Set Of Matches Research Articles

Task allocation and on-the-job training

We study dynamic task allocation when providers' expertise evolves endogenously through training. We characterize optimal assignment protocols and compare them to discretionary procedures, where it is the clients who select their service providers. Our results indicate that welfare gains from centralization are greater when tasks arrive more rapidly, and when training technologies improve. Monitoring seniors' backlog of clients always increases welfare but may decrease training. Methodologically, we explore a matching setting with endogenous types, and illustrate useful adaptations of queueing theory techniques for such environments.

Linking Early Detection/Treatment of Parkinson’s Disease using Deep Learning Techniques

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and difficulty with balance and coordination. It is highly important for early detection of Parkinson’s Disease for patients to receive proper treatment. This paper aims to present a preliminary data mining procedure that help Parkinson’s Disease patients slow down their progression of the disease while helping early detection of the disease. For early non-invasive treatment, our research first analyses the early symptoms of Parkinson’s Disease, designs/selects a proper demo video, let the user follow the demo to exercise and upload his exercise video to our deep learning APP: LaBelle. LaBelle utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both demo and the user, calculates the angles created between different joints and major body parts. LaBelle’s AI model uses a K-means clustering algorithm to create a group of clusters for both demo and the user dataset. Using the two sets of clusters, LaBelle identifies the key frames in the user video and searches the demo cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. Meanwhile, if the user is willing to donate their exercise data, he can simply input his age, whether he is a PD patient (maybe for how long) anonymously. Then his data can be stored into our customized dataset, used in data mining for Parkinson’s Disease prediction, which involves building/training our deep learning CNN model and help early detection of Parkinson’s Disease.

LoRa-Based Smart Waste Bins Placement using Clustering Method in Rural Areas of Indonesia

Abstract ATP Tennis stands for the “The Association of Tennis Professionals” which is the primary governing body for male tennis players. ATP was formed in Sep 1972 for professional tennis players. A study has been done on tennis players’ datasets to implement supervised machine learning techniques to illustrate match data and make predictions. An appropriate dataset has been chosen, data cleaning has been implemented to extract anomalies, data is visualized via plotting methods in R language and supervised machine learning models applied. The main models applied are linear regression and decision tree. Results and predictions have been extracted from the applied models. In the linear regression model, the correlation is calculated to find the relation between dependent and independent variables, furthermore the results and prediction are extracted from the linear regression model. Also, three hypotheses are applied for multiple linear regression model. The decision tree modeled the best of 3 or best of 5 sets of matches and predicted which set of matches would be considered best. Keywords: Machine Learning, supervised learning, linear regression, decision tree, R language, Tennis, ATP.

Applying Machine Learning- Supervised Learning Techniques for Tennis Players Dataset Analysis

Abstract ATP Tennis stands for the “The Association of Tennis Professionals” which is the primary governing body for male tennis players. ATP was formed in Sep 1972 for professional tennis players. A study has been done on tennis players’ datasets to implement supervised machine learning techniques to illustrate match data and make predictions. An appropriate dataset has been chosen, data cleaning has been implemented to extract anomalies, data is visualized via plotting methods in R language and supervised machine learning models applied. The main models applied are linear regression and decision tree. Results and predictions have been extracted from the applied models. In the linear regression model, the correlation is calculated to find the relation between dependent and independent variables, furthermore the results and prediction are extracted from the linear regression model. Also, three hypotheses are applied for multiple linear regression model. The decision tree modeled the best of 3 or best of 5 sets of matches and predicted which set of matches would be considered best. Keywords: Machine Learning, supervised learning, linear regression, decision tree, R language, Tennis, ATP.

Study on Optimization Method of Visual Odometry Based on Feature Matching

The mismatching of image features affects the calculation of the fundamental matrix and then leads to poor estimation accuracy of SLAM visual odometry. Aiming at the above problems, a visual odometry optimization method based on feature matching is proposed. Firstly, the initial matching set is roughly filtered by the minimum distance threshold method, and then, the relative transformation relationship between images is calculated by the RANSAC algorithm. If it conforms to the transformation relationship, it is an interior point. The iteration result with most interior points is the correct matching result. Then, the homography transformation between images is calculated, and the fundamental matrix is calculated by it. The interior points are determined by epipolar geometric constraints, and the fundamental matrix with the most interior points is obtained. Finally, the effects of the visual odometry optimization algorithm are verified by the TUM data set from two aspects: feature matching and fundamental matrix calculation. The experimental results show that an improved feature matching algorithm can effectively remove mismatched feature points while improving the operation efficiency. At the same time, the accuracy of feature point matching is increased by 15.8%. The fundamental matrix estimation algorithm not only improves the calculation accuracy of the fundamental matrix but also increases the interior point rate by 11.9%. A theoretical basis for improving the accuracy estimation of visual odometry will be provided.

Clinical application of a template-guided automated planning routine.

Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application. A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan. The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″±4' 33″. Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.

Kasteleyn cokernels and perfect matchings on planar bipartite graphs

The determinant method of Kasteleyn gives a method of computing the number of perfect matchings of a planar bipartite graph. In addition, results of Bernardi exhibit a bijection between spanning trees of a planar bipartite graph and elements of its Jacobian. In this paper, we explore an adaptation of Bernardi’s results, providing a simply transitive group action of the Kasteleyn cokernel of a planar bipartite graph on its set of perfect matchings, when the planar bipartite graph in question is of the form $$G^+$$ , as defined by Kenyon, Propp and Wilson.

A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms.

Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets. We propose a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. Our 8-point approach covers data preprocessing, blocking, record adjudication, linkage evaluation, and reviewer characteristics. This framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. In turn, this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.

Effects of Cataract on Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Thickness on Swept Source Optical Coherence Tomography.

To evaluate the changes in peripapillary retinal nerve fiber layer (pRNFL) thickness and macular ganglion cell-inner plexiform layer (mGC-IPL) thickness measured by swept source optical coherence tomography (SS-OCT) following cataract surgery in patients with glaucoma. We included 42 glaucoma eyes and 42 case-matched normal eyes that underwent cataract surgery without complications. One matching set included one glaucoma eye and one case-matched normal eye. The age, sex, and cataract subtype scores were similar for each group. Before and within 3 months of surgery, we measured the pRNFL thickness and mGCIPL thickness by SS-OCT. Following cataract surgery, the image quality (IQ) of SS-OCT improved in both groups. The thickness of the pRNFL and mGC-IPL increased in the mean values and all areas, except for pRNFL from 1 to 4 o'clock in the glaucoma group and at 1 o'clock in the normal group. Posterior subcapsular cataract was related to the change in IQ following surgery. The glaucoma and normal group showed greater pRNFL thickness change due to lesser preoperative pRNFL thickness. Furthermore, the mGC-IPL thickness change was greater in the glaucoma group because of lesser preoperative mGC-IPL thickness. By contrast, the normal group demonstrated greater mGC-IPL thickness change due to higher cortical cataract scores. Cataracts caused the deterioration of the IQ in SS-OCT, thereby resulting in an undermeasurement of the pRNFL and mGC-IPL thickness. Preoperative pRNFL and mGC-IPL were negatively associated with postoperative pRNFL and mGCIPL thickness change in the glaucoma and normal groups. Therefore, ophthalmologists should particularly consider the effect of cataract while diagnosing glaucoma using SS-OCT.

Do Early Phase Oncology Trials Predict Clinical Efficacy in Subsequent Biomarker-Enriched Phase III Randomized Trials?

Promising early phase trial results of biomarker-targeted therapies have occasionally led to regulatory approval. We examined if early phase trials were predictive of efficacy in randomized controlled trials (RCTs) with matching treatment settings. Cancer drug RCTs conducted between January 2006 and March 2021 were identified through Clinicaltrials.gov. Biomarker-enriched RCTs and associated matching early phase trials were included. Trial pairs were compared using objective response rate (ORR) and progression-free survival (PFS). We examined whether early phase trials results were associated with RCT results using logistic regression. The search yielded 2157 unique RCTs and 27 RCTs pairing with early phase trials were included. Based on average difference of trial pairs, ORR was similar (1.6%; 95% confidence interval (CI) - 2.5 to 5.6, p = 0.50) and median PFS was higher in early phase trials (2.0months; 95% CI 0.9-3.0, p < 0.05). On an individual pair basis, there was large variability in difference for ORR (range - 23.9 to 20.2%) and median PFS (range - 0.8 to 7.4months). The probability of the RCT meeting its primary endpoint is 95% (95% prediction interval (PI) 72.8-99.3%) when the early phase trial ORR is 77.7%. Overall, in early phase trials, ORR has minimal bias and median PFS appears to be slightly overestimated. Substantial variability between trials suggests early phase trial results may be inconsistent with subsequent RCT. Early phase trial results may be associated with RCTs meeting their primary endpoint when ORR is very high; however, caution must be exercised when using early phase trials as representative of RCTs.

Stable Matchings with Restricted Preferences: Structure and Complexity

In the stable marriage (SM) problem, there are two sets of agents—traditionally referred to as men and women—and each agent has a preference list that ranks (a subset of) agents of the opposite sex. The goal is to find a matching between men and women that is stable in the sense that no man-woman pair mutually prefers each other to their assigned partners. In a seminal work, Gale and Shapley [ 16 ] showed that stable matchings always exist and described an efficient algorithm for finding one. Irving and Leather [ 24 ] defined the rotation poset of an SM instance and showed that it determines the structure of the set of stable matchings of the instance. They further showed that every finite poset can be realized as the rotation poset of some SM instance. Consequently, many problems—such as counting stable matchings and finding certain “fair” stable matchings—are computationally intractable (NP-hard) in general. In this article, we consider SM instances in which certain restrictions are placed on the preference lists. We show that three natural preference models— k -bounded, k -attribute, and (k 1 , k 2 ) -list—can realize arbitrary rotation posets for constant values of k . Hence, even in these highly restricted preference models, many stable matching problems remain intractable. In contrast, we show that for any fixed constant k , the rotation posets of k -range instances are highly restricted. As a consequence, we show that exactly counting and uniformly sampling stable matchings, finding median, sex-equal, and balanced stable matchings, are fixed-parameter tractable when parameterized by the range of the instance. Thus, these problems can be solved in polynomial time on instances of the k-range model for any fixed constant k.

Perception of generosity under matching and rebate subsidies

AbstractExisting evidence from laboratory experiments finds that a match is likely to increase charitable donations by more than a theoretically equivalent rebate. A number of explanations have been proposed for this in the literature. One idea, which has never been tested, is that people consider a match to be more generous, because unlike the rebate, there is no reward for making a donation in the match setting. We design a survey to determine whether people do consider matches more generous than rebates, and probe the reasons subjects give for their answers. We find that a significant number of people do consider rebates less generous because of the reward associated with donations in such a setting.

FECH Expression Correlates with the Prognosis and Tumor Immune Microenvironment in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is, by far, the most prevalent and fatal kind of kidney cancer. Ferrochelatase (FECH) is an enzyme that performs a significant function in the onset and progression of many distinct kinds of malignant tumors. Nevertheless, its predictive usefulness in renal clear cell carcinoma (RCC) has not yet been fully investigated. FECH expression in ccRCC and healthy adjoining tissues was primarily screened utilizing data sourced from The Cancer Genome Atlas (TCGA) and subsequently validated using data from an independent cohort derived from the Gene Expression Omnibus (GEO) and the Human Protein Atlas HPA databases. The relationship among FECH expression, clinicopathological parameters, and overall survival (OS) was assessed utilizing multivariate analysis and Kaplan-Meier survival curves. Additionally, the protein networks with FECH interaction were constructed with the aid of the online Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Gene ontology (GO) analysis, and gene set enrichment analysis (GSEA) were conducted based on TCGA data, and a single-sample GSEA was utilized to explore the link between FECH expression and the infiltration status of immune cells in the tumor. The Gene Expression Profiling Interactive Analysis (GEPIA) and TIMER databases were utilized to investigate the relationships of FECH expression with the infiltrating immune cells and the matching gene marker sets. FECH expression was shown to be substantially lowered in ccRCC tumors as opposed to that observed in normal tissues (p < 0.05). Lower levels of FECH expression were shown to have a strong association with higher grades of cancer and more advanced TNM stages. The findings of multivariate and univariate analyses illustrated that the OS in patients with ccRCC with low FECH expression is shorter in contrast with that in the high FECH expression group (p < 0.05). It was discovered that CPOX and frataxin are key proteins that interact with FECH. ccRCC with FECH deficiency was linked to the lack of infiltrating immune cells and their respective marker sets, which included CD4+ T cells. In ccRCC, decreased FECH expression was linked to disease progression, unfavorable prognosis, and impaired immune cell infiltration.

BMCSA:Multi-feature spatial convolution semantic matching model based on BERT

This paper proposes a multi-feature spatial convolutional semantic matching model (BMCSA) based on BERT by enriching different feature spatial information of semantic features. BMCSA employs the BERT model to extract the semantic features of the text, then uses the two-dimensional convolutional network to extract different feature spatial information, and finally combines the Attention mechanism to capture the global feature spatial information. We use two different semantic matching data sets and a text inference data set to verify the effectiveness of the proposed model. Experimental results prove that BMCSA is better than the baseline model.

Sustained Management of COVID-19: Doing More of What Works to Control Future Surges.

Sustained Management of COVID-19: Doing More of What Works to Control Future Surges.

Genetic protocols for DNA extraction from white-tailed deer cast antlers to confirm individuality

White-tailed deer (Odocoileus virginianus) are the most sought-after deer species in America. The antlers of mammals, such as deer, are one of the fastest regenerative tissues in the world and are grown and naturally cast every year. Research on cast antlers have been used for a variety of purposes including population comparisons and impacts of deer health due to climatic stressors. When investigating cast antlers, it is important to confirm individuality of match sets in addition to antlers of the same individual between years. Therefore, individuality must be confirmed genetically, and protocols must be developed and established to do so. Our objectives were to 1) establish a genetic protocol to harvest DNA from cast antlers using connective tissue, and 2) determine individuality from subsequent years and match sets. When fresh antlers are cast, they leave behind a viable connective tissue from which DNA can be extracted. The DNA was successfully extracted from the skin rings harvested from naturally cast antlers. This study developed viable methods to confirm individuality, which aid researchers and wildlife biologists in a better understanding of the white-tailed deer herd as they set management goals and harvest regulations.

Prophet Inequality for Bipartite Matching: Merits of Being Simple and Nonadaptive

We consider the Bayesian online selection problem of a matching in bipartite graphs, that is, the weighted online matching problem where the edges arrive online and edge weights are generated from a known distribution. This setting corresponds to the intersection of two matroids in the work of Kleinberg and Weinberg [ 40 ] and Feldman et al. [ 27 ]. We study a simple class of nonadaptive policies that we call vertex-additive policies. A vertex-additive policy assigns static prices to every vertex in the graph and accepts only those edges whose weight exceeds the sum of the prices on the edge endpoints. We show that there exists a vertex-additive policy with the expected payoff of at least one-third of the prophet’s payoff and present a gradient descent algorithm that quickly converges to the desired vector of vertex prices. Our results improve on the adaptive online policies of Kleinberg and Weinberg and Feldman et al. for the intersection of two matroids in two ways: our policy is nonadaptive and has a better approximation guarantee of 3 instead of the previous guarantees of 5.82 in Kleinberg and Weinberg and 5.43 in Feldman et al. We give a complementary lower bound of 2.25 for any online algorithm in the bipartite matching setting. Funding: This work was supported by Fundamental Research Funds of the Central Universities in China, a Science and Technology Innovation 2030 major project [“New Generation of Artificial Intelligence,” Project 2018AAA0100903], the Shanghai Municipal Education Commission Innovation Program, and the Program for Innovative Research Team of the Shanghai University of Finance and Economics (IRTSHUFE).

The lattice of worker-quasi-stable matchings

In a many-to-one matching model, we study the set of worker-quasi-stable matchings when firms' choice functions satisfy substitutability. Worker-quasi-stability is a relaxation of stability that allows blocking pairs involving a firm and an unemployed worker. We show that this set has a lattice structure and define a Tarski operator on this lattice that models a re-equilibration process and has the set of stable matchings as its fixed points.

Impossibility of weakly stable and strategy-proof mechanism

Many real matching markets are subject to distributional constraints. When the set of feasible matchings is restricted by some distributional constraints, a stable matching may not exist. In contrast, a weakly stable matching is guaranteed to exist under a very general class of constraints that satisfies a condition called heredity. However, it has been an open question whether a weakly stable matching can be obtained by a strategy-proof mechanism. We negatively answer this open question; no weakly stable and strategy-proof mechanism exists under any heredity feasibility constraint in general.

Quantifying the potential of morphological parameters for human dental identification: part 3—selecting the strongest skeletal identifiers in the mandible

The current study aimed to select the best mandibular morphological identifiers. One-hundred eighty-five panoramic radiographs were retrospectively collected, in which four landmarks were located on the mandible: the most superior point of the condyle right/left (CONR/L), of the coronoid right/left (CORR/L), of the mandibular lingula right/left (LINR/L), and the most mesial point of the mental foramen right/left (MMFR/L). Five linear measurements, 6 angles, and 10 ratios were measured bilaterally. Three groups of statistics were considered: (1) mean potential set; (2) inter-observer agreement quantified by intra-class correlation (ICC) and within-subject coefficient of variation (WSCV); and (3) Spearman correlation. Parameters were selected for a step-by-step cascade. In a univariate approach, the following parameters proved to have the best identifying capacity: ratio 3 right (between lines CONR - CORR and LINR - MMFR) with mean potential set 13%, ICC 0.90, and WSCV 4.8%; ratio 4 (between lines CONR/L - CORR/L and MMFR - MMFL) with mean potential set 13%, ICC 0.92, and WSCV 8.9%; and angle 4 left (between landmarks LINL, MMFL, and MMFR) with mean potential set of 18%, ICC 0.91, and WSCV 1.2%. The correlation coefficients ranged from 0.01 to 0.33. In a multivariate approach, the identifying capacity improved drastically, with all ratios combined as the strongest identifier (mean potential set 1.29%). In conclusion, a single ratio or a single angle already narrows down the set of potential matches, but the mean potential set remains relatively large. Combining all ratios drastically increases the certainty of the match.