Matching Algorithm Research Articles

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

ObjectiveDevelop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU). MethodsAnalyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI). ResultsA slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment. ConclusionsPAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

Enhanced intrusion detection with fiber optic sensors in rainy weather conditions: a comparative study of phase parameter matching and cross rebuilding algorithms for higher end communication

Enhanced intrusion detection with fiber optic sensors in rainy weather conditions: a comparative study of phase parameter matching and cross rebuilding algorithms for higher end communication

Research on the railway multi-source homonymous geographical entity matching algorithm based on dynamic time warping

This study aims to explore an efficient technique for matching multisource homonymous geographical entities in railways to address the identification issues of homonymous geographical entities. Focusing on railway line vector spatial data, this research investigates the matching problem of multisource homonymous geographical entities. Building on statistical feature matching of attribute data, a curve similarity calculation method based on the DTW algorithm is designed to achieve better local elastic matching, overcoming the limitations of the Fréchet algorithm. The empirical study utilizes railway line layer data from two data sources within Beijing’s jurisdiction, fusing 6237 segment lines from source 2 with 105 long lines from source 1. The structural comparison between the two data sources is conducted through statistical methods, applying cosine similarity and the maximum similarity value of TF-IDF for text similarity calculation. Finally, Python is used to implement the DTW algorithm for curve similarity. The experimental results show an average DTW distance of 3.92, a standard deviation of 4.63, and a mode of 0.005. Similarity measurement results indicate that 95.53% of records are within the predetermined threshold, demonstrating the effectiveness and applicability of the method. The findings significantly enhance the accuracy of railway data matching, promoting the informatization of the railway industry, and hold substantial significance for improving railway operational efficiency and system performance.

Domain Wall Color Code.

We introduce the domain wall color code, a new variant of the quantum error-correcting color code that exhibits exceptionally high code-capacity error thresholds for qubits subject to biased noise. In the infinite bias regime, a two-dimensional color code decouples into a series of repetition codes, resulting in an error-correcting threshold of 50%. Interestingly, at finite bias, our color code demonstrates thresholds identical to those of the noise-tailored XZZX surface code for all single-qubit Pauli noise channels. The design principle of the code is that it introduces domain walls which permute the code's excitations upon domain crossing. For practical implementation, we supplement the domain wall code with a scalable restriction decoder based on a matching algorithm. The proposed code is identified as a comparably resource-efficient quantum error-correcting code highly suitable for realistic noise.

Respecting priorities versus respecting preferences in school choice: When is there a trade-off?

A classic trade-off that school districts face when deciding which matching algorithm to use is that it is not possible to always respect both priorities and preferences. The student-proposing deferred acceptance algorithm (DA) respects priorities but can lead to inefficient allocations. We identify a new condition on school choice markets under which DA is efficient. Our condition generalizes earlier conditions by placing restrictions on how preferences and priorities relate to one another only on the parts that are relevant for the assignment. Whenever there is a unique allocation that respects priorities, our condition captures all the environments for which DA is efficient. We show through stylized examples and simulations that our condition significantly expands the range of known environments for which DA is efficient. We also discuss how our condition sheds light on existing empirical findings.

Leveraging Fuzzy Match: Building an Automated Record Linkage Template in SAS Dataflux

Background ICES has conducted Probabilistic Record Linkage (PRL) for heath and non-health data for over 15 years. The PRL process has made data linkable to many ICES data holdings. However, due to the manual gray area resolution, record linkages for large datasets are time consuming with inconsistent results. ApproachAdapting from many years of experience with PRL methodology, record linkage templates were built in SAS Dataflux. By using combination of deterministic linkage and fuzzy match algorithms, the multi-pass linkage strategy is maintained by passing unlinked records in subsequent linkage comparison cycles. The new template uses personal identifiers of multiple given names, surnames, date of birth, death date (if applicable) and sex. In each pass, the pool of possible matched pairs is created by merging on low sensitive matchcodes with different conditions. Then, using a rule-based approach, possible pairs are examined, flags are assigned, and survival rules are applied to select the best matched records. ResultsBy feeding a pre-prepared linkage file along with updating the input and output file names, the record linkage job will be conducted automatically in SAS Dataflux through submitting a “run” command. It significantly reduces the turnaround time with no clerical review required, achieves a similar linkage rate as PRL consistently, and is utilized by various research projects. ConclusionThe automated SAS Dataflux record linkage template is more efficient than the traditional PRL process. It eliminates the human intervention of gray area resolution, while reducing project turnaround times, and maintaining a comparable accuracy and linkage rate.

As our population changes, so must we: how race and ethnicity data merging has evolved over time in the Wisconsin Administrative Data Core

The Institute for Research on Poverty (IRP), centered at the University of Wisconsin-Madison in Wisconsin, USA, began collaboration with various Wisconsin state agencies in 1985 to link data between disparate state agencies. These initial efforts have since evolved into the Wisconsin Administrative Data Core (WADC) which is an individual-level population dataset connecting extensive administrative data spanning decades of Wisconsin social services program history. Since its purpose is to facilitate evidence-driven policymaking and support program evaluation, it is essential that our data linking efforts accurately represent our Wisconsin population. Since its inception, the WADC has adapted to changes in how race and ethnicity are collected and reported. While Wisconsin has a racial makeup that is generally less diverse than the national population, the number of non-white and multi-racial identifying residents has increased over time. Furthermore, varying state agency demographic collection and reporting methods have fueled the need for our linking methods to change, too. IRP initially implemented standard data linkage methods using a mixture of probabilistic and deterministic matching of personal identifying information. But as the racial composition of Wisconsin residents changed over time, our linking assumptions induced implicit biases. In response, we updated our matching algorithm to account for the cultural nuances in how personally identifying information is assigned and reported within various racial and ethnic subgroups. Furthermore, we prioritized values from data sources that were self-reported rather than assigned from a third-party. These methodological modifications have improved the WADC’s reliability and representativeness of the population.

An Underwater Stereo Matching Method: Exploiting Segment-Based Method Traits without Specific Segment Operations

Stereo matching technology, enabling the acquisition of three-dimensional data, holds profound implications for marine engineering. In underwater images, irregular object surfaces and the absence of texture information make it difficult for stereo matching algorithms that rely on discrete disparity values to accurately capture the 3D details of underwater targets. This paper proposes a stereo method based on an energy function of Markov random field (MRF) with 3D labels to fit the inclined plane of underwater objects. Through the integration of a cross-based patch alignment approach with two label optimization stages, the proposed method demonstrates features akin to segment-based stereo matching methods, enabling it to handle images with sparse textures effectively. Through experiments conducted on both simulated UW-Middlebury datasets and real deteriorated underwater images, our method demonstrates superiority compared to classical or state-of-the-art methods by analyzing the acquired disparity maps and observing the three-dimensional reconstruction of the underwater target.

Pregnant People-Infant Linkage for Surveillance

Objectives and ApproachAs maternal mortality and morbidity continue to progress, better data is needed to comprehensively understand the problem, especially in underserved populations who are differentially impacted. Surveillance in safety-net populations is complicated by care fragmentation and interoperability challenges between primary care and hospital settings. . AllianceChicago (AC), a health center-controlled network, supports a common data infrastructure across a set of Federally Qualified Health Centers (FQHCs) in Chicago that is further linked to a distributed query infrastructure connecting Chicago’s major health systems known as CAPriCORN. Leveraging electronic health record (EHR) data collected by the AC and CAPriCORN consortium, the objective of this surveillance effort was to connect infant data with maternal data while protecting privacy. We developed a deterministic matching algorithm that utilized multiple approaches and technologies for Pregnant People-Infant (PPI) pair identification. The linkage process involved a five-step method in which each step is recorded within the linkage database and the confidence interval is calculated. ResultsPreliminary analyses are ongoing. Through each step of the linkage process, the confidence interval increased with the increase of matching nodes, highlighting its accumulative accuracy. The result demonstrates the feasibility of linking maternal and baby healthcare characteristics using a range of clinical and demographic variables captured in pseudonymized hospital and ambulatory data. Conclusions and ImplicationsOur approach improves the capacity of EHR data for surveillance while protecting patient privacy and preserving data confidentiality. Triangulating outcomes recorded in different settings can help improve data quality for research and surveillance.

Optimization of whole slide imaging scan settings for computer vision using human lung cancer tissue.

Digital pathology has become increasingly popular for research and clinical applications. Using high-quality microscopes to produce Whole Slide Images of tumor tissue enables the discovery of insights into biological aspects invisible to the human eye. These are acquired through downstream analyses using spatial statistics and artificial intelligence. Determination of the quality and consistency of these images is needed to ensure accurate outcomes when identifying clinical and subclinical image features. Additionally, the time-intensive process of generating high-volume images results in a trade-off that needs to be carefully balanced. This study aims to determine optimal instrument settings to generate representative images of pathological tissue using digital microscopy. Using various settings, an H&E stained sample was scanned using the ZEISS Axio Scan.Z1. Next, nucleus segmentation was performed on resulting images using StarDist. Subsequently, detections were compared between scans using a matching algorithm. Finally, nucleus-level information was compared between scans. Results indicated that while general matching percentages were high, similarity between information from replicates was relatively low. Additionally, settings resulting in longer scanning times and increased data volume did not increase similarity between replicates. In conclusion, the scan setting ultimately deemed optimal combined consistent and qualitative performance with low throughput time.

Exploring the predictive values of CRP and lymphocytes in coronary artery disease based on a machine learning and Mendelian randomization

PurposeTo investigate the predictive value of leukocyte subsets and C-reactive protein (CRP) in coronary artery disease (CAD).MethodsWe conducted a Mendelian randomization analysis (MR) on leukocyte subsets, C-reactive protein (CRP) and CAD, incorporating data from 68,624 patients who underwent coronary angiography from 2010 to 2022. After initial screening, clinical data from 46,664 patients were analyzed. Techniques employed included propensity score matching (PSM), logistic regression, lasso regression, and random forest algorithms (RF). Risk factors were assessed, and the sensitivity and specificity of the models were evaluated using receiver operating characteristic (ROC) curves. Additionally, survival analysis was conducted based on a 36-month follow-up period.ResultsThe inverse variance weight (IVW) analysis showed that basophil count (OR 0.92, 95% CI: 0.84–1.00, P = 0.048), CRP levels (OR 0.87, 95% CI: 0.73–1.00, P = 0.040), and lymphocyte count (OR 1.10, 95% CI: 1.04–1.16, P = 0.001) are significant risk factors for CAD. Using LASSO regression, logistic regression, and RF analysis, both CRP and lymphocyte counts were consistently identified as risk factors for CAD, prior to and following PSM. The ROC curve analysis indicated that the combination of lymphocyte and CRP levels after PSM achieves a higher diagnostic value (0.85). Survival analysis revealed that high lymphocyte counts and low CRP levels are associated with a decreased risk of Major Adverse Cardiovascular Events (MACE) (P < 0.001). Conversely, a higher CRP level combined with lymphocyte counts correlates with a poorer prognosis.ConclusionThere is a causal relationship between lymphocytes, CRP and CAD. The combined assessment of CRP and lymphocytes offers diagnostic value for CAD. Furthermore, high CRP levels coupled with low lymphocyte counts are associated with a poor prognosis.

Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica

The noisy Red-Throated Caracara (Ibycter americanus) is a species whose population has inexplicably declined across much of its range and is now rare in the Pacific and Caribbean slopes of Costa Rica. Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. Although these studies are limited in the country, it represents an area with great interdisciplinary potential for technological advances. This study focused on the use of Pattern Matching to detect the presence of the Red-Throated Caracara in northern Costa Rica using a large number of sound recordings and its validation with metrics such as Accuracy, Precision Negative predictive value, Sensitivity, Specificity and Unweighted average recall. The results showed a moderate performance of the model by obtaining accuracy and precision values of 0.71 compared to the values obtained in other investigations in which the reported model was used. Therefore, we suggest exploring new techniques and methods to improve the detection of the species, considering the particular acoustic structure, the repertoire of sounds of the species and similarities with vocalizations of other species. This similarity could indicate a supposed anti-predator defense behavior by “imitating” the sounds of other species with which it shares habitat. To optimize this acoustic detection, we recommend using complementary techniques such as noise filters that improve the quality and precision of the data.

A neural algorithm for computing bipartite matchings

Finding optimal bipartite matchings-e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review-is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons "compete" with each other to "win" muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems.

Retraction Note: A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in semantic web

Retraction Note: A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in semantic web

A Novel Algorithm for Enhancing Terrain-Aided Navigation in Autonomous Underwater Vehicles

The position error in an inertial navigation system (INS) for autonomous underwater vehicles (AUVs) increases over time. Terrain-aided navigation can assist in correcting these INS position errors. To enhance the matching accuracy under large initial position errors, an improved terrain matching algorithm comprising terrain contour matching (TERCOM), particle swarm optimization (PSO), and iterative closest contour point (ICCP), named TERCOM-PSO-ICCP, is proposed. Initially, an enhanced TERCOM with an increased rotation angle is utilized to minimize heading errors and reduce the initial position error. The similarity extremum approach evaluates the initial matching outcomes, leading to an enhanced accuracy in the initial results. Next, artificial bee colony (ABC)-optimized PSO is employed for secondary matching to further reduce the initial position error and narrow the matching area. Finally, the ICCP, using the Mahalanobis distance as the objective function, is applied for the third matching, leveraging the ICCP’s fine search capabilities. The effective combination of these three algorithms significantly improves the terrain-aided navigation matching effect. Two tests show that the improved TERCOM-PSO-ICCP effectively reduces the matching error and corrects the position of the INS.

Efficacy of revascularization in CTO patients based on hibernating myocardium therapy.

The effectiveness of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) is still uncertain, especially for patients with ischemic left ventricular dysfunction. This study aimed to assess hibernating myocardium (HM), as determined by single-photon emission computed tomography (SPECT) and 18F-FDG positron emission tomography (PET), and to compare the benefits of PCI and optimal medical therapy (OMT). A retrospective study collected data from 332 patients with CTO and ischemic left ventricular dysfunction. The study compared patients who underwent PCI or OMT via propensity score matching (PSM) analysis which was performed with a 1:2 matching protocol using the nearest neighbour matching algorithm. The primary endpoint of the study was the occurrence of major adverse cardiac events (MACE), defined as a composite of cardiac death, readmission for worsening heart failure (WHF), revascularization and myocardial infarction (MI). After PSM, there were a total of 246 individuals in the PCI and OMT groups. Following Cox regression, hibernating myocardium/total perfusion defect (HM/TPD) was identified as an independent risk factor (hazard ratio (HR): 1.03, 95% confidence interval (CI): 1.008-1.052, p = .007). The cut-off value of HM/TPD was 38%. The results of the subgroup analysis suggest that for patients with HM/TPD >38%, the OMT group had a greater risk of MACE (p = .035). A sensitivity analysis restricting patients with single-vessel CTO lesions, HM/TPD remained an independent predictor (HR 1.025, 95% CI 1.008-1.043, p = .005). HM/TPD is an independent predictor of MACE, and for patients with HM/TPD > 38%, CTO-PCI had a lower risk of MACE compared with OMT. However, further validation is still needed through large-scale studies.

Simple and Accurate Rotary Sensor Based on the Rotary Angle Gauss–Newton Image Matching Algorithm

Simple and Accurate Rotary Sensor Based on the Rotary Angle Gauss–Newton Image Matching Algorithm

Enhanced precision in greenhouse tomato recognition and localization

The core challenge in realizing automatic tomato harvesting in greenhouse environments lies in the precise identification and localization of the fruits. This paper introduces a comprehensive approach based on an improved YOLOv5 detection algorithm and optimized binocular stereo vision technology. Firstly, by introducing the C3-Transformer Encoder (CTM) structure and Bidirectional Feature Pyramid Network (Bi-FPN), this study enhanced the model’s ability to recognize tomatoes, especially under complex backgrounds and occlusion conditions. After field testing, the mAP50 accuracy reached 97.1%, an increase of 1.2 percentage points, enhancing detection precision. In addition, the ZED binocular camera was used, and the census stereo matching algorithm was optimized, significantly reducing disparity errors, thereby improving the accuracy of depth information. This allows the model to accurately calculate the three-dimensional spatial position of tomatoes obscured by branches and leaves, greatly improving the efficiency of the harvesting robot. Through field debugging verification with the harvesting robot, the method proposed in this study has shown high accuracy and reliability in the recognition and localization of tomatoes in complex greenhouse environments.

Colour Matching Algorithm in Artificial Intelligence-based Chinese Painting Teaching

Colour Matching Algorithm in Artificial Intelligence-based Chinese Painting Teaching

Optimization of process parameters in laser cladding multi channel forming using MVBM-NSGA-II method

ABSTRACT Laser cladding multi-channel forming is a complex, nonlinear process characterized by multi-parameter coupling. To determine the technology parameters, an optimization method of multivariate black-box prediction model (MVBM) matching non-dominated sequential genetic algorithm (NSGA-II) was constructed. Multi-channel LC orthogonal experiments were designed. The MVBM was established with process technology parameters as inputs and characteristic parameters reflecting the quality (surface hardness, dilution rate) as responses. The set of optimal solutions for technology parameters by the NSGA-II. The experimental results showed that, under the optimum parameters, the average hardness of the optimized increased by 6.1%, and the dilution rate was reduced by 49.8%. The dilution rate was maintained within the optimal range. The study’s findings can provide theoretical support for optimizing LC multi-objective technology parameters and improving coating quality control.