Set Of Matches Research Articles

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

RNA-sequencing predicts a role of androgen receptor and aldehyde dehydrogenase 1A1 in osteosarcoma lung metastases

One-third of pediatric patients with osteosarcoma (OS) develop lung metastases (LM), which is the primary predictor of mortality. While current treatments of patients with localized bone disease have been successful in producing 5-year survival rates of 65–70%, patients with LM experience poor survival rates of only 19–30%. Unacceptably, this situation that has remained unchanged for 30 years. Thus, there is an urgent need to elucidate the mechanisms of metastatic spread in OS and to identify targetable molecular pathways that enable more effective treatments for patients with LM. We aimed to identify OS-specific gene alterations using RNA-sequencing of extremity and LM human tissues. Samples of extremity and LM tumors, including 4 matched sets, were obtained from patients with OS. Our data demonstrate aberrant regulation of the androgen receptor (AR) pathway in LM and predicts aldehyde dehydrogenase 1A1 (ALDH1A1) as a downstream target. Identification of AR pathway upregulation in human LM tissue samples may provide a target for novel therapeutics for patients with LM resistant to conventional chemotherapy.

Prevalence of somatic diseases in adults with attention deficit hyperactivity disorder in Japan is highest in people aged ≥40 years with mental disorders: a cross-sectional study of a Japanese health insurance claims database.

Studies have reported an association between attention deficit hyperactivity disorder (ADHD) and somatic diseases; however, the correlation of mental disorders with the association between ADHD and somatic diseases remains uninvestigated. This study investigated and compared the prevalence of somatic diseases among adults with/without ADHD, stratified by the presence or absence of mental disorders. This cross-sectional study (October 2020-September 2021), using data (June 2013-September 2021) from a Japanese health insurance claims database, included adult participants with a medical record of and receiving medication for ADHD (ADHD group); the control group (matched 1:5 by age/sex) comprised participants without ADHD. The prevalence and odds ratio (OR; ADHD versus control) of type 2 diabetes mellitus (T2DM), diabetes complications, hypertension, cardiovascular disease (CVD), dyslipidemia, gout and hyperuricemia, chronic obstructive pulmonary disease (COPD), non-alcoholic fatty liver disease/non-alcoholic steatohepatitis (NAFLD/NASH), and atopic dermatitis were investigated. Pooled ORs for stratified analysis were calculated using the Mantel-Haenszel method. In the matched analysis sets, the ORs for all somatic diseases were significantly higher for the ADHD group (n=15,028) versus the control group (n=74,796). On stratified analysis, the Mantel-Haenszel ORs were significant for NAFLD/NASH (1.53; 95% confidence interval [CI]: 1.34, 1.73), diabetes complications (1.39; 95% CI: 1.09, 1.77), and gout and hyperuricemia (1.34; 95% CI: 1.19, 1.51). Furthermore, the stratum-specific ORs for T2DM, hypertension, and dyslipidemia were >1 and <1 in the presence and absence of mental disorders, respectively. The prevalence of all somatic diseases except atopic dermatitis increased with age. For participants aged ≥40 years, the Mantel-Haenszel ORs were significant for all somatic diseases except CVD, COPD, and atopic dermatitis. The prevalence of several somatic diseases, including chronic disorders, was high among adults with ADHD, particularly in those aged ≥40years and those with mental disorders.

Strategyproof and fair matching mechanism for union of symmetric m-convex constraints.

We identify a new class of distributional constraints defined as a union of symmetric M-convex sets, which can represent a wide range of real-life constraints in two-sided matching settings. Since M-convexity is not closed under union, a union of symmetric M-convex sets does not belong to this well-behaved class of constraints. Consequently, devising a fair and strategyproof mechanism to handle this new class is challenging. We present a novel mechanism for it called Quota Reduction Deferred Acceptance (QRDA), which repeatedly applies the standard Deferred Acceptance mechanism by sequentially reducing artificially introduced maximum quotas. We show that QRDA is fair and strategyproof when handling a union of symmetric M-convex sets, which extends previous results obtained for a subclass of the union of symmetric M-convex sets: ratio constraints. QRDA always yields a weakly better matching for students than a baseline mechanism called Artificial Cap Deferred Acceptance (ACDA). We also experimentally show that QRDA outperforms ACDA in terms of nonwastefulness.

Automated football match reports as models of textuality

Abstract This paper deals with automated football match reports as a common genre of automated journalism. Based on a corpus of automated and human-written reports (n = 1,302) on the same set of matches and with reference to linguistic concepts of text and textuality, the textual properties of these texts are analyzed both quantitatively and qualitatively. The analysis is based on the idea that the task of text generation can be described as the task of automatically selecting cues of textuality such as connectives or signals of thematic relatedness. The results show that automated and human-written texts differ significantly in the use of these cues, particularly in the use of linguistic means for creating evaluation and contrast, and thus allow to trace in detail, how these cues contribute to cohesion, coherence and narrative qualities. Different from computational linguistic approaches focused on optimizing text generation algorithms, this paper proposes to use automated texts, which are to some extent imperfect, as models of textuality that through their imperfection can say something about the nature of texts in general. The paper thus contributes to the field of (mostly communication studies) research on automated journalism in which the texts themselves are rarely investigated.

Feature matching based on Gaussian kernel convolution and minimum relative motion

Feature matching is a necessary and important step for remote sensing image registration, intended to establish reliable point correspondences between two sets of features. In this paper, we propose a feature registration model based on local relative motion, which combines Gaussian kernel convolution with relative motion (GRM) vector to obtain better results by removing wrong matches and improving the inlier point accuracy. We first establish putative matching based on the similarity between local descriptors. Then, the preliminary hypothetical matching point set is filtered using consistency with nearest neighbors among the inlier points to obtain a more accurate motion vector, and to fit the real motion vector through the Gaussian convolution kernel. Finally, we find the displacement between the fitted motion vector and the matching generated motion vector. And combine the displacement with the optimization model to find the inlier point set. Experimental results show that our GRM method outperforms related work, achieving better matching results.

Study on seismic behavior and collapse risk of super high-rise braced mega frame-core tube structural system

The braced mega frame-core tube (BMFCT) structure could provide an efficient lateral-force resisting (LFR) system for high-rise or super high-rise buildings, especially for those higher than 500 m. The BMFCT structure is a dual LFR system, including the outer braced mega frame and the inner core tube. The seismic behavior and design method of this structural system is not clear. In this study, a parametric study based on collapse risk-targeted analysis is conducted to clarify seismic mechanism and performance and to make clear means of matching reasonable stiffness and setting energy-dissipation components. Five comparable BMFCT structures with different mega brace stiffness proportions and frame-tube stiffness ratios are designed. An efficient elasto-plastic modeling method is introduced to establish the accurate FE models. The corresponding collapse risks are calculated through Incremental Dynamic Analysis and fragility theory. The results show that the structural collapse risk increases with the increased frame-tube stiffness ratio. Therefore，the stiffness and bearing capacity of peripheral frames should be strengthened to ensure the structural collapse margin. On the other hand, the collapse resistance capacity gradually decreases with the increasing of the mega brace stiffness. Thus, the stiffness proportion of mega braces should be recommended to meet the basic lateral stiffness requirements, which shall not exceed 35 % of the overall structural stiffness. Moreover, the distribution feature and ideal arrangement of the energy dissipation components are proposed. The seismic design recommendation based on the uniform collapse risk is also proposed for the BMFCT system, which serves a reference for the structural design of super high-rise buildings.

MATCHING FILTER-BASED VSLAM OPTIMIZATION IN INDOOR ENVIRONMENTS

Abstract. An important factor that reduced the accuracy of motion trajectories in existing VSLAM (Visual Simultaneous Localization and Mapping) systems is the poor estimation of the position pose of the vision odometer. The existing methods generate many incorrect matches during the feature matching process, resulting in low computational accuracy of rotations and translations between cameras, which further leads to a reduction in the robustness of the overall system. In addition, the sparse feature point maps do not provide a detailed description of the surrounding environment, which makes it difficult for the devices equipped with VSLAM systems to perform advanced tasks such as navigation, path planning and human-computer interaction. To address the accuracy problem, we select the set of matches from existing feature matching algorithms based on the motion consistency constraint and use a random sampling consistency algorithm to obtain the best quality matches from the selected samples for computing the geometric transformation model and estimating the current pose. To address the problem of sparse map points, we use the depth information from the RGB-D or Stereo camera to build a dense map module to ensure that information about the surrounding environment is recorded as a point cloud, which provides data support for the implementation of advanced tasks of the device.

Association Between HSV‐1 and Risk of Alzheimer’s Disease Detected by a Propensity‐Score Matched Case Design

AbstractBackgroundHerpes simplex virus 1 (HSV‐1) is a neurotropic and neuroinvasive species that can enter the brain triggering inflammation and an immune response, increasing the risk of Alzheimer disease (AD). Previously, we found a significant association between HSV‐1 and AD in whole‐exome sequencing (WES) data (OR = 3.69, P = 6.71×10−4) but not whole‐genome sequenced (WGS) data (OR = 0.92, P = 0.27) from the AD Sequencing Project (ADSP).MethodTo understand this discrepancy, and to better control for the numerous covariates and technical differences that influenced viral quantity, we created a novel algorithm using propensity score analysis (PSA) which permits analysis of observational study data comparable to a randomized controlled trial. WGS reads from 6,541 AD cases and 6,855 controls from the ADSP (including 7,395 non‐Hispanic Whites, 2,961 African Americans, and 3,040 Caribbean Hispanics) that did not align to the human genome were mapped to viral reference sequences and quantified. Propensity scores were derived for 7,619 individuals with at least one HSV‐1 read and 5,777 individuals lacking any HSV‐1 reads using a binary treatment indicator for HSV‐1 status. We applied forward stepwise regression including known confounders (APOE ε4 status, sex, age, tissue source (blood or brain), population ancestry, sequencing center, and PCR amplification) to identify other potential confounders and/or interactions and quadratic terms for creating propensity scores. We tried several matching algorithms to generate matched sets of individuals with and without HSV‐1. Utilizing the best quality matched set, we evaluated the association of AD with HSV‐1 status using a weighted generalized linear model.ResultThe final regression model included terms for the covariates mentioned above that were available for most of the original sample (5828 AD cases and 6487 controls). We found a significant association between HSV‐1 and AD (OR = 1.10, P = 0.02).ConclusionResults from WGS data confirm our previous findings obtained from WES data and support the hypothesis that HSV‐1 is associated with an increased AD risk, highlighting the utility of PSA for analyzing association of risk factors with complex disorders like AD. Further studies are needed to replicate this finding in other ancestral populations and explain the lack of association in WGS data.

A simple recommendation for the analysis of matching data.

The matching paradigm can take a number of forms and has been used in many areas of psychology. When participants are asked to match or order sets of objects, researchers must correctly account for the number of matches expected purely by chance. Not accounting for the expected chance matches can lead to incorrectly drawing conclusions based on one's data. This study demonstrated that the z test can be an appropriate and easy test to use in the analysis of matching data from studies that require pairs of objects to be matched with each other. This article proves that in a matching paradigm the expected number of chance matches is 1.0 and the associated variance is also 1.0. The z test is shown to maintain the Type I error close to the nominal significance level when the null hypothesis is true and the sample size is at least 80 or 110. To attain power of .80, a sample size larger than 80 may be needed depending upon the effect size associated with the area of interest and the hypothesized alternative probability distribution. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

RCRUX: A Rapid and Versatile Tool for Generating Metabarcoding Reference libraries in R.

The sequencing revolution requires accurate taxonomic classification of DNA sequences. Key to making accurate taxonomic assignments are curated, comprehensive reference barcode databases. However, the generation and curation of such databases has remained challenging given the large and continuously growing volumes of both DNA sequence data and novel reference barcode targets. Monitoring and research applications require a greater diversity of specialized gene regions and targeted taxa then are currently curated by professional staff. Thus there is a growing need for an easy to implement computational tool that can generate comprehensive metabarcoding reference libraries for any bespoke locus. We address this need by reimagining CRUX from the Anacapa Toolkit and present the rCRUX package in R which, like it's predecessor, relies on sequence homology and PCR primer compatibility instead of keyword-searches to avoid limitations of user-defined metadata. The typical workflow involves searching for plausible seed amplicons (get_seeds_local() or get_seeds_remote()) by simulating in silico PCR to acquire a set of sequences analogous to PCR products containing a user-defined set of primer sequences. Next, these seeds are used to iteratively blast search seed sequences against a local copy of the National Center for Biotechnology Information (NCBI) formatted nt database using a taxonomic-rank based stratified random sampling approach ( blast_seeds() ). This results in a comprehensive set of sequence matches. This database is dereplicated and cleaned (derep_and_clean_db()) by identifying identical reference sequences and collapsing the taxonomic path to the lowest taxonomic agreement across all matching reads. This results in a curated, comprehensive database of primer-specific reference barcode sequences from NCBI. Databases can then be compared (compare_db()) to determine read and taxonomic overlap. We demonstrate that rCRUX provides more comprehensive reference databases for the MiFish Universal Teleost 12S, Taberlet trnl, fungal ITS, and Leray CO1 loci than CRABS, MetaCurator, RESCRIPt, and ecoPCR reference databases. We then further demonstrate the utility of rCRUX by generating 24 reference databases for 20 metabarcoding loci, many of which lack dedicated reference database curation efforts. The rCRUX package provides a simple to use tool for the generation of curated, comprehensive reference databases for user-defined loci, facilitating accurate and effective taxonomic classification of metabarcoding and DNA sequence efforts broadly.

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

Building facades has always been a challenge for feature matching in oblique photogrammetry due to weak textures, non-Lambertian objects, severe occlusion, and distortion. Plumb lines are essential building geometry structural feature lines in building facades, which show strong spatial relevance to these problems. Achieving plumb line matching has great application potential for optimizing the process and products of oblique photogrammetry. Thus, we proposed a novel matching algorithm for plumb lines based on spatial and color hybrid constraints according to its central projection imaging characteristics. Firstly, based on vanishing point theory, the plumb lines from photos were back-calculated to determine the matching target set; secondly, the property of its large elevation ranges was exploited to calculate the homonymous points as spatial constraints by projecting plumb lines onto the stratified spatial planes; thirdly, the neighboring primary colors on both sides of the plumb lines were extracted as feature descriptors and compared by colorimetry; then, the greedy strategy was employed to successively filter out the locally optimal solutions satisfying the spatial and color hybrid constraints to complete the initial matching; finally, the intersection-over-union analysis of the solution plane and the verticalness evaluation of the matching results were implemented to eliminate errors. The results show that the proposed algorithm can achieve an average accuracy of 97.29% and 78.41% in the forward and lateral overlap experiments from multi-scenes, respectively, displaying a strong adaptability to poor texture, inconsistency, and distortion. In conclusion, thanks to the plumb-line-oriented matching strategy, this algorithm owns inherent advantages in theory and computational complexity. It is suitable for all building-oriented oblique photogrammetry tasks and is highly worthy of promotion and application.

Popular critical matchings in the many-to-many setting

We consider the many-to-many bipartite matching problem in the presence of two-sided preferences and two-sided lower quotas. The input to our problem is a bipartite graph G=(A∪B,E), where each vertex in A∪B specifies a strict preference ordering over its neighbours. Each vertex has an upper quota denoting the maximum number of vertices that can be assigned to it. In addition, each vertex has a lower quota denoting the minimum number of vertices that need to be assigned to it. In the many-to-many setting with two-sided lower quotas, informally, a critical matching is a matching which fulfils vertex lower quotas to the maximum possible extent. This is a natural generalization of the definition of critical matching in the one-to-one setting considered by Kavitha (FSTTCS 2021). In this work, our goal is to find a popular matching in the set of critical matchings. A matching is popular in a given set of matchings if it remains undefeated in a head-to-head election with any matching in that set. Here, vertices cast votes between pairs of matchings. We show that there always exists a matching that is popular in the set of critical matchings. We present an efficient algorithm to compute such a matching of the largest size. We prove the popularity of our matching using a dual certificate.

Toric Rings of Perfectly Matchable Subgraph Polytopes

The perfectly matchable subgraph polytope of a graph is a (0,1)-polytope associated with the vertex sets of matchings in the graph. In this paper, we study algebraic properties (compressedness, Gorensteinness) of the toric rings of perfectly matchable subgraph polytopes. In particular, we give a complete characterization of a graph whose perfectly matchable subgraph polytope is compressed.

Early Prognostic Biomarkers of Non-Relapse Mortality in Allogeneic Mismatched Unrelated Hematopoietic Cell Transplantation

Introduction A major challenge in allogeneic hematopoietic cell transplantation (HCT) has been advancing outcomes for individuals lacking a fully matched donor who require a mismatched unrelated donor (MMUD) HCT. Recently, the 15-MMUD study demonstrated encouraging results for post-transplant cyclophosphamide (PT-Cy)-based prophylaxis in MMUD recipients (Shaw, Jimenez, JCO 2021). Our retrospective analysis confirmed improvement in MMUD survival with PT-Cy compared to historical anti-thymocyte globulin (ATG)/calcineurin-based regimens (Jimenez, Blood Adv 2022). Elevations of plasma biomarkers such as Stimulation-2 (ST2) have demonstrated increased risk of non-relapse mortality (NRM) and GVHD, however these have not been validated in the MMUD setting. Here, we carried out a pilot study to determine if biomarker analysis of ST2, Chemokine ligand-9 (CXCL-9), and Regenerating islet-derived 3-α (REG3α) at Day 14 (D14) and 30 (D30) can identify patients at higher risk for NRM after MMUD HCT. Methods Patients with banked samples and underwent MMUD HCT with either ATG or PT-Cy as GVHD prophylaxis at the University of Miami between 2016 - 2020 were included. Enzyme-linked immunosorbent assays for ST2, CXCL-9, and REG3α were performed in batches on cryopreserved plasma. Descriptive statistics of demographic variables were compared using chi-squared, Fisher's exact, or Wilcoxon rank sum tests. ST2 values were treated as continuous variables, and CXCL-9 was dichotomized due to frequent values below the lower limit of detection. Cumulative incidences of NRM were analyzed with relapse as a competing risk. Time-dependent receiver operating curves (ROC) were constructed and Area Under the ROC Curve (AUC) calculated. Youden's index was used to select optimum thresholds for each biomarker. Results We included 47 patients (ATG: 23; PT-Cy: 24). The PT-Cy group had more bone marrow graft representation (50% vs 22%, p = 0.04) and more donors mismatched at &amp;gt;1 HLA-locus (29% vs 0%, p&amp;lt;0.01). Median follow up was 746 days (range 44-1968). Point estimate of overall survival at 2 years was 35% (95% CI 20-61) for ATG and 71% (95% CI 55-92) for PT-Cy (p=0.02). Median overall survival was estimated at 413 days among ATG patients and could not be estimated for PT-Cy patients. The cumulative incidence of NRM at 2-years was 30% (95% CI 13-51%) for ATG and 17% (95% CI 5-35%) for PT-Cy (p=0.03). Among all patients, ST2 was prognostic for NRM using time-varying covariates including combined D14 and D30 (D14+30) with a HR of 2.14 (95% CI 1.04-4.39, p = 0.04). The D14+30 ROC to predict NRM at 1-year using ST2 showed an AUC of 0.83 (95% CI 0.66-1). D+14 thresholded ST2 value maximizing Youden's index of ≥79.89 ng/mL was discriminative for 1-year NRM (p=0.002) (Fig. 1). Additionally, combination of CXCL-9 to ST2 could optimize the prognostic value when compared to ST2 alone: HR at D14+D30: 3.82 (95% CI 1.01-14.47, p=0.048) with an improved AUC of 0.9 (95% CI 0.79-1) (Fig. 2). REG3α did not significantly add to the AUC. Although limited by sample size, we explored the association by GVHD prophylaxis (ATG or PT-Cy). First, in the ATG group, the D14 ST2 value that maximized thresholded Youden's index of 61.39 ng/mL was predictive of 1-year NRM (p=0.04). Similarly, the D+30 ST2 values of 148.06 ng/mL was predictive of NRM (p&amp;lt;0.001). The AUC for prediction of 1-year NRM for Second, in the PT-Cy group, elevations of D30 ST2 were prognostic of 1-year NRM with HR 15.05 (95% CI 1.77-27.83, p=0.01). Consequently, the AUC for prediction of 1-year NRM for D30 ST2 was 0.92 (95% CI 0.77-1) and for ST2 + CXCL-9 was 0.96 (95% CI 0.86-1). Conclusions This pilot study suggests that after MMUD HCT, D14 and D30 biomarkers are prognostic for NRM across GVHD prophylaxis platforms. Importantly, we find that ST2 maintains its prognostic capacity in the increasingly popular PT-Cy group. This supplements what we previously reported in the haploidentical and matched unrelated donor PT-Cy setting (Kanakry, Haematologica 2017). Biomarker combination ST2 + CXCL-9 showed improved AUC over ST2 alone, and the composite D14+30 timepoint may have additive utility over a single timepoint. While the optimal time for biomarker collection is unknown, our data suggests D14 had value for ATG-treated patients, whereas D30 may be more important in the PT-Cy-treated group. A validation cohort is being assembled for PT-Cy based MMUD HCT.

Dynamic matching with transfers

I study a two-sided market of dynamic matching with sequential arrivals. Agents trade off waiting to match with a more desirable agent with immediately matching with a less desirable agent. I study the consequences of transfer availability for two protocols: first-in-first-out (FIFO) and last-in-first-out (LIFO). For FIFO, welfare is weakly greater with transfers than without. For LIFO, there is a range of waiting costs such that welfare is weakly lower with transfers than without. For sufficiently low waiting costs, the equilibrium welfare for FIFO with transfers remains strictly lower than socially optimal. Thus, unlike in a static matching setting, transfers in my dynamic matching setting do not generate utilitarian efficient outcomes.

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks.

Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in generating robust feature matches for images with repeated textures, low textures, low sharpness, and low contrast. Moreover, the use of narrower optics in these cameras adds to the challenges during the feature-matching stage, particularly for images captured during low-altitude flight missions. In order to enhance the robustness of feature detection and matching in low pixel resolution images, in this study we propose a novel approach utilizing 3D Convolution-based Siamese networks. Compared to state-of-the-art methods, this approach takes advantage of all the spectral information available in hyperspectral imaging in order to filter out incorrect matches and produce a robust set of matches. The proposed method initially generates feature matches through a combination of Phase Stretch Transformation-based edge detection and SIFT features. Subsequently, a 3D Convolution-based Siamese network is utilized to filter out inaccurate matches, producing a highly accurate set of feature matches. Evaluation of the proposed method demonstrates its superiority over state-of-the-art approaches in cases where they fail to produce feature matches. Additionally, it competes effectively with the other evaluated methods when generating feature matches in low-pixel resolution hyperspectral images. This research contributes to the advancement of low pixel resolution hyperspectral imaging techniques, and we believe it can specifically aid in mosaic generation of low pixel resolution hyperspectral images.

Effectiveness of 13-valent pneumococcal conjugate vaccine on all-cause pneumonia in children under 5 years in Shanghai, China: An observational study

BackgroundStreptococcus pneumoniae (Spn) is a common respiratory pathogen and the main cause of bacterial pneumonia, meningitis, and bacteremia, acute otitis media. Imported 13-valent pneumococcal conjugate vaccine (PCV13) was licensed in China and introduced in Shanghai in 2017. We aim to describe PCV13 vaccination trends and pneumonia incidence of children under 5 from 2017 to 2020, then estimate the effectiveness of PCV13 against community-acquired pneumonia (CAP) in children under 5 in Shanghai, China. MethodsBy calculating propensity scores with logistic regression, a comparison group was formed by frequency matching one unvaccinated child to one vaccinated child. For matching, we used the nearest-neighbor matching algorithm and exact matching, and then created distinct matched analysis sets for two cohorts. A Kaplan-Meier analysis was conducted to measure the cumulative incidence of all-cause pneumonia in both groups and used the log-rank test to assess the differences between the two cumulative incidence curves. Cox proportional hazards regression was used to compare the adjusted hazard ratios (HR) of differences in all-cause pneumonia between the two groups. ResultsChildren received three or more doses PCV13 accounted for 85.7% of all vaccinated children. The incidence of pneumonia in Shanghai’s Songjiang district decreased rapidly from 2017, when PCV13 vaccination presented an overall increasing trend. The estimated vaccine effectiveness against visits for all-cause pneumonia was 19% (95% CI: 3 to 32) after the first dose in children vaccinated with at least one dose of PCV13. The protective effectiveness of PCV13 was found to be higher for hospitalized pneumonia (30%, 95% CI: 5% to 49%) than for outpatient pneumonia (19%, 95% CI: 4% to 32%). ConclusionsPCV13 vaccination among children aged 0–5 years substantially reduced the incidence of all-cause pneumonia. Direct immunization of children under 5 years is an effective strategy to combat outpatient pneumonia, and hospitalized pneumonia.

Improving sparse graph attention for feature matching by informative keypoints exploration

Feature matching refers to building correct point correspondences from two-view images. Typical methods create coarse matches merely based on the similarity of local descriptors, then additionally use geometrical constraints or coherence to get a clean match set. However, this indirect paradigm would be strictly limited by the pre-matching results. This can be alleviated by a GNN-based matching network that constructs correct correspondences directly from feature positions and descriptions. Attentional pooling is utilized to reduce the high computational complexity and memory usage by sparsing the graph attention. In this paper, we propose an enhanced sparse GNN, namely KeyGNN, for fast feature matching, by designing Guided Attentional Pooling to emphasize the informative cues in GNN layers. Specifically, we propose Informative Keypoints Exploration Module and Guided Sparse Module to respectively extract information-rich points and use them to guide the attentional pooling for better information preservation. This process is constrained by the proposed Structural Importance Ranking Loss and Descriptor Distribution Loss, that ensure the structural information can be correctly extracted and the generated feature vectors be well distributed in the deep space. Unlike existing techniques, our method can focus and preserve more on the main information in the sparse GNN stage, thus avoiding information loss and enhancing the accuracy. Extensive experiments on large and public datasets reveal that our method can obtain better performance than the state-of-the-arts in the tasks of camera pose estimation, fundamental matrix estimation, and visual localization. The runtime and memory test shows that our network takes low computational and memory usage, which is friendly to real-time vision tasks.

On the Set of Stable Matchings in a Bipartite Graph

On the Set of Stable Matchings in a Bipartite Graph