Merging Approach Research Articles

Deep Q-Network-Enabled Platoon Merging Approach for Autonomous Vehicles

Platoon merging control of autonomous vehicles driving in a platoon formation can improve traffic efficiency. However, current platoon merging control approaches primarily rely on rules, making it challenging to achieve optimal control. In this study, we propose a platoon merging control approach based on a deep Q-network (DQN). First, we specify the state and action of the vehicle and establish a set of reward functions to ensure safe driving. Then, the DQN algorithm is used to train a neural network suitable for merging the controls of connected and automated vehicles (CAVs) to continuously approach the state-value function. Finally, we compare the proposed approach with a rule-based (RB) vehicle merging approach using a MATLAB simulation. In particular, CAVs are driven simultaneously using the two approaches in a random environment. The simulation results show that the proposed DQN-based vehicle merging approach requires less merging travel time and fewer vehicle lane change times than the RB approach. Additionally, merging can result in improved capacity in medium and high traffic densities compared with no-merging: the higher the CAV penetration rate, the larger the improvement. We verify the effectiveness of the proposed approach for different initial conditions. We suggest that the proposed method is a safe and robust method for CAV platoon merging, and that it can be applied to increase the capacity of freeways and roads.

On the methodology of three-way structured merge in version control systems: Top-down, bottom-up, or both

Three-way merging is an essential component of version control systems. Despite the efficiency of the conventional line-based textual methods, syntax-based structured approaches have demonstrated benefits in improving merge accuracy. Prior structured merging approaches visit abstract syntax trees in a top-down manner, which struggles to identify and merge shifted code generally. This work introduces a novel methodology combining a top-down and a bottom-up visit of abstract syntax trees, which manipulates shifted code effectively and elegantly. Especially, we reduce the merge problem of ordered lists to computing a topological sort of strongly connected components of the constraint graph. Compared with four representative merge tools in 40,533 real-world merge scenarios, our approach achieves the highest merge accuracy while being 2.5x as fast as a state-of-the-art structured merge tool.

Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.

Fragment merging approach for design, synthesis, and biological assessment of urea/acetyl hydrazide clubbed thienopyrimidine derivatives as GSK-3β inhibitors

New thienopyrimidine derivatives were designed and synthesized as GSK-3β inhibitors based on the structure of active binding site of GSK-3β enzyme. In this study, compounds 6b and 6a were found to be moderate GSK-3β inhibitors with IC50s 10.2 and 17.3 μM, respectively. Molecular docking study was carried out by docking the targeted compounds in the binding site of the GSK-3β enzyme using the MOE program. Moreover, ADME study was performed to predict certain pharmacokinetic properties. The results showed that all synthesized compounds may not be able to penetrate the blood brain barrier; so, the chances of CNS side effects are predicted to be low. CYP1D6 is predicted to be inhibited by compounds (5a, 5d, 6a, 9a and 9b), So drug-drug interactions are expected upon administration of these compounds.Graphical

ScAce: an adaptive embedding and clustering method for single-cell gene expression data.

Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment. In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.

Heterocyclic Merging of Stereochemically Diverse Chiral Piperazines and Morpholines with Indazoles.

We report a heterocyclic merging approach to construct novel indazolo-piperazines and indazolo-morpholines. Starting from chiral diamines and amino alcohols, novel regiochemically (1,3 and 1,4) and stereochemically diverse (relative and absolute) cohorts of indazolo-piperazines and indazolo-morpholines were obtained within six or seven steps. The key transformations involved are a Smiles rearrangement to generate the indazole core structure and a late-stage Michael addition to build the piperazine and morpholine heterocycles. We further explored additional vector diversity by incorporating substitutions on the indazole aromatic ring, generating a total of 20 unique, enantiomerically pure heterocyclic scaffolds.

Assessment and Data Fusion of Satellite-Based Precipitation Estimation Products over Ungauged Areas Based on Triple Collocation without In Situ Observations

Reliable assessment of satellite-based precipitation estimation (SPE) and production of more accurate precipitation data by data fusion is typically challenging in sparsely gauged and ungauged areas. Triple collocation (TC) is a novel assessment approach that does not require gauge observations; it provides a feasible solution for this problem. This study comprehensively validates the TC performance for assessing SPEs and performs data fusion of multiple SPEs using the TC-based merging (TCM) approach. The study area is the Tibetan Plateau (TP), a typical area lacking gauge observations. Three widely used SPEs are used: the integrated multi-satellite retrievals for global precipitation measurement (IMERG) “early run” product (IMERG-E), the precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) dynamic infrared (PDIR), and the Climate Prediction Center (CPC) morphing technique (CMORPH). Validation of the TC assessment approach shows that TC can effectively assess the SPEs’ accuracy, derive the spatial accuracy pattern of the SPEs, and reveal the accuracy ranking of the SPEs. TC can also detect the SPEs’ accuracy patterns, which are difficult to obtain from a traditional approach. The data fusion results of the SPEs show that TCM incorporates the regional advantages of the individual SPEs, providing more accurate precipitation data than the original SPEs, revealing that data fusion is reasonable and reliable in ungauged areas. In general, the TC approach performs well for the assessment and data fusion of SPEs, showing reasonable applicability in the TP and other areas lacking gauge data than other methods because it does not rely on gauge observations.

Fragment merging approach for the design of thiazole/thiazolidine clubbed pyrazoline derivatives as anti-inflammatory agents: Synthesis, biopharmacological evaluation and molecular modeling studies

Fragment merging approach was applied for the design of thiazole/thiazolidinone clubbed pyrazoline derivatives 5a-e, 6a-c, 7 and 10a-d as dual COX-2 and 5-LOX inhibitors. Compounds 5a, 6a, and 6b were the most potent and COX-2 selective inhibitors (IC50= 0.03–0.06 μM, SI = 282.7–472.9) with high activity against 5-LOX (IC50 = 4.36–4.86 μM), while compounds 5b and 10a were active and selective 5-LOX inhibitors with IC50 = 2.43 and 1.58 μM, respectively. In vivo assay and histopathological examination for most active candidate 6a revealed significant decrease in inflammation with higher safety profile in comparison to standard drugs. Compound 6a exhibited the same orientation and binding interactions as the reference COX-2 and 5-LOX inhibitors (celecoxib and quercetin, respectively). Consequently, compound 6a has been identified as a potential lead for further optimization and the development of safe and effective anti-inflammatory drugs.

A Method for Merging Multi-Source Daily Satellite Precipitation Datasets and Gauge Observations over Poyang Lake Basin, China

Obtaining precipitation estimates with high resolution and high accuracy is critically important for regional meteorological, hydrological, and other applications. Although satellite precipitation products can provide precipitation fields at various scales, their applications are limited by the relatively coarse spatial resolution and low accuracy. In this study, we propose a multi-source merging approach for generating accurate and high-resolution precipitation fields on a daily time scale. Specifically, a random effects eigenvector spatial filtering (RESF) method was first applied to downscale satellite precipitation datasets. The RESF method, together with Kriging, was then applied to merge the downscaled satellite precipitation products with station observations. The results were compared against observations and a data fusion dataset, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). It was shown that the estimates of the proposed method significantly outperformed the individual satellite precipitation product, reducing the average value of mean absolute error (MAE) by 52%, root mean square error (RMSE) by 63%, and improving the mean value of Kling–Gupta efficiency (KGE) by 157%, respectively. Daily precipitation estimates exhibited similar spatial patterns to the MSWEP products, and were more accurate in almost all cases, with a 42% reduction in MAE, 46% reduction in RMSE, and 79% improvement in KGE. The proposed approach provides a promising solution to generate accurate daily precipitation fields with high spatial resolution.

Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar

Study regionthis study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focusthe rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the regionbased on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.

An improved differential bond energy algorithm with fuzzy merging method to improve the document clustering for information mining

AbstractThe vast and diversified text materials on the internet in recent years have drastically increased the importance of information mining. By organizing documents into cohesive groupings, a document clustering method is a suitable tool for dealing with massive amounts of documents. Text documents, on the other hand, include sparse and uninformative features like noise, unrelated, and unneeded features, which reduce the efficiency of the document clustering methods. For noise removal, this work employs the hyperclique pattern‐based data cleaner pre‐processing approach. Then, differential bond energy algorithm (DBEA) is combined with a fuzzy merging approach and termed improved differential bond energy algorithm with fuzzy merging (IDBEFM) to handle the issues present in text document clustering. It seeks to discover and display natural variable clusters among large amounts of data. IDBEFM clusters are documented in three steps: in the first step, a cluster similarity matrix is instantiated by applying the DBEA. The second step develops an innovative technique for automatically dividing the cluster matrix into compact cohesive clusters, and in the third step, a fuzzy merging approach is used for combining identical clusters by the correlations and interrelationships between the resultant clusters. The test findings demonstrated that the accomplishment of the proposed technique is much superior to standard clustering algorithms.

Acute Kidney Injury Survivor Care Following Hospital Discharge: A Mixed-Methods Study of Nephrologists and Primary Care Providers.

Widespread delivery of high-quality care for acute kidney injury (AKI) survivors after hospital discharge requires a multidisciplinary team. We aimed to compare management approaches between nephrologists and primary care providers (PCPs) and explored strategies to optimize collaboration. Explanatory sequential mixed-methods study using a case-based survey followed by semi-structured interviews. Nephrologists and PCPs providing AKI survivor care at 3 Mayo Clinic sites and the Mayo Clinic Health System were included. Survey questions and interviews elucidated participants' recommendations for post-AKI care. Descriptive statistics were used to summarize survey responses. Qualitative data analysis used deductive and inductive strategies. A connecting and merging approach was used for mixed-methods data integration. 148 of 774 (19%) providers submitted survey responses (24/72 nephrologists and 105/705 PCPs). Nephrologists and PCPs recommended laboratory monitoring and follow-up with a PCP shortly after hospital discharge. Both indicated that the need for nephrology referral, and its timing should be dictated by clinical and non-clinical patient-specific factors. There were opportunities for improvement in medication and comorbid condition management in both groups. Incorporation of multidisciplinary specialists (eg, pharmacists) was recommended to expand knowledge, optimize patient-centered care, and alleviate provider workload. Survey findings may have been affected by non-response bias and the unique challenges facing clinicians and health systems during the COVID-19 pandemic. Participants were from a single health system, and their views or experiences may differ from those in other health systems or serving different populations. A multidisciplinary team-based model of post-AKI care may facilitate implementation of a patient-centered care plan, improve adherence to best practices, and reduce clinician and patient burden. Individualizing care for AKI survivors based on clinical and non-clinical patient-specific factors is needed to optimize outcomes for patients and health systems.

Adaptive Merging on Phase Change Memory

Indexing is a well-known database technique used to facilitate data access and speed up query processing. Nevertheless, the construction and modification of indexes are very expensive. In traditional approaches, all records in the database table are equally covered by the index. It is not effective, since some records may be queried very often and some never. To avoid this problem, adaptive merging has been introduced. The key idea is to create an index adaptively and incrementally as a side-product of query processing. As a result, the database table is indexed partially depending on the query workload. This paper faces the problem of adaptive merging for phase change memory (PCM). The most important features of this memory type are limited write endurance and high write latency. As a consequence, adaptive merging should be investigated from the scratch. We solve this problem in two steps. First, we apply several PCM optimization techniques to the traditional adaptive merging approach. We prove that the proposed method (eAM) outperforms a traditional approach by 60%. After that, we invent the framework for adaptive merging (PAM) and propose a new variant of the PCM-optimized index. It further improves the system performance by 20% for databases where search queries interleave with data modifications.

Mid‐Tropospheric Layer Temperature Record Derived From Satellite Microwave Sounder Observations With Backward Merging Approach

AbstractWe present a new version (v5.0) of the NOAA Center for Satellite Applications and Research (STAR) mid‐tropospheric temperature (TMT) time series. This data set uses a backward‐merging approach to intercalibrate 16 satellite‐based microwave sounding records. The instrument observations included those from the Microwave Sounding Unit (MSU) during 1979–2004, Advanced Microwave Sounding Unit‐A (AMSU‐A) during 1998–2017, and Advanced Technology Microwave Sounder (ATMS) from 2011 to present. A TMT time series during 2002–present based on satellite microwave observations in stable sun‐synchronous orbits was used as a reference in the backward merging process in which earlier satellites were adjusted and merged to the reference. Observations from earlier satellites were recalibrated to remove their calibration drifting errors relative to the reference using sequential overlapping observations. This included removal of spurious warming drifts in the MSU observations onboard NOAA‐11, NOAA‐12, and NOAA‐14 and a spurious cooling drift in the NOAA‐15 AMSU‐A observations. Temperature changes resulting from diurnal sampling drifts were corrected using an observation‐based semi‐physical model developed in this study. Other adjustments included channel frequency differences between MSU and AMSU‐A companion channels and instrument blackbody warm target effect on observed radiances. These adjustments resulted in inter‐consistent TMT records spanning MSU, AMSU‐A, and ATMS. The merged time series produced a global mean TMT trend of 0.092 ± 0.043 K/decade during 1979–2021 and a total tropospheric trend of 0.142 ± 0.045 K/decade after removal of a stratospheric cooling effect in TMT. Remarkably, the total tropospheric trends during the latest half period were nearly doubled the earlier half period over the global ocean.

Hybrid modeling of viscoelastic and switching–induced heating in ferroelectrics

AbstractThis contribution focuses on two effects causing a temperature change in electrically loaded ferroelectrics. These materials are often subjected to cyclic loading, where viscoelasticity prevails at lower electric fields, whereas dissipative heating by domain switching dominates above the coercive field. While viscoelasticity is modeled within a phenomenological, rheological framework, the ferroelectric constitutive behaviour is described based on a microphysical model. Merging both approaches results in the hybrid model presented in this paper.

Design, synthesis and characterisation of a novel type II B-RAF paradox breaker inhibitor

The mutation V600E in B-Raf leads to mitogen activated protein kinase (MAPK) pathway activation, uncontrolled cell proliferation, and tumorigenesis. ATP competitive type I B-Raf inhibitors, such as vemurafenib (1) and PLX4720 (4) efficiently block the MAPK pathways in B-Raf mutant cells, however these inhibitors induce conformational changes in the wild type B-Raf (wtB-Raf) kinase domain leading to heterodimerization with C-Raf, causing paradoxical hyperactivation of the MAPK pathway. This unwanted activation may be avoided by another class of inhibitors (type II) which bind the kinase in the DFG-out conformation, such as AZ628 (3) preventing heterodimerization. Here we present a new B-Raf kinase domain inhibitor, based on a phenyl(1H-pyrrolo [2,3-b]pyridin-3-yl)methanone template, that represents a hybrid between 4 and 3. This novel inhibitor borrows the hinge binding region from 4 and the back pocket binding moiety from 3. We determined its binding mode, performed activity/selectivity studies, and molecular dynamics simulations in order to study the conformational effects induced by this inhibitor on wt and V600E mutant B-Raf kinase. We discovered that the inhibitor was active and selective for B-Raf, binds in a DFG-out/αC-helix-in conformation, and did not induce the aforementioned paradoxical hyperactivation in the MAPK pathway. We propose that this merging approach can be used to design a novel class of B-Raf inhibitors for translational studies.

A Novel Merging Method for Generating High-Quality Spatial Precipitation Information over Mainland China

Accurate estimation of precipitation is critically important for a variety of fields, such as climatology, meteorology, and water resources. However, the availability of precipitation measurements has proved to be spatially inadequate for many applications. In this study, to acquire high-quality precipitation fields with enhanced accuracy and a fine-scale spatial resolution of 1 km × 1 km, we developed a new data fusion method by establishing an energy function model using the downscaled Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) precipitation product and high-density station observation in mainland China. Our merging approach was inspired by the interdisciplinary research framework integrating the methods in the fields of image processing, earth science, and machine learning. Cross-validation analyses were performed for the monthly precipitation over the period 2009–2018. It was found that the results of the newly developed method were more accurate than the original IMERG products in terms of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and Kling–Gupta efficiency (KGE). The merging precipitation results exhibit consistent spatial patterns with the original IMERG products, yet have good agreement with station observations. The gauge observations were the major source of the prediction skill of precipitation for the proposed method, and the downscaled-IMERG precipitation products added additional spatial details in the final merging results. Results indicate that the proposed merging method can reproduce the spatial details of the precipitation fields as well as enhance their accuracy. In addition, the time evolution of the error index indicates that the improvement in the merged result was stable over time, with KGE improving by 14% on average. The developed approach provides a promising way of estimating precipitation with high spatial resolution and high accuracy, which will benefit hydrological and climatological studies.

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision.

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.

Ultrasonic multi-view data merging using the vector coherence factor

Over the years, ultrasonic imaging, both in the context of medical and industrial applications, has ushered in the development of many advanced techniques, some based on the use of the phase of the measured signal instead of its amplitude. These techniques use what is called coherence factors to enhance image quality. Leveraging these factors when calculating the image provides an effective solution to the low restitution of small scatterers, where specular reflectors in conventional ultrasonic imaging are strongly restituted, by weighting an amplitude-based beamformer output. In the field of nondestructive testing, this solution generally boils down to multiplying the well-known Total Focusing Method (TFM) image by a coherence factor map which is also obtained through delay-and-sum (DAS). It has also been demonstrated that coherence factor maps, especially in the case of the vector coherence factor (VCF), can be used as interpretable images themselves, which then results in an amplitude-free imaging process with many potential advantages. Additionally, the fusion of multi-view (or multi-mode) TFM images is still of particular interest since the burden of selecting a single or several views for an inspection scenario highly depends on the geometry and position of the defect that needs to be identified. Although sensitivity maps have been introduced to help in the selection of relevant views, no similar tool currently exists for views generated using directly coherence factor maps. In this study, two different merging approaches for multi-view VCF images are explored and evaluated: pixel-by-pixel summation and maximum pixel value selection. A probabilistic threshold, derived from the VCF statistical background, is also defined to enhance the resulting merged view contrast. The images obtained through the proposed methods were evaluated both qualitatively and quantitatively using the Contrast-to-Noise Ratio (CNR). Experiments were conducted on two sets of samples. The first one was a 20 mm thick steel plate with Flat Bottom Holes (FBH) machined on its edge mimicking reflectors used for calibration of a circumferential weld inspection apparatus. The second one was composed of two 19.05 mm (34′′) thick steel plates with EDM notches at several angles centered at the mid-wall or surface-breaking on the backwall. Since the experimental setup, consisting of a 60-element probe, with a center frequency of 7.5 MHz, standing on a 36°Rexolite wedge, involved exciting mostly shear waves, only views composed of the transversal wave paths were considered for merging. Results show that by applying either one of the proposed merging methods, all artificial defects are reconstructed with a satisfactory CNR (above 30 dB), irrespective of the shape or location of the defects. Overall, the pixel-by-pixel summation and the maximum pixel value methods gave similar results. Applying the proposed threshold on the merged view improved the CNR by up to 15.8 dB. Finally, these simple but generic and robust multi-view merging methods and their associated thresholds could be employed while using a simplified hardware setup, as demonstrated previously in the literature.

Snippet-based result merging in federated search

In federated search, the central broker simultaneously forwards the search query to multiple resources. The returned results from those resources are then merged into a single ranked list. An autonomous resource in a federated search system usually does not provide scores for the retrieved documents; even if some of them do, scores from different resources are incomparable due to the heterogeneity in many aspects of those resource involved such as retrieval models and corpus statistics. Many results merging approaches have been proposed in the literature to deal with this problem. However, to the best of our knowledge, none of them has utilised snippets. This article proposes a snippet-based weighting scheme for the query terms involved. It quantifies the importance of each query term from two angles: the frequency of the term and the part in which the term occurs inside a snippet. Three parts – which are URL, title, and description – are given different weights. Experiments are conducted with the TREC 2013 FedWeb data set. The results show that the proposed methods consistently outperform several baseline models. We also find in many instances, a further small slight performance improvement is achievable by an extra measure of weighting each of the resources involved, which can be done in the phase of resource selection.