Weight Bipartite Matching Research Articles

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

Long-Term Matching Optimization With Federated Neural Temporal Difference Learning in Mobility-on-Demand Systems

Efficient bipartite graph matching of orders and drivers is a central operational problem in industrial mobility-on-demand (MOD) systems. Traditional studies adopt pure combinatorial optimization models for order-and-driver matching, which do not consider the long-term rewards of the dynamic MOD decision making process. Toward long-term optimization, this article presents a systemic paradigm of online matching with federated neural temporal difference learning (FedTDLearning), which encompasses learning and matching phases. During the learning phase, the long-term matching process is modeled as a Markov decision process, which is typically solved by employing data-driven reinforcement learning in an offline central training scheme. Massive amounts of data would be generated on the network by industrial MOD systems. It is impossible to send all the large-scale industrial data to the cloud server for centralized model training due to network bandwidth limitations and safety concerns. Therefore, a generic and innovative FedTDLearning is proposed to achieve long-term matching in a distributed manner. During the matching phase, a real-time bipartite matching optimization problem is formulated to maximize the learned spatiotemporal value and minimize the pickup distance, which is reducible to the minimum-cost maximum weight bipartite graph matching problem. A distance-learned-value ratio algorithm is proposed to find an optimal matching in the bipartite graph based on the joint optimization of FedTDLearning and the combinatorial fractional programming approach. Furthermore, to pursue optimal computation efficiency, we solve the real-time matching problem by constructing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k$ </tex-math></inline-formula> NN) bipartite graph where each order is connected with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN drivers. Using openly available real-world data, the prototype system and experimental evaluations show that our proposed algorithms have effective problem-solving capability in practice.

Optimizing the Age of Information for Multi-Source Information Update in Internet of Things

Age of Information (AoI) has recently become a new performance metric that captures the freshness/timeliness of information at end users in Internet of Things (IoT). Most existing information update mechanisms mainly consider the status information from a single Source Node (SN) or from multiple SNs with independent observations. However, in a practical IoT system, each status could be observed by multiple SNs, which brings new challenge in optimizing the AoI. To consider this issue, this paper formulates two optimization problems, named AoI-aware Multi-source Information Update problem (<i>AoI-MSIU</i>) and AoI-Reduction-aware Multi-source Information Update problem (<i>AoIR-MSIU</i>) problem, respectively. Specifically, AoI-MSIU problem assumes that the status are the first time to be uploaded to the BS, while AoIR-MSIU problem supposes that the BS already maintains the status that it has received before. Besides maximizing the revenue of information update, both problems consider how to optimize the AoI of the status. For AoI-MSIU problem, we prove that it is NP-hard, and propose an efficient greedy algorithm with a guaranteed approximation ratio. For AoIR-MSIU problem, we propose a polynomial-time optimal solution, which is based on a maximum weight bipartite matching on an auxiliary graph. Finally, we evaluate the performance of the proposed schemes through simulations.

An efficient data transmission scheme through 5G D2D-enabled relays in wireless sensor networks

Energy-efficient data transmission is a key challenge in Wireless Sensor Networks (WSNs). Cluster heads (CHs) are often used to relay aggregated data from sensor nodes to a central base station (BS). However, the transmission range of such relay nodes is limited, necessitating multihop communication for forwarding data from edge relays to BS. Further, links may not be created due to the unavailability of suitable relays resulting in unreliable networks. The use of cellular device-to-device (D2D) communication eliminates the requirement of very short-range communication of WSNs and also helps in maintaining a good link throughput. In this paper, a cellular D2D-assisted relay communication scheme has been proposed for forwarding data where the issue of link disconnection is solved using dual connectivity of D2D and cellular communication among cluster heads. The proposed scheme, DSPA (D2D Relay Node (DRN) Selection and uplink Power Allocation), uses the Maximum Weight Bipartite Matching for selection of relays. Performance analysis shows that the higher sleeping probability of the sensor nodes improves the overall energy efficiency of the network and also increases the average hop gain.

A Modified Decomposition Algorithm for Maximum Weight Bipartite Matching and Its Experimental Evaluation

Let G be an undirected bipartite graph with positive integer weights on the edges. We refine the existing decomposition theorem originally proposed by Kao et al., for computing maximum weight bipartite matching. We apply it to design an efficient version of the decomposition algorithm to compute the weight of a maximum weight bipartite matching of G in O(|V |W /k(|V |, W /N))-time by employing an algorithm designed by Feder and Motwani as a subroutine, where |V | and N denote the number of nodes and the maximum edge weight of G, respectively and k(x, y) = log x/ log(x 2 /y). The parameter W is smaller than the total edge weight W, essentially when the largest edge weight differs by more than one from the second largest edge weight in the current working graph in any decomposition step of the algorithm. In best case W = O(|E|) where |E| be the number of edges of G and in worst case W = W, that is, |E| ≤ W ≤ W. In addition, we talk about a scaling property of the algorithm and research a better bound of the parameter W. An experimental evaluation on randomly generated data shows that the proposed improvement is significant in general.

Maximization of Two Techniques of Relay Networks using Co-Operative Protocols

This paper considers the transfer helped co-usable transmission in remote systems, Where numerous client pair lead bidirectional interchanges by means of different transfers dependent on Orthogonal Frequency Division Multiplexing (OFDM) transmission. The fundamental goal is to improve the general framework execution by augmenting the complete throughput. It manages the joint enhancement of channel and hand-off task, subcarrier portion just as hand-off determination. The issue is detailed as a combinatorial improvement issue. It primarily manages two-way transferring and to make it progressively manageable. It received a chart based methodology. Along these lines the issue is fathomed ideally in polynomial time by changing it into Maximum Weighted Bipartite Matching (MWBM) issue. The reproduction result shows the correlation of the proposed calculation and the seat mark alongside two handing-off conventions.

Maximization of Two Techniques of Relay Networks using Co-Operative Protocals

This study recognise the transfer-assisted co-usable transmission in remote systems, where numerous client pairs lead bidirectional interchanges through various transfers depending on the transmission of the Orthogonal Frequency Division Multiplexing. The fundamental goal is to improve the overall execution of the framework by increasing the overall performance. It manages the joint enhancement of channel and hand-off task, subcarrier portion just as hand-off determination. The issue is detailed as a combinatorial improvement issue. It primarily manages two-way transferring and to make it progressively manageable. It received a chart based methodology. Along these lines the issue is fathomed ideally in polynomial time by changing it into Maximum Weighted Bipartite Matching (MWBM) issue. The reproduction result shows the correlation of the proposed calculation and the seat mark alongside two handing-off conventions.

QoS-Based DWBA Algorithm for NG-EPON

The next-generation Ethernet passive optical network (NG-EPON) is basically classified into two architectures on the basis of the wavelength sharing by the optical network units (ONUs). The single scheduling domain (SSD) and multi-scheduling domain (MSD) EPON are the two different design architectures for NG-EPON. A vital task in NG-EPON is to design dynamic wavelength bandwidth allocation (DWBA) algorithms that can meet the future demands of the network subscribers. A number of DWBA algorithms have been designed for time and wavelength division multiplex (TWDM) EPON. The existing DWBA algorithms for TWDM-EPON could be used in MSD-EPON by making necessary parametric changes. The design and implementation of new DWBA algorithms for MSD-EPON are still required specifically. In this paper, we have proposed a quality of service (QoS)-based DWBA algorithm for NG-EPON. We have comparatively analyzed our proposed DWBA with the existing algorithms like earlier finished time (EFT), weighted bipartite matching (WBM), and earlier finished time with void filling (EFT-VF). The results show that our proposed DWBA algorithm performs better as compared to EFT, WBM, and EFT-VF on the basis of average packet delay and average completion time for NG-EPON.

Maximizing throughput gain via resource allocation in D2D communications

By reusing the cellular resources, device-to-device (D2D) communication is becoming a very promising technology that greatly enhances the spectrum utilization. To harvest the benefits that D2D communications can offer, efficient resource allocation strategy is required to guarantee the demands of quality of service (QoS) for both cellular and D2D users. This paper proposes a resource allocation scheme to alleviate the performance deterioration of the D2D communications with spectrum reuse. To maximize the overall throughput gain, the proposed scheme is designed to reduce the rate loss of cellular users and improve the rate of D2D users simultaneously in a two-step manner. Specifically, it first calculates the reuse gain for a single D2D pair and a single cellular user. Next, a maximum weight bipartite matching is further proposed to select the reuse pair to maximize the overall network throughput gain. Numerical results demonstrate that the proposed resource allocation scheme can significantly improve the network throughput performance with average user rate guaranteed.

Systematic module approach identifies altered genes and pathways in four types of ovarian cancer.

The present study aimed to identify altered genes and pathways associated with four histotypes of ovarian cancer, according to the systematic tracking of dysregulated modules of reweighted protein-protein interaction (PPI) networks. Firstly, the PPI network and gene expression data were initially integrated to infer and reweight normal ovarian and four types of ovarian cancer (endometrioid, serous, mucinous and clear cell carcinoma) PPI networks based on Spearman's correlation coefficient. Secondly, modules in the PPI network were mined using a clique-merging algorithm and the differential modules were identified through maximum weight bipartite matching. Finally, the gene compositions in the altered modules were analyzed, and pathway functional enrichment analyses for disrupted module genes were performed. In five conditional-specific networks, universal alterations in gene correlations were revealed, which leads to the differential correlation density among disrupted module pairs. The analyses revealed 28, 133, 139 and 33 altered modules in endometrioid, serous, mucinous and clear cell carcinoma, respectively. Gene composition analyses of the disrupted modules revealed five common genes (mitogen-activated protein kinase 1, phosphoinositide 3-kinase-encoding catalytic 110-KDα, AKT serine/threonine kinase 1, cyclin D1 and tumor protein P53) across the four subtypes of ovarian cancer. In addition, pathway enrichment analysis confirmed one common pathway (pathways in cancer), in the four histotypes. This systematic module approach successfully identified altered genes and pathways in the four types of ovarian cancer. The extensive differences of gene correlations result in dysfunctional modules, and the coordinated disruption of these modules contributes to the development and progression of ovarian cancer.

A novel non-rigid registration algorithm for zebrafish larval images.

Precise three-dimensional mapping of a large number of gene expression patterns, neuronal types and connections to an anatomical reference helps us to understand the vertebrate brain and its development. Zebrafish has evolved as a model organism for such study. In this paper, we propose a novel non-rigid registration algorithm for volumetric zebrafish larval image datasets. A coarse affine registration using the L-BFGS algorithm is applied first on the moving dataset. We then divide this coarsely registered moving image and the reference image into a union of overlapping patches. Minimum weight bipartite graph matching algorithm is employed to find the correspondence between the two sets of patches. The corresponding patches are then registered using the diffeomorphic demons method with proper intra-patch regularization. For each voxel lying in the overlapping regions, we impose inter-patch regularization through a composite transformation obtained from the adjacent transformation fields. Experimental results on four multi-view confocal 3D datasets show the advantage of the proposed solution over the existing ViBE-Z software.

Systematic tracking of disrupted modules identifies significant genes and pathways in hepatocellular carcinoma.

The objective of the present study is to identify significant genes and pathways associated with hepatocellular carcinoma (HCC) by systematically tracking the dysregulated modules of re-weighted protein-protein interaction (PPI) networks. Firstly, normal and HCC PPI networks were inferred and re-weighted based on Pearson correlation coefficient. Next, modules in the PPI networks were explored by a clique-merging algorithm, and disrupted modules were identified utilizing a maximum weight bipartite matching in non-increasing order. Then, the gene compositions of the disrupted modules were studied and compared with differentially expressed (DE) genes, and pathway enrichment analysis for these genes was performed based on Expression Analysis Systematic Explorer. Finally, validations of significant genes in HCC were conducted using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. The present study evaluated 394 disrupted module pairs, which comprised 236 dysregulated genes. When the dysregulated genes were compared with 211 DE genes, a total of 26 common genes [including phospholipase C beta 1, cytochrome P450 (CYP) 2C8 and CYP2B6] were obtained. Furthermore, 6 of these 26 common genes were validated by RT-qPCR. Pathway enrichment analysis of dysregulated genes demonstrated that neuroactive ligand-receptor interaction, purine and drug metabolism, and metabolism of xenobiotics mediated by CYP were significantly disrupted pathways. In conclusion, the present study greatly improved the understanding of HCC in a systematic manner and provided potential biomarkers for early detection and novel therapeutic methods.

Systematic tracking of altered modules identifies disrupted pathways in teratozoospermia.

The objective of this study was to identify disrupted pathways in teratozoospermia by systematically tracking dysregulated modules in reweighted protein-protein interaction (PPI) networks. We inferred and reweighted the PPI networks of normal and teratozoospermia groups based on Spearman correlation coefficients. Modules in the PPI networks were explored via a clique-merging algorithm and altered modules were identified based on maximum weight bipartite matching. Furthermore, pathway-enrichment analyses of genes in altered modules were performed by Database for Annotation, Visualization, and Integrated Discovery (DAVID) to illuminate the biological pathways in teratozoospermia. A total of 20,102 genes were screened from the expression profile. We explored 2406 and 2101 modules in normal and disease PPI networks, respectively. Moreover, we obtained 875 altered modules by comparing modules in normal and teratozoospermia PPI networks. At P < 0.01, the genes involved in 2855 interactions with score changes >1 were mainly enriched in 66 pathways and the genes in altered modules were enriched in 71 pathways. The activity genes (missed and added genes in the disrupted modules) were enriched in 41 common pathways. There were 36 mutual enriched pathways under the five different conditions. Moreover, the cell cycle pathway was disrupted in the first 10 pathways of each condition. This study provides a powerful biomarker discovery platform to better understand the progression of teratozoospermia by systematically tracking dysregulated modules. This method uncovered potential diagnostic and therapeutic targets of teratozoospermia. This information might lead to improved monitoring and treatment of teratozoospermia.

Systematic Tracking of Disrupted Modules Identifies Altered Pathways Associated with Congenital Heart Defects in Down Syndrome.

BackgroundThis work aimed to identify altered pathways in congenital heart defects (CHD) in Down syndrome (DS) by systematically tracking the dysregulated modules of reweighted protein-protein interaction (PPI) networks.Material/MethodsWe performed systematic identification and comparison of modules across normal and disease conditions by integrating PPI and gene-expression data. Based on Pearson correlation coefficient (PCC), normal and disease PPI networks were inferred and reweighted. Then, modules in the PPI network were explored by clique-merging algorithm; altered modules were identified via maximum weight bipartite matching and ranked in non-increasing order. Finally, pathways enrichment analysis of genes in altered modules was carried out based on Database for Annotation, Visualization, and Integrated Discovery (DAVID) to study the biological pathways in CHD in DS.ResultsOur analyses revealed that 348 altered modules were identified by comparing modules in normal and disease PPI networks. Pathway functional enrichment analysis of disrupted module genes showed that the 4 most significantly altered pathways were: ECM-receptor interaction, purine metabolism, focal adhesion, and dilated cardiomyopathy.ConclusionsWe successfully identified 4 altered pathways and we predicted that these pathways would be good indicators for CHD in DS.

An optimal data association method based on the minimum weighted bipartite perfect matching

Data association is an important problem in simultaneous localization and mapping, however, many single frame based methods only provide suboptimal solutions. In this paper an optimal graph theoretic approach is proposed. We formulate the data association as an integer programming (IP) and then prove that it is equivalent to a minimum weight bipartite perfect matching problem. Therefore, optimally solving the bipartite matching problem implies optimally resolving the IP, i.e. the data association problem. We compare the proposed approach with other widely used data association methods. Experimental results validate the effectiveness and accuracy of the proposed approach, and manifest that this graph based data association method can be used for online application.

Temporal Identification of Dysregulated Genes and Pathways in Clear Cell Renal Cell Carcinoma Based on Systematic Tracking of Disrupted Modules.

Objective. The objective of this work is to identify dysregulated genes and pathways of ccRCC temporally according to systematic tracking of the dysregulated modules of reweighted Protein-Protein Interaction (PPI) networks. Methods. Firstly, normal and ccRCC PPI network were inferred and reweighted based on Pearson correlation coefficient (PCC). Then, we identified altered modules using maximum weight bipartite matching and ranked them in nonincreasing order. Finally, gene compositions of altered modules were analyzed, and pathways enrichment analyses of genes in altered modules were carried out based on Expression Analysis Systematic Explored (EASE) test. Results. We obtained 136, 576, 693, and 531 disrupted modules of ccRCC stages I, II, III, and IV, respectively. Gene composition analyses of altered modules revealed that there were 56 common genes (such as MAPK1, CCNA2, and GSTM3) existing in the four stages. Besides pathway enrichment analysis identified 5 common pathways (glutathione metabolism, cell cycle, alanine, aspartate, and glutamate metabolism, arginine and proline metabolism, and metabolism of xenobiotics by cytochrome P450) across stages I, II, III, and IV. Conclusions. We successfully identified dysregulated genes and pathways of ccRCC in different stages, and these might be potential biological markers and processes for treatment and etiology mechanism in ccRCC.

Collision Bottleneck Throughput in Bacterial Conjugation-based Nanonetworks

Bacterial conjugation-based nanonetwork has been recently proposed as a novel molecular communication paradigm, in which the bacteria act as carriers. This is the foundational work proposing the phenomenon of collision which occurs in the form of multi-conjugation of multiple carrier bacteria at the side of receiver nanodevice. We show the effect of this conjugation-based collision on the maximum achievable throughput of the network, using a simple graph-theoretic approach, namely, Maximum Weight Bipartite Matching. One of the several interesting results that emerges concerns the maximum achievable throughput, which is bounded by Θ(n/p) in case of homogeneous nodes, where n and p refer to the total number of nodes, and the vertical layers in the network, respectively.

EXTENDED COMPATIBILITY PATH BASED HARDWARE BINDING: AN ADAPTIVE ALGORITHM FOR HIGH LEVEL SYNTHESIS OF AREA-TIME EFFICIENT DESIGNS

Hardware binding is an important step in high level synthesis (HLS). The quality of hardware binding affects the area-time efficiency of a design. The goal of a synthesis process is to produce a design which meets the area-time requirements. In this paper, we present a new hardware binding algorithm with focus on area reduction. It is called extended compatibility path-based (ECPB) hardware binding and extends the compatibility path-based (CPB) hardware binding method by exploiting inter-operation flow dependencies, non-overlapping lifetimes of variables and modifying the weight relation in order to make it application aware and thus adaptive in nature. The presented methodology also takes into account bit width of functional units (FUs) and multi mode FUs. It performs simultaneous FU and register binding. Implemented within a C to register transfer level (RTL) framework, it produces binding results which are better than those produced by weighted bipartite matching (WBM) and CPB algorithms. The use of ECPB algorithm results in an average reduction of 34% and 17.44% in area-time product over WBM and CPB methods, respectively.

A case study for understanding the nature of redundant entities in bibliographic digital libraries

Purpose – The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case study of the name authority control problem in DLs. Design/methodology/approach – To find a sample of name variants across DLs (e.g. DBLP and ACM) and in a single DL (e.g. ACM), the approach is based on two bipartite matching algorithms: Maximum Weighted Bipartite Matching and Maximum Cardinality Bipartite Matching. Findings – First, the authors validated the effectiveness and efficiency of the bipartite matching algorithms. The authors also studied the nature of real cases of author name variants that had been found across DLs (e.g. ACM, CiteSeer and DBLP) and in a single DL. Originality/value – To the best of the authors knowledge, there is less research effort to understand the nature of author name variants shown in DLs. A thorough analysis can help focus research effort on real problems that arise when the authors perform duplicate detection methods.

Device-to-Device Communications Underlaying Cellular Networks

In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs.