Efficient Algorithm Research Articles

A comprehensive analytical and computational assessment of soil water characteristics curves in Atlantic Canada: Application of a novel SelectKbestbased GEP model

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables.

The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a look-up table (LUT) is recently introduced to respond to this demand. However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the performance. To overcome this intrinsic limitation of the single-LUT solution, we propose a universal method to construct multiple LUTs like a neural network, termed MuLUT. Firstly, we devise novel complementary indexing patterns, as well as a general implementation for arbitrary patterns, to construct multiple LUTs in parallel. Secondly, we propose a re-indexing mechanism to enable hierarchical indexing between cascaded LUTs. Finally, we introduce channel indexing to allow cross-channel interaction, enabling LUTs to process color channels jointly. In these principled ways, the total size of MuLUT is linear to its indexing capacity, yielding a practical solution to obtain superior performance with the enlarged receptive field. We examine the advantage of MuLUT on various image restoration tasks, including super-resolution, demosaicing, denoising, and deblocking. MuLUT achieves a significant improvement over the single-LUT solution, e.g., up to 1.1dB PSNR for super-resolution and up to 2.8dB PSNR for grayscale denoising, while preserving its efficiency, which is 100× less in energy cost compared with lightweight deep neural networks. Our code and trained models are publicly available at https://github.com/ddlee-cn/MuLUT.

Outcomes of ilium and iliosacral Ewing's sarcoma resection reconstructed with tibial strut allograft.

Ilium is the most common site of pelvic Ewing's sarcoma (ES). Resection of the ilium and iliosacral joint causes pelvic disruption. However, the outcomes of resection and reconstruction are not well described. In this study, we report patients' outcomes after resection of the ilium and iliosacral ES and reconstruction with a tibial strut allograft. Medical files of 43 patients with ilium and iliosacral ES who underwent surgical resection and reconstruction with a tibial strut allograft between January 2010 and October 2021 were reviewed. The lesions were classified into four resection zones: I1, I2, I3, and I4, based on the extent of resection. Functional outcomes, oncological outcomes, and surgical complications for each resection zone were of interest. Functional outcomes were assessed using a Musculoskeletal Tumor Society (MSTS) score and Toronto Extremity Salvage Score (TESS). The mean age of the patients was 17 years (SD 9.1). At a mean follow-up of 70.8 months (SD 50), the mean functional outcomes were 24.2 points (SD 6.3) for MSTS and 81 points (SD 11) for TESS. The mean MSTS and TESS scores were associated with the iliac resection zone (< 0.001). Nine patients (20.9%) had local recurrence. The recurrence was not associated with the zone of iliac resection (p = 0.324). The two-year disease-free survival of the patients was 69.4%. The mean time to graft union was longer in patients with the I4 resection zone (p < 0.001). The complication rate was 34.9%, and nerve palsy (11.6%) was the most common. The rate of surgical complications was not associated with the resection zone. Reconstruction using tibial strut allograft is an efficient procedure after the resection of the ilium and iliosacral ES. Functional outcomes and complications of iliac ES depend on the resection zone, and inferior outcomes could be generally expected when more segments of the pelvic ring are resected, even if it is reconstructed.

The Alternating Direction Method of Multipliers for Sufficient Dimension Reduction

The minimum average variance estimation (MAVE) method has proven to be an effective approach to sufficient dimension reduction. In this study, we apply the computationally efficient optimization algorithm named alternating direction method of multipliers (ADMM) to a particular approach (MAVE or minimum average variance estimation) to the problem of sufficient dimension reduction (SDR). Under some assumptions, we prove that the iterative sequence generated by ADMM converges to some point of the associated augmented Lagrangian function. Moreover, that point is stationary. It also presents some numerical simulations on synthetic data to demonstrate the computational efficiency of the algorithm.

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

Efficient function approximation in enriched approximation spaces

Abstract An enriched approximation space is the span of a conventional basis with a few extra functions included, for example to capture known features of the solution to a computational problem. Adding functions to a basis makes it overcomplete and, consequently, the corresponding discretized approximation problem may require solving an ill-conditioned system. Recent research indicates that these systems can still provide highly accurate numerical approximations under reasonable conditions. In this paper we propose an efficient algorithm to compute such approximations. It is based on the AZ algorithm for overcomplete sets and frames, which simplifies in the case of an enriched basis. In addition, analysis of the original AZ algorithm and of the proposed variant gives constructive insights on how to achieve optimal and stable discretizations using enriched bases. We apply the algorithm to examples of enriched approximation spaces in literature, including a few nonstandard approximation problems and an enriched spectral method for a 2D boundary value problem, and show that the simplified AZ algorithm is indeed stable, accurate and efficient.

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

AbstractBackgroundThe rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions.MethodsWe developed a novel machine‐learning approach called GEP‐CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP‐CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis.ResultsWe identified and validated 1184 three‐SNP combinations that had the highest fitness function values. Notably, SERPINA1, ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1, EGF and MUC16. These SNPs‐related proteins were confirmed to interact with each other in the protein–protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms.ConclusionWe successfully developed an effective deep‐learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.

Improve robustness of machine learning via efficient optimization and conformal prediction

AbstractThe advance of machine learning (ML) systems in real‐world scenarios usually expects safe deployment in high‐stake applications (e.g., medical diagnosis) for critical decision‐making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance‐regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end‐to‐end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample‐efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP‐based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk‐aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms.

Rapid and Comprehensive Analysis of 41 Harmful Substances in Multi-Matrix Products by Gas Chromatography–Mass Spectrometry Using Matrix-Matching Calibration Strategy

Harmful substances in consumer goods pose serious hazards to human health and the environment. However, due to the vast variety of consumer goods and the complexity of their substrates, it is difficult to simultaneously detect multiple harmful substances in different materials. This paper presents a method for the simultaneous determination of 41 harmful substances comprising 17 phthalates (PAEs), 8 organophosphate flame retardants (OPFRs), and 16 polycyclic aromatic hydrocarbons (PAHs) in five types of products using the matrix-matching calibration strategy. The method employs an efficient ultrasonic extraction procedure using a mixture of dichloromethane and methylbenzene, followed by dissolution–precipitation and analysis through gas chromatography–mass spectrometry. Compared with previous experiments, we established a universal pretreatment method suitable for multi-matrix materials to simultaneously determine multiple harmful substances. To evaluate the effects of the matrix on the experimental results, we compared neat standard solutions and matrix-matching standard solutions. The results demonstrated that all compounds were successfully separated within 30 min with excellent separation efficiency. Additionally, the linear relationships of all analytes showed strong correlation coefficients (R2) of at least 0.995, ranging from 0.02 mg/L to 20 mg/L. The average recoveries of the target compounds (spiked at three concentration levels) were between 73.6 and 124.1%, with a relative standard deviation (n = 6) varying from 1.2% to 9.9%. Finally, we tested 40 different materials from consumer products and detected 16 harmful substances in 31 samples. Overall, this method is simple and accurate, and it can be used to simultaneously determine multiple types of hazardous substances in multi-matrix materials by minimizing matrix effects, making it an invaluable tool for ensuring product safety and protecting public health.

FastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data.

The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies. We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data. FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.

Empowering African vultures optimizer using Archimedes optimization algorithm for maximum efficiency for global optimization and feature selection

Empowering African vultures optimizer using Archimedes optimization algorithm for maximum efficiency for global optimization and feature selection

Dynamic optimization of die-less spinning process through autonomous modeling of time-varying forming law

Die-less spinning is a typical incremental forming technology with flexible processing parameters, including the roller path and loading parameters, whose forming law changes dynamically and easy-to-produce defects of excessive thinning and flange wrinkling. This brings a great challenge to the process design of die-less spinning. In this study, we proposed a novel dynamic optimization approach and system considering the time-varying forming law for the processing parameters in die-less spinning. The idea of proposed approach is that simulating the spinning process step by step through autonomous finite element modeling, real-time extracting and modeling the simulated spinning status at each forming step to capture the time-varying forming law, then gradually optimizing the processing parameters through efficient multi-objective optimization algorithm. By conducting the proposed dynamic optimization approach to the first spinning pass of die-less spinning, series of polynomial response surface models (PRSM) of wall thickness and flange wrinkling at various forming steps were developed to describe the time-varying forming law. On this basis, the roller path and matching loading parameters in each step were gradually optimized through a differential evolution algorithm with the multi-objective of minimizing wall thickness reduction, reducing flange wrinkling and maximizing forming efficiency. The optimized process produced better spun part than the traditional spinning process, presenting the wall thickness reduction ≤5 % and the flange wrinkling ≤2.5 mm. Furthermore, the time-varying influence of processing parameters on the wall thickness and flange wrinkling were elucidated based on the PRSM of spinning status at various forming steps.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

High-Resolution 17O Solid-State NMR as a Unique Probe for Investigating Oxalate Binding Modes in Materials: The Case Study of Calcium Oxalate Biominerals.

Oxalate ligands are found in many classes of materials, including energy storage materials and biominerals. Determining their local environments at the atomic scale is thus paramount to establishing the structure and properties of numerous phases. Here, we show that high-resolution 17O solid-state NMR is a valuable asset for investigating the structure of crystalline oxalate systems. First, an efficient 17O-enrichment procedure of oxalate ligands is demonstrated using mechanochemistry. Then, 17O-enriched oxalates were used for the synthesis of the biologically relevant calcium oxalate monohydrate (COM) phase, enabling the analysis of its structure and heat-induced phase transitions by high-resolution 17O NMR. Studies of the low-temperature COM form (LT-COM), using magnetic fields from 9.4 to 35.2 T, as well as 13C-17O MQ/D-RINEPT and 17O{1H} MQ/REDOR experiments, enabled the 8 inequivalent oxygen sites of the oxalates to be resolved, and tentatively assigned. The structural changes upon heat treatment of COM were also followed by high-resolution 17O NMR, providing new insight into the structures of the high-temperature form (HT-COM) and anhydrous calcium oxalate α-phase (α-COA), including the presence of structural disorder in the latter case. Overall, this work highlights the ease associated with 17O-enrichment of oxalate oxygens, and how it enables high-resolution solid-state NMR, for "NMR crystallography" investigations.

CLASSROOM VIGNETTES: Experiencing Efficiency of Algorithms and Implementations

CLASSROOM VIGNETTES: Experiencing Efficiency of Algorithms and Implementations

Efficient modeling of heat conduction in multiply bonded composites covered with thermal barrier coating

In engineering practice, 3D (three-dimensional) composites consisting of multiply thin layers of anisotropic materials have been extensively applied. For enhancing their thermal endurance, the composites are often coated with a very thin layer of medium with low conductivity, usually referred to as the thermal barrier coating (TBC). This paper presents an efficient algorithm for the boundary element method (BEM) to accurately compute the nearly singular integrals in modeling 3D anisotropic heat conduction in TBC-coated composites, consisting of several thin anisotropic media. This algorithm is especially efficient because no analytical transformation of the integrands is involved as proposed in the past. When the TBC is prescribed with Dirichlet condition, a new equivalent convective condition is proposed to replace the TBC modeling. Moreover, thin films of adhesives on interfaces are considered by introducing interfacial conductance. In the end, several examples are presented.

Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve

The estimation of the soil hydraulic conductivity curve (SHCC) through machine learning tools for determining pedotransfer functions (PTFs) is essential in various agriculture, hydrological, and environmental management-related studies. The accuracy and reliability of machine learning tools such as backpropagation neural network (BNN) for estimating SHCC mainly depends on the optimization of its architecture, specifically the selection of the network training algorithms. By comparing the performance of network training algorithms, various researchers related to agriculture science, hydrology and environmental can choose the appropriate algorithm that provides the best balance of accuracy, speed, computational efficiency, and robustness for hydrological modeling purposes. Therefore, the present study assessed and compared the performance of 4 training algorithms including, gradient descent with momentum (GDM), Quasi-Newton (QN), Bayesian regularization (BR) and Levenberg-Marquardt (LM) for the estimation of the parametric-based PTFs of the van Genuchten-Mualem hydraulic conductivity equation as novelty of this research, which has not been investigated, so far. In this study four GDM, BR, LM and QN-based PTFs were derived to estimate the SHCC from different combinations of readily available properties of 86 soil samples from UNSODA database. The integral root mean square error (IRMSE) and coefficient of determination (R2) and were used to assess the estimations. The performance of BR algorithm-based PTFs on testing data varied from 4.634 to 6.690 with average 5.476 cm day−1 in terms of IRMSE, and 0.958 to 0.966 with average 0.963 in terms of R2, which provided better simulation than other training algorithms. The outcome of the present study on testing data showed that the BR algorithm was stable and robust to heterogeneous and noisy data compared to other training algorithms. Finally, development of accurate and reliable PTFs using efficient algorithms can play a crucial role in soil resource assessments, water resource management, irrigation planning, and designing effective drainage systems, because accurate knowledge of SHCC is vital for optimizing irrigation and fertilization practices and minimizing wastage of water and nutrients.

Biochar from co-pyrolysis of biological sludge and woody waste followed by chemical and thermal activation: end-of-waste procedure for sludge management and biochar sorption efficiency for anionic and cationic dyes.

Nine biochars were produced by co-pyrolysis of sawdust and biological sludge following the "design of experiment" approach. Two kinds of sludge (both deriving from the treatment of mixed industrial-municipal wastewater) and two types of woody waste were selected as categorical predicting variables, while contact time, pyrolysis temperature, and sludge percentage were used as quantitative variables. Biochars were analysed for their product characteristics and environmental compatibility based on the European Standards (EN 12915-1:2009) for materials intended for water treatment (i.e. ash content, water leachable polycyclic aromatic hydrocarbons (PAHs) and elements), as well as for specific surface area (SSA), using them as response variables of a multivariate partial least square multiple regression, whose results provided interesting insights on the relationships between pyrolysis conditions and biochar characteristics. Biochars produced with sludge and/or providing the highest SSA values (258-370 m2g-1) were selected to undergo a sustainable chemical treatment using a by-product of the gasification of woody biomass, complying in all cases with European Standards and achieving therefore the end-of-waste status for sewage sludge. The biochar deriving from the highest percentage of sludge (30% by weight) and with the highest SSA (390 m2g-1) was thermally activated achieving SSA of 460 m2g-1 and then tested for the sorption of direct yellow 50 and methylene blue in ultrapure water and real wastewater, compared to a commercial activated carbon (AC). The biochar showed Langmuir sorption maxima (Qm) 2-9 times lower than AC, thus highlighting promising sorption performances. Qm for methylene blue in wastewater (28mg‧g-1) was confirmed by column breakthrough experiments.

Analysis of Soil Health Parameters to Identify Important Soil Nutrients and Weights Using Feature Engineering for Multiple Agri-Advices

Introduction:: Identification of important soil nutrients is a very important task for precision farming and developing efficient machine learning models. Method:: The existing work shows that the patent is filed and published on a method and device for assessment of soil health parameters and recommendation of fertilizers. The existing work is done for one advice at a time not for several advices. Multiple advices that are taken into account for the task are appropriate crops, organic fertilizer, and combination 1 and combination 2 of fertilizers. objective: Apply feature selection techniques based on Chi-Square, ANOVA and Mutual Information Gain scoring functions such as Select K Best and Select Percentile for multiple agri-advice dataset of Pune District regions to identify important soil health features to reduce the complexity of classification models and in turn reduce space and the computational time of different classification models. Result:: This paper presented results of feature selection techniques based on Chi-Square, ANOVA and Mutual Information Gain scoring functions such as Select K Best and Select Percentile for multiple agri-advice datasets of Pune District regions to identify important soil health features. Conclusion:: As per Chi-Square, ANOVA and Mutual Information scoring functions with Select K Best and Select Percentile techniques ‘Mn’ was the most important parameter and Cu’ and ‘B’ were the least important parameters among all 11 parameters common in 4 agriculture advices. Whereas Ph, K, Fe, 'OC', 'N', 'S', 'Mn', and 'P' will be used for future research work on the development of an efficient classification algorithm for multi-advice generators.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies.