Multiple Matching Research Articles

KRL_MLCCL: Multi-label classification based on contrastive learning for knowledge representation learning under open world

Knowledge representation learning(KRL) transforms knowledge graph(KG) from symbol space to vector space. However, KRL under open world assumption(OWA) is deeply trapped in the dilemma of lack of labels due to difficulty or high cost in labeling. To address this problem, we propose KRL_MLCCL:Multi-Label Classification based on Contrastive Learning(CL) Knowledge Representation Learning method. Specifically, (1)we formalize a problem of solving true knowledge graph objects(KGOs) matchings(KGOMs) under the OWA in the original KGOM sample space(KGOMSS)(multi-label classification with one known true matching(positive-example)). (2)we solve the problem in the new KGOMSS, generated through augmenting the true matching according to CL’s idea(multi-label classification with multiple known true matching). (3)we score the true matchings based on hermitian inner product and softmax and minimize a negative logarithm likelihood loss to establish KRL_MLCCL model preliminarily. (4)we migrate the learned model back to the original KGOMSS to solve the true matching problem. We creatively design and apply a positive-example augmentation way of CL enabling KRL_MLCCL with back migration ability: “pulling KGOs in true matching close and pushing KGOs in false matching away”, which helps KRL out of the labels shortage dilemma faced in modeling. We also propose a negative-example noise filtering algorithm to enhance this ability. The open world entity prediction(OWEP) experiment on dataset FB15K-237-OWE shows that the performance of KRL_MLCCL is increased by 3% in Hits@10 and 1.32% in MRR compared with the state-of-the-art in the baselines. The experiments of OWEP in KG also show that KRL_MLCCL has a better back migration ability.

Electrostatic self-assembly sandwich-like 2D/2D NiFe-LDH/MXene heterostructure for strong microwave absorption

MXene has great application potential in electromagnetic (EM) wave absorbers because of its high attenuation ability; however, self-stacking and excessively high conductivity are major obstacles to its widespread use. To address these issues, a NiFe layered double hydroxide (LDH)/ MXene composite with two-dimensional (2D)/2D sandwich-like heterostructure was constructed through electrostatic self-assembly. The NiFe-LDH not only acts as an intercalator to prevent self-stacking of the MXene nanosheets, but also serves as a low-dielectric choke valve to optimize impedance matching. At a thickness of 2 mm and filler loading of 20 wt%, the minimum reflection loss (RLmin) value could reach −58.2 dB, and the absorption mechanism was analyzed based on multiple reflection, dipole/interfacial polarization, impedance matching, and synergy between dielectric and magnetic losses. Furthermore, the simulation of the radar cross section (RCS) further confirmed the efficient absorption properties and application prospects of the present material. Our work demonstrates that designing sandwich structures based on 2D MXene is an effective way to improve the performance of EM wave absorbers.

New insights into the source of the boron memory effect and implications for accurate boron isotope measurements.

The boron (B) memory effect is a concern for B isotope analysis in inductively-coupled plasma mass spectrometry and a potential cause of poor data comparability between laboratories. It is widely assumed that the memory resides in water droplets on the surface of the spray chamber. However, even without the use of the spray chamber, background subtractions are still required to generate accurate data, therefore additional causes for the memory effect exist, which are investigated here. Different parts of the mass spectrometer were examined to pinpoint the source of a particularly high B background. After identifying the torch as the source of the background, different parts of the torch were soaked in dilute nitric acid, which was analyzed for B over time. B was leached out of the tip of the outer quartz tube of the torch in a fashion similar to borosilicate glass, which suggests the incorporation of B into the silica structure of the torch at high temperatures. Running 3% nitric acid washes effectively reduces the background. B background compositions change based on the solutions run beforehand, therefore different blank subtraction methods generate systematic differences. A new background subtraction method that utilizes B isotope ratios improved the precision by up to 0.14‰. The addition of a water wash step before sample elution led to smaller eluent volumes and improved matrix matching without causing a B breakthrough. An important part of the B memory derives from the torch glass, which incorporates B from sample solutions at high temperatures. Multiple nitric acid washes, matrix matching, blank subtraction, and standard sample bracketing generated accurate B isotope analyses with background/signal ratios as high as 10%, without the need for hazardous chemicals as washes. B isotope values of two sediment standards that represent average post-Archean continental crust were reported.

Order-Preserving Multiple Pattern Matching in Parallel

The order-preserving multiple pattern matching problem is to find all substrings of T whose relative orders are the same for any pattern in a set of patterns. Various sequential algorithms have been studied for the order-preserving multiple pattern matching problems. In this paper, we propose two parallel algorithms, each of which uses Aho–Corasick automata and fingerprint tables, respectively. We also present experimental results of comparing the execution times of each parallel algorithm on various types of time-series data.

Few-mode optical parametric amplification in a multiple quasi-phase-matched thin-film lithium niobate waveguide

Mode-division multiplexing (MDM) technology, an effective approach to boost the transmission capacity, has seen momentous progress in the past decade. Wideband, low-noise, and power-efficient optical amplifiers for the MDM systems are highly desired. We propose and theoretically demonstrate a broadband few-mode optical parametric amplifier (OPA) using a multimode thin-film periodically poled lithium niobate (PPLN) waveguide with multiple quasi-phase matching (QPM). The few-mode OPA relies on intramodal and intermodal difference-frequency generation (DFG) processes, obtaining strong parametric amplification of MDM signals using a single-frequency fundamental-mode pump. Differential modal gain (DMG) is optimized by adjusting the effective length of parametric interaction for each mode. In addition, parasitic DFG processes to cause nonlinear modal crosstalk is suppressed due to the large phase mismatch ensured by the multiple QPM design. The results show that amplification of signals carrying three modes with >11.2-dB gain per mode over 41 nm bandwidth covering extended C band can be achieved with a 600-mW pump, while the DMG is less than 1.1 dB and nonlinear modal crosstalk is negligible.

Design of a highly efficient 0.6-4.5GHz multioctave bandwidth GaN-HEMT power amplifier

A methodology for designing multioctave high-efficiency power amplifiers (PA) is presented in this letter. The sequential load-pull technique (SLPT) considering the second and third harmonic characteristics is proposed and combined with the multiple frequency matching theory (MFMT) to achieve outstanding performance in terms of operating bandwidth as well as efficiency. To verify the proposed theory, a 10 W GaN HEMT device is used. The proposed PA achieves a fractional bandwidth of 152% from 0.6 to 4.5GHz. In such a wide frequency band, the drain efficiency (DE) is measured as 50%-68% with an output power of greater than 38dBm and a large signal gain greater than 8dB.

DevTag: A Benchmark for Fingerprinting IoT Devices

Nowadays, various Internet of Things (IoT) devices, such as routers, webcams, and network printers, have been deployed across the Internet. For security and management purposes, it is important to accurately fingerprint IoT devices. In this work, we build a first benchmark called DevTag (IoT Device Tagging) for fingerprinting IoT devices. Specifically, DevTag supports retrieving packet-level features from IoT devices through two different data collections, passive monitoring, and active probing. For detecting IoT devices, DevTag integrates model-based and rule-based fingerprinting methods. For the model-based detection, we reimplemented five typical deep algorithms to infer IoT device classification models. For the rule-based detection, we generated nearly 41 117 rules in a unified format by analyzing several open-source tools. Furthermore, we conducted a systematic analysis to explore the advantages and limitations of those two methods for detecting IoT devices. Our analysis results reveal that the model-based detection has a significant advantage in distinguishing coarse-grained IoT devices (e.g., device type and vendor), while it is not suitable to detect product information as the label amount is massive. The rule-based detection is capable of extracting fine-grained device information with high precision in a short time. However, rules also suffer several inherent problems, such as multiple matching, conflicting, and overlapping issues. Finally, we implemented and distributed a prototype of DevTag working as the first benchmark for detecting IoT devices in the network community.

An INS/Geomagnetic Integrated Navigation Method for Coarse Estimation of Positioning Error and Search Area Adaption Applied to High-Speed Aircraft

The geomagnet aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft navigation. It can overcome the shortcomings of pure inertial navigation error accumulation over time and achieve long-term, autonomous, and all-weather navigation. The traditional iterative closest contour point (ICCP) algorithm, however, requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this article proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the aircraft flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this article can reach within 200 m.

Real-Time Dynamic Map With Crowdsourcing Vehicles in Edge Computing

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> to detect, match, and track objects on the road. We design the data plane of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> significantly outperforms existing solutions in terms of latency, coverage, and accuracy.

Complete sequences of six major histocompatibility complex haplotypes, including all the major MHC class II structures.

Accurate and comprehensive immunogenetic reference panels are key to the successful implementation of population-scale immunogenomics. The 5Mbp Major Histocompatibility Complex (MHC) is the most polymorphic region of the human genome and associated with multiple immune-mediated diseases, transplant matching and therapy responses. Analysis of MHC genetic variation is severely complicated by complex patterns of sequence variation, linkage disequilibrium and a lack of fully resolved MHC reference haplotypes, increasing the risk of spurious findings on analyzing this medically important region. Integrating Illumina, ultra-long Nanopore, and PacBio HiFi sequencing as well as bespoke bioinformatics, we completed five of the alternative MHC reference haplotypes of the current (GRCh38/hg38) build of the human reference genome and added one other. The six assembled MHC haplotypes encompass the DR1 and DR4 haplotype structures in addition to the previously completed DR2 and DR3, as well as six distinct classes of the structurally variable C4 region. Analysis of the assembled haplotypes showed that MHC class II sequence structures, including repeat element positions, are generally conserved within the DR haplotype supergroups, and that sequence diversity peaks in three regions around HLA-A, HLA-B+C, and the HLA class II genes. Demonstrating the potential for improved short-read analysis, the number of proper read pairs recruited to the MHC was found to be increased by 0.06%-0.49% in a 1000 Genomes Project read remapping experiment with seven diverse samples. Furthermore, the assembled haplotypes can serve as references for the community and provide the basis of a structurally accurate genotyping graph of the complete MHC region.

On the Multiple Pattern String Matching in DNA Databases

On the Multiple Pattern String Matching in DNA Databases

Rapid productivity prediction method for frac hits affected wells based on gas reservoir numerical simulation and probability method

Abstract As an important unconventional resource, shale gas can alleviate energy shortage, and its efficient development ensures the long-term growth of oil and gas. The prediction of production levels and estimated ultimate recovery with high accuracy is necessary for shale gas development. Conventional methods are widely applied in the oil and gas industry owing to their simplicity and effectiveness; however, none of them can accurately predict the results for frac hits affected wells. In this work, a probability method based on the numerical model of shale gas reservoir has been formed. In view of the impact of frac hits on the productivity of production wells during the development of shale gas reservoirs, an embedded discrete fractured numerical simulation method for gas reservoirs is proposed to simulate the geological engineering parameter range of wells before frac. And aiming at the established numerical model of shale gas reservoir, this method adopts the ensemble smoother with multiple data assimilation automatic history matching technology to carry out the history matching process of the model. Based on the probability theory and numerical simulation results, this study analyses the influence of different distribution functions of parameters on the calculation results of reserves, and obtains the expected curve of reserves through combination calculation. Besides, the effectiveness of this method was verified by comparing with other traditional predicted method.

Engineering faster double‐array Aho–Corasick automata

AbstractMultiple pattern matching in strings is a fundamental problem in text processing applications such as regular expressions or tokenization. This article studies efficient implementations of double‐array Aho–Corasick automata (DAACs), data structures for quickly performing the multiple pattern matching. The practical performance of DAACs is improved by carefully designing the data structure, and many implementation techniques have been proposed thus far. A problem in DAACs is that comprehensive descriptions and experimental analyses on their ideas are not provided. Engineers face difficulties in implementing an efficient DAAC. In this article, we review implementation techniques for DAACs and provide a comprehensive description of them. We also propose several new techniques for further improvement. We conduct exhaustive experiments through real‐world datasets and reveal the best combination of techniques to achieve a higher performance in DAACs. The best combination is different from those used in the most popular libraries of DAACs, which demonstrates that their performance can be further enhanced. On the basis of our experimental analysis, we developed a new Rust library for fast multiple pattern matching using DAACs, named Daachorse, as open‐source software at https://github.com/daac‐tools/daachorse. Experiments demonstrate that Daachorse outperforms other AC‐automaton implementations, indicating its suitability as a fast alternative for multiple pattern matching in many applications.

Study of Google Form-Based Assessment for Learning at UPT SD Negeri Binangun 01, Blitar Regency

Assessment for learning is an integral part of the learning process. One part of the assessment for learning is formative assessment. In the 21st century learning era, the implementation of formative assessments is often implemented on a digital basis. Google form is a platform that often used in the implementation of formative assessments. This study aims to examine the google form-based assessment for learning in class III to VI UPT SD Negeri Binangun 01 Blitar Regency. The research subjects consisted of 9 teachers and 173 students. The method used in this research is a qualitative descriptive research method. Research data obtained by data collection techniques from the results of questionnaires, observations, interviews, and documentation. Based on the analysis of the research data it was concluded that the use of google forms can make it easier for teachers to apply formative assessments in the form of objective questions, namely multiple choice, matching, true/false, and attitude scales (yess/no). The easeness experienced by the teacher can be seen in the activities making questions, making answer keys, determining scores, accessing answers and accessing student scores collectively on objective questions. Teachers encounter obstacles in making answer keys, determining scores, and accessing scores on questions in the form of short answers and essays. According to students, making formative assessments through google forms is more practical, attractive, safe, economical (paper and cost), in accordance with digital developments, and can be deleted easily. The weaknesses according to students and teachers are in the form of internet network constraints and electricity networks

Comparisons of Various Imputation Methods for Incomplete Water Quality Data: A Case Study of The Langat River, Malaysia

In this study, the ability of numerous statistical and machine learning models to impute water quality data was investigated at three monitoring stations along the Langat River in Malaysia. Inconsistencies in the percentage of missing data between monitoring stations (varying from 20 percent (moderate) to over 50 percent (high)) represent the greatest obstacle of the study. The main objective was to select the best method for imputation and compare whether there are differences between the methods used by the different stations. The paper focuses on different imputation methods such as Multiple Predictive Mean Matching (PMM), Multiple Random Forest Imputation (RF), Multiple Bayesian Linear Regression Imputation (BLR), Multiple Linear Regression (non-Bayesian) Imputation (LRNB), Multiple Classification and Regression Tree (CART), k-nearest neighbours (kNN) and Bootstrap-based Expectation Maximisation (EMB). Remarkably, among all seven imputation techniques, the kNN produces identically reliable results. The imputed data is all rated as ‘very good’ (NSE &gt; 0.75). This was confirmed by the calculation of |PBIAS|&lt;5.30 (all imputed data are‘very good’) and KGE≥0.87 (all imputations are rated as’ good’). Imputation performance improves for all three monitoring stations with an index of agreement, WI ≥ 0.94, despite varying percentages of missing data. According to the findings, the kNN imputation approach outperforms the others and should be prioritised in actual use. Future research with the existing methods could benefit from the addition of geographical data.

Propensity score matching after multiple imputation when a confounder has missing data.

One of the main challenges when using observational data for causal inference is the presence of confounding. A classic approach to account for confounding is the use of propensity score techniques that provide consistent estimators of the causal treatment effect under four common identifiability assumptions for causal effects, including that of no unmeasured confounding. Propensity score matching is a very popular approach which, in its simplest form, involves matching each treated patient to an untreated patient with a similar estimated propensity score, that is, probability of receiving the treatment. The treatment effect can then be estimated by comparing treated and untreated patients within the matched dataset. When missing data arises, a popular approach is to apply multiple imputation to handle the missingness. The combination of propensity score matching and multiple imputation is increasingly applied in practice. However, in this article we demonstrate that combining multiple imputation and propensity score matching can lead to over-coverage of the confidence interval for the treatment effect estimate. We explore the cause of this over-coverage and we evaluate, in this context, the performance of a correction to Rubin's rules for multiple imputation proposed by finding that this correction removes the over-coverage.

The effect of equalization of public health services on the health China's migrant population: Evidence from 2018 China Migrants Dynamic Survey.

China has implemented an equalization of public health Services policy for migrants in 40 pilot cities since 2013. The main objective of this study is to explore the effect of this migrant-based reform policy on the health status of the migrant population in China. Using the China Migrants Dynamic Survey (CMDS), we included 152,000 migrants aged 15 years or over in 2018. Standardized questionnaires were used to collect socio-economic information and self-reported health status. The Associations between the equalization of public health services and health status were estimated using Multiple regression estimation models and Propensity Score Matching (PSM) methods. Public health equalization reform in China has a significant and positive effect on the health status of the migrant population (β = 0.033, p < 0.001). Compared to males, higher income, under 60 years of age, inter-provincial mobility, and migrants those already living in urban areas, the equalization of public health Services had shown more significant positive effects on the groups who were inter-provincial migration (β = 0.055, p < 0.001), females (β = 0.055, p < 0.001), having low-income (β = 0.077, p < 0.001), aged over 60 years old (β = 0.191, p < 0.001), and living in rural areas (β = 0.038, p < 0.001). And multiple robustness tests prove that the above results are reliable. Our findings confirmed the positive health effect of the equalization of public health services reform on china's migrant population, especially among vulnerable groups such as those in low income groups, in rural areas and females. And we recommend that it is necessary to further promote the practices and experiences of the pilot cities. First, strengthen health education for the mobile population and improve their health literacy. Second, further increase the financial investment to improve the coverage of public health services and the equity in resource allocation among regions. Last, strengthen the information-based management of the migrant population and prevent and control infectious diseases.

Local Scanned Point-Cloud Registration of Aeroengine Pipeline System Based on Axial Constraints

The final assembled state is one of the crucial factors in the quality of an aeroengine pipeline system. It directly affects the safety, reliability, and thrust-to-weight ratio. Because of the limitations of aeroengine assembly and inspection technologies, ensuring the consistent assembly of pipeline systems in aeroengine production has become a challenging and key issue. We inspect the assembly of an aeroengine pipeline system with high precision and reliability using a single-frame point cloud that is acquired with a structured-light 3D scanner. To achieve this, we propose a novel registration framework that considers the structural constraints of the aeroengine pipeline system to achieve an accurate point-cloud alignment between the single-frame point-cloud data and the standard model. First, pipe axes are extracted from the single-frame point-cloud and standard models as the constraints for the coarse registration. Subsequently, a new point-cloud descriptor for curved segment features is designed to implement the rapid retrieval of structural features from both the single-frame data and relative models automatically. Finally, we use a voting mechanism to determine the best result when there are multiple matches. This allows us to achieve accurate and efficient registration with a non-uniform assembly structure. Experiments on both actual and synthetic datasets illustrate that the proposed registration method achieves higher accuracy and efficiency. Compared with the state-of-the-art algorithm, our method reduces the RMSE value by approximately 9 times during the quasi-real-time single-view raw point cloud assembly process of the aeroengine pipeline system.

Low-Cost Interventions to Improve Cervical Cancer Screening: An Integrative Review.

Cervical cancer (CC) is a major public health problem in low- and middle-income countries. Although screening can reduce CC incidence, screening programs are difficult to implement in resource-limited countries, making innovative interventions necessary. PubMed®, MEDLINE®, CINAHL®, LILACS, and SciELO databases were searched for studies published within the past five years that explored interventions to improve CC screening. Of the 486 articles identified, 35 were included in the review. The evidence was summarized, analyzed, and organized by theme. Several low-cost interventions improved aspects of CC screening, most of which were associated with a significant increase in adherence and uptake. Other interventions led to better baseline knowledge and involvement among patients and healthcare providers and a higher proportion of patients receiving treatment. Screening programs can use single or multiple approaches and match them to the local conditions and available resources. By understanding the various interventions that can mitigate CC incidence, healthcare providers can select the best approach to reach women eligible for CC screening.

Impact of Data Resolution on Tracking Southern Ocean Cyclones

Abstract The ERA5 new generation of high-resolution reanalysis provides a possibility to obtain more accurate cyclone tracks in the Southern Ocean. With a commonly used cyclone tracking algorithm, this study evaluates the impact of data resolution on Southern Ocean cyclone tracks for the period from 1980 to 2020 by preprocessing the ERA5 dataset at different spatial and temporal resolutions. A new track-matching method is proposed to assure an accurate comparison of different track datasets, considering the multiple match pairs and best match pair for each track. It is found that the number, distribution, and characteristics of cyclones are considerably different for various resolution scenarios. The higher spatial resolution captures more tracks, while the increased temporal resolution decreases the number, as well as the lifetime and the moving distance of tracks. The shared cyclones of different track datasets show different characteristics, influenced by both spatial and temporal resolutions. Higher spatial resolution schemes tend to identify more additional track points after the overlapping time of shared tracks rather than before. The spatial distribution pattern of additional track points is consistent when increasing temporal or spatial resolution separately. These results are a reference for the application of objective tracking algorithms in the Southern Ocean using input data with higher resolution. Significance Statement Automatic tracking algorithms are important tools for the research of cyclones and their associated weather phenomena. High-resolution input data are available now for cyclone tracking but it also brings more noise that may affect the results. The problem can be solved by a preprocess (smoothing) process using suitable spatial and temporal resolutions. This study compared the features of Southern Ocean cyclones obtained by different spatial and temporal resolution schemes. We further study the shared cyclones in different track datasets with a new track-matching method and discuss the different impacts of spatial and temporal resolution, as well as provide a reference for the application and improvement of cyclone tracking in the future.