strength under high pressure. This paper proposes a machine learning approach that utilizes Long Short-Term Memory (LSTM) neural networks and Bayesian optimization algorithms to enhance the analysis of data from ramp compression strength measurement experiments. This method leverages LSTM neural networks to uncover the complex relationship between the rear interface velocity of the sample and the in situ particle velocity in numerical simulations. By using a well-trained network model, it enables direct interpretation of experimental data, leading to accurate predictions of key physical quantities along the loading and unloading paths in ramp compression experiments. A comparative analysis between theoretical curves from numerical simulations and LSTM neural network predictions shows a high degree of consistency. This approach is applied to ramp compression experiments on Ta and CuCrZr materials, demonstrating superior accuracy over the free-surface approximation and incremental impedance matching methods. Additionally, this method relies solely on the equation of state during numerical computations, eliminating the need for the complex constitutive equations required by the transfer function method, thus enhancing data processing efficiency and practicality.

Methods for the Construction and Editing of an Efficient Control Network for the Photogrammetric Processing of Massive Planetary Remote Sensing Images

Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude.

Programmable Multiplexed Nucleic Acid Detection by Harnessing Specificity Defect of CRISPR-Cas12a.

CRISPR-Cas12a works like a sophisticated algorithm in nucleic acid detection, yet its challenge lies in sometimes failing to distinguish targets with mismatches due to its specificity limitations. Here, the mismatch profiles, including the quantity, location, and type of mismatches in the CRISPR-Cas12a reaction, are investigated and its various tolerances to mismatches are discovered. By harnessing the specificity defect of the CRISPR-Cas12a enzyme, a dual-mode detection strategy is designed, which includes approximate matching and precise querying of target sequences and develop a programmable multiplexed nucleic acid assay. With the assay, 14 high-risk human papillomavirus (HPV) subtypes are simultaneously detected, collectively responsible for 99% of cervical cancer cases, with attomolar sensitivity. Specifically, the assay not only distinguishes HPV16 and HPV18, the two most common subtypes but also detects 12 other high-risk pooled HPV subtypes. To enable low-cost point-of-care testing, the assay is incorporated into a paper-based microfluidic chip. Furthermore, the clinical performance of the paper-based microfluidic chip is validated by testing 75 clinical swab samples, achieving performance comparable to that of PCR. This programmable multiplexed nucleic acid assay has the potential to be widely applied for sensitive, specific, and simultaneous detection of different pathogens.

Fuzzy Logic-Driven Natural Language Processing in Pharma Supply Chain Analytics

Fuzzy logic offers a method for handling uncertainty and imprecision, proving valuable in natural language processing (NLP). Fuzzy search, a key component, enhances search functionality by permitting approximate matches. This study examines the application of fuzzy search techniques in wholesale pharmaceutical distribution, where data retrieval accuracy is crucial for public health and safety. We present two case studies, each showcasing specific fuzzy search methods designed to overcome unique data retrieval challenges. A Python implementation demonstrates the practical application of these techniques to enhance search accuracy and efficiency in large pharmaceutical datasets. Our results highlight fuzzy logic's potential to revolutionize information retrieval systems. By offering practical insights and technical guidance, this research aims to enable pharmaceutical industry stakeholders to effectively implement fuzzy search techniques, leading to improved data management and decision-making processes.

The Radiative–Convective Gap: Fact or Fiction?

Gaia characterizes the stellar populations of nearby open clusters with unprecedented precision. We investigate the Böhm-Vitense gap, which has been found as a prominent feature in the stellar sequence of open clusters. Using PARSEC isochrone fitting, we derive astrophysical parameters for more than 1100 bona fide single stars each in Praesepe, identify more than 1100 bona fide single stars in the α Persei (Melotte 20) open cluster, and confirm their approximate match in terms of age (≈710 and ≈45 Myr) and metallicity ([M/H] ≈ +0.15 and ≈+0.13 dex) to the Hyades and Pleiades, respectively. By merging data of the cluster pairs, we improve number statistics. We do not find a clear gap in the combined observational G abs versus BP − RP color–magnitude diagram (CMD) in the stellar mass range corresponding to the location of the Böhm-Vitense gap. We reproduce gaps in simulated Hyades-type CMDs randomly drawn from an initial mass function. There is no strong evidence for a discontinuity originating in the transition from radiative to convective energy transport in the stellar photosphere. We conclude that the observed gaps in the stellar sequences of open clusters could be explained by small number statistics and the uneven mass–color relation at the transition from spectral type A to F.

B-move: Faster Lossless Approximate Pattern Matching in a Run-Length Compressed Index.

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.'s r-index and Nishimoto and Tabei's move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.'s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient, lossless approximate pattern matching in run-length compressed space. It achieves bidirectional character extensions up to 7 times faster than the br-index, closing the performance gap with FM-index-based alternatives. For locating occurrences, b-move performs and operations up to 7 times faster than the br-index. At the same time, it maintains the favorable memory characteristics of the br-index, for example, all available complete E. coli genomes on NCBI's RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license.

Enhancing disturbance rejection in boost converter for CPL: A controller design approach with partial pole placement and approximate model matching

In DC microgrids, the cascade operation of the DC-DC converters leads to a constant power load fed by the boost converter, resulting in unstable behavior in addition to the non-minimum phase dynamics. An enhanced load disturbance rejection control scheme based on partial pole placement and an approximate model matching technique is proposed. This approach is inspired by the well-known direct synthesis method to achieve optimal load disturbance rejection performance. The effectiveness of the proposed control scheme has been validated through hardware implementation, demonstrating its capability in setpoint tracking and load disturbance rejection under variations in load power and input voltage. The robust stability of the suggested scheme has been investigated through the Kharitonov theorem and Monte-Carlo simulation, considering different performance matrices. The suitability of the presented scheme has been established by analyzing the comparative performance of recently reported works. The proposed controller reduces peak overshoot and settling time by ∼50%, handles system uncertainties up to 75%, and outperforms FOPID controllers tuned with particle swarm optimization, queen bee genetic algorithm, and chaos game optimization methods.

Lossless Approximate Pattern Matching: Automated Design of Efficient Search Schemes.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern. Together, these searches ensure the identification of all approximate occurrences of a search pattern within a predefined limit of k errors. While existing literature offers designed schemes for up to k = 4 errors, designing search schemes for larger k values incurs escalating computational costs. Our method integrates a greedy algorithm and a novel Integer Linear Programming (ILP) formulation to design efficient search schemes for up to k = 7 errors. Comparative analyses demonstrate the superiority of our ILP-optimal schemes over alternative strategies in both theoretical and practical contexts. Additionally, we propose a dynamic scheme selection technique tailored to specific search patterns, further enhancing efficiency. Combined, this yields runtime reductions of up to 53% for higher k values. To facilitate search scheme generation, we present Hato, an open-source software tool (AGPL-3.0 license) employing the greedy algorithm and utilizing CPLEX for ILP solving. Furthermore, we introduce Columba 1.2, an open-source lossless read-mapper (AGPL-3.0 license) implemented in C++. Columba surpasses existing state-of-the-art tools by identifying all approximate occurrences of 100,000 Illumina reads (150 bp) in the human reference genome within 24 seconds (maximum edit distance of 4) and 75 seconds (maximum edit distance of 6) using a single CPU core. Notably, our study showcases Columba's capability to align 100,000 reads of length 50, with high error rates and up to an edit distance of 7, in a mere 2 hours and 15 minutes. This achievement is unmatched by other lossless aligners, which require over 3 hours for edit distance 5 alignments. Moreover, Columba exhibits a mapping rate four times higher than that of a lossy tool for this dataset.

Dynamic Euclidean bottleneck matching

A fundamental question in computational geometry is for a set of input points in the Euclidean space, that is subject to discrete changes (insertion/deletion of points at each time step), whether it is possible to maintain an exact/approximate minimum weight perfect matching and/or bottleneck matching (a perfect matching that minimizes the length of the longest matched edge), in sublinear update time. In this work, we answer this question in the affirmative for points on a real line and for points in the plane with a bounded geometric spread.For a set P of n points on a line, we show that there exists a dynamic algorithm that maintains an exact bottleneck matching of P and supports insertion and deletion in O(log⁡n) time. Moreover, we show that a modified version of this algorithm maintains an exact minimum-weight perfect matching with O(log⁡n) update (insertion and deletion) time. Next, for a set P of n points in the plane, we show that a (62)-factor approximate bottleneck matching of Pk, at each time step k, can be maintained in O(log⁡Δ) amortized time per insertion and O(log⁡Δ+|Pk|) amortized time per deletion, where Δ is the geometric spread of P (the ratio between the diameter of P and the distance between the closest pair of points in P).

Sigmoni: classification of nanopore signal with a compressed pangenome index.

Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10-100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications. Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.

B-move: faster bidirectional character extensions in a run-length compressed index.

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.'s r-index and Nishimoto and Tabei's move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.'s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient bidirectional character extensions in run-length compressed space. It achieves bidirectional character extensions up to 8 times faster than the br-index, closing the performance gap with FM-index-based alternatives, while maintaining the br-index's favorable memory characteristics. For example, all available complete E. coli genomes on NCBI's RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. Thus, b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license.

Resume Application Tracking System with Google Gemini Pro

Abstract: Resume Application Tracking System with Google Gemini Pro provides an innovative solution to the problems in the recent hiring. The main aim of this system is to help the job seekers in order to grab an oppurtunity in the competitive job market and ensure that the process of resume screening goes smoothly. The technology used in this system is Google Gemini Pro, the most capable and large language model created by Google DeepMind. This technology is capable of interpreting code, video, audio, image, text and helps to increase the accuracy and speed of resume screening. The system also makes use of Streamlit for the user interface and PyPDF2 for direct text extraction from resumes. Users can provide the appropriate job description and upload their resumes, which will then be analyzed and the system will display the approximate matching percentage, missing keywords and a brief profile summary. The system will automatically evaluate and classify resumes in real-time applications, delivering recruiters an easy way to find eligible applicants. This approach is used in wide range of sectors and can be beneficial as the job market is down due to recession. Therefore, the goal of this system is to tranform the resume processing industry, saving recruiters time and money while also raising the standard of candidate selection.

Modification and completion of geological structure knowledge graph based on pattern matching

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

CHDmap: One Step Further Toward Integrating Medicine-Based Evidence Into Practice

Abstract Evidence-based medicine, rooted in randomized controlled trials, offers treatment estimates for the average patient but struggles to guide individualized care. This challenge is amplified in complex conditions like congenital heart disease due to disease variability and limited trial applicability. To address this, medicine-based evidence was proposed to synthesize information for personalized care. A recent article introduced a patient similarity network, CHDmap, which represents a promising technical rendition of the medicine-based evidence concept. Leveraging comprehensive clinical and echocardiographic data, CHDmap creates an interactive patient map representing individuals with similar attributes. Using a k-nearest neighbor algorithm, CHDmap interactively identifies closely resembling patient groups based on specific characteristics. These approximate matches form the foundation for predictive analyses, including outcomes like hospital length of stay and complications. A key finding is the tool’s dual capacity: not only did it corroborate clinical intuition in many scenarios, but in specific instances, it prompted a reevaluation of cases, culminating in an enhancement of overall performance across various classification tasks. While an important first step, future versions of CHDmap may aim to expand mapping complexity, increase data granularity, consider long-term outcomes, allow for treatment comparisons, and implement artificial intelligence–driven weighting of various input variables. Successful implementation of CHDmap and similar tools will require training for practitioners, robust data infrastructure, and interdisciplinary collaboration. Patient similarity networks may become valuable in multidisciplinary discussions, complementing clinicians’ expertise. The symbiotic approach bridges evidence, experience, and real-life care, enabling iterative learning for future physicians.

Survey on Context Based Identification of Customer Name Variations using ML Techniques

Abstract: The demand for automated validation of new customer company accounts, with a specific emphasis on resolving name variations within large databases, is steadily increasing. This surge in demand is a direct response to the significant scale at which data for new customers is being input into CRM systems, necessitating manual validation and correction processes. The primary objective of this survey paper is to identify machine learning methods capable of addressing the challenge of resolving name variations in extensive databases. To achieve this goal, we propose a multi-step approach. Firstly, we employ an approximate string matching algorithm with high computational speed to identify matches with a very high percentage of accuracy. In cases where such matches are not found in the system, we utilize named entity recognition techniques to annotate company names and their suffixes, such as INC, PVT, LTD, CORP, and CO, which may appear in various forms. To resolve abbreviation disambiguity, we explore the application of machine learning algorithms, including the naïve Bayes classifier, decision trees, and Support Vector Machines. In this survey paper, we conclude by presenting potential approximate string matching algorithms, a named entity recognition method, and a model for resolving abbreviation disambiguity. Our review not only provides a comprehensive overview of the current state of research in this area but also highlights gaps in the existing knowledge, offering valuable insights for future research and practical application.

Validation of a Thai artificial chatmate designed for cheering up the elderly during the COVID-19 pandemic.

The COVID-19 pandemic severely affected populations of all age groups. The elderly are a high-risk group and are highly vulnerable to COVID-19. Assistive software chatbots can enhance the mental health status of the elderly by providing support and companionship. The objective of this study was to validate a Thai artificial chatmate for the elderly during the COVID-19 pandemic and floods. Chatbot design includes the establishment of a dataset and emotional word vectors in which data consisting of emotional sentences were converted into the word vector form using a pre-trained word2vec model. A word vector was then input into a convolutional neural network (CNN) and trained until the model converges using sentence embedding and similarity word segmentation. Sentence vectors were generated by averaging each word vector using an averaged vector method. For approximate similarity matching, the Annoy library was used to create the indices in tree sorting. Data were collected from 22 elderly and assessed by the Post-Study System Usability Questionnaire (PSSUQ). The study revealed that 72.73% of the respondents found the chatbot easy to learn and use, 63.64% of the respondents found the chatbot can autonomously determine the next course of action, and 59.09% of the respondents believed that troubleshooting guidelines were provided for overcoming errors. The accuracy of the chatbot providing a reasonable response is 56.20±13.99%. Most users were satisfied with the chatbot system. The proposed chatbot provided considerable essential insights into the development of assistance systems for the elderly during the coronavirus pandemic (COVID-19) and during the period of national disasters. The model can be expanded to other applications in the future.

Machine learning-based vorticity evolution and super-resolution of homogeneous isotropic turbulence using wavelet projection

A wavelet-based machine learning method is proposed for predicting the time evolution of homogeneous isotropic turbulence where vortex tubes are preserved. Three-dimensional convolutional neural networks and long short-term memory are trained with a time series of direct numerical simulation (DNS) data of homogeneous isotropic turbulence at the Taylor microscale Reynolds number 92. The predicted results are assessed by using the flow visualization of vorticity and statistics, e.g., probability density functions of vorticity and enstrophy spectra. It is found that the predicted results are in good agreement with DNS results. The small-scale flow topology considering the second and the third invariants of the velocity gradient tensor likewise shows an approximate match. Furthermore, we apply the pre-trained neural networks to coarse-grained vorticity data using super-resolution. It is shown that the super-resolved flow field well agrees with the reference DNS field, and thus small-scale information and vortex tubes are well regenerated.

Map matching queries on realistic input graphs under the Fréchet distance

Map matching is a common preprocessing step for analysing vehicle trajectories. In the theory community, the most popular approach for map matching is to compute a path on the road network that is the most spatially similar to the trajectory, where spatial similarity is measured using the Fréchet distance. A shortcoming of existing map matching algorithms under the Fréchet distance is that every time a trajectory is matched, the entire road network needs to be reprocessed from scratch. An open problem is whether one can preprocess the road network into a data structure, so that map matching queries can be answered in sublinear time. In this paper, we investigate map matching queries under the Fréchet distance. We provide a negative result for geometric planar graphs. We show that, unless SETH fails, there is no data structure that can be constructed in polynomial time that answers map matching queries in O (( pq ) 1 − δ ) query time for any δ > 0, where p and q are the complexities of the geometric planar graph and the query trajectory, respectively. We provide a positive result for realistic input graphs, which we regard as the main result of this paper. We show that for c -packed graphs, one can construct a data structure of \(\tilde{O}(cp) \) size that can answer (1 + ε)-approximate map matching queries in \(\tilde{O}(c^4 q \log ^4 p) \) time, where \(\tilde{O}(\cdot) \) hides lower-order factors and dependence on ε.