The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicabili...The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicability and reliability of upward recursive formula in principle is amended.An optimal scheme of upward-and downward-joint recursions has been developed for the sequential F(z) computations.No additional accuracy is needed with the fundamental term of recursion because the absolute error of Fn(z) always decreases with the recursive approach.The scheme can be employed in modifying any of existent subprograms for Fn<z> computations.In the case of p-d-f-and g-type Gaussians,combining this method with Schaad's formulas can reduce,at least,the additive operations by a factor 40%;the multiplicative and exponential operations by a factor 60%.展开更多
A combinatory method of determining the turbulent fluxes in the surface layer has been developed and their general representations have been thus obtained.The universal functions of the (M-O) similarity in the surface...A combinatory method of determining the turbulent fluxes in the surface layer has been developed and their general representations have been thus obtained.The universal functions of the (M-O) similarity in the surface layer can be de- termined by the method.The results calculated by using the ITCE's data indicate that the method is feasible.展开更多
The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way...The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively.展开更多
Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well wi...Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant co...To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。展开更多
In this paper,we firstly establish a combinatorial identity with a free parameter x,and then by means of derivative operation,several summation formulae concerning classical and generalized harmonic numbers,as well as...In this paper,we firstly establish a combinatorial identity with a free parameter x,and then by means of derivative operation,several summation formulae concerning classical and generalized harmonic numbers,as well as binomial coefficients are derived.展开更多
In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or ove...In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.展开更多
I. INTRODUCTION The exploration for a unified basis of the combinatory logic and the predicate calculus will promote laying a strict and thorough mathematical foundation of the programming language possessing itself o...I. INTRODUCTION The exploration for a unified basis of the combinatory logic and the predicate calculus will promote laying a strict and thorough mathematical foundation of the programming language possessing itself of the functional and logic paradigms. The purpose of this note, proceeding from the algebraic oersoective, is to formulize the first-order mathematical展开更多
In the light of a question of J. L. Krivine about the consistency of an extensional λ-theory,an extensional combinatory logic ECL+U(G)+RU_∞+ is established, with its consistency model provedtheoretically and it is s...In the light of a question of J. L. Krivine about the consistency of an extensional λ-theory,an extensional combinatory logic ECL+U(G)+RU_∞+ is established, with its consistency model provedtheoretically and it is shown the it is not equivalent to any system of universal axioms. It is expressed bythe theory in first order logic that, for every given group G of order n, there simultaneously exist infinitelymany universal retractions and a surjective n-tuple notion, such that each element of G acts as a permutationof the components of the n-tuple, and as an Ap-automorphism of the model; further each of the universalretractions is invarian under the action of the Ap-automorphisms induced by G The difference between thetheory and that of Krivine is the G need not be a symmetric group.展开更多
In this study,a microscopic method for calculating the nuclear level density(NLD)based on the covariant density functional theory(CDFT)is developed.The particle-hole state density is calculated by a combinatorial meth...In this study,a microscopic method for calculating the nuclear level density(NLD)based on the covariant density functional theory(CDFT)is developed.The particle-hole state density is calculated by a combinatorial method using single-particle level schemes obtained from the CDFT,and the level densities are then obtained by considering collective effects such as vibration and rotation.Our results are compared with those of other NLD models,including phenomenological,microstatisti-cal and nonrelativistic Hartree–Fock–Bogoliubov combinatorial models.This comparison suggests that the general trends among these models are essentially the same,except for some deviations among the different NLD models.In addition,the NLDs obtained using the CDFT combinatorial method with normalization are compared with experimental data,including the observed cumulative number of levels at low excitation energies and the measured NLDs.The CDFT combinatorial method yields results that are in reasonable agreement with the existing experimental data.展开更多
Immunotherapy has efficiently revolutionized the treatment of human neoplastic diseases.However,the overall responsive rate of current immunotherapy is still unsatisfactory,benefiting only a small proportion of patien...Immunotherapy has efficiently revolutionized the treatment of human neoplastic diseases.However,the overall responsive rate of current immunotherapy is still unsatisfactory,benefiting only a small proportion of patients.Therefore,significant attention has been paid to the modulation of tumor microenvironment(TME)for the enhancement of immunotherapy.Interestingly,recent studies have shown that cyclic GMP-AMP synthasestimulator of interferon gene(cGAS-STING)was initially found as an innate immune sensor to recognize cytoplasmic DNA(such as bacterial,viral,micronuclei,and mitochondrial).It is a promising signaling pathway to activate antitumor immune responses via type I interferon production.Notably,Mn^(2+)was found to be a critical molecule to sensitize the activation of the cGAS-STING pathway for better immunotherapy.This activation led to the development of Mn^(2+)-based strategies for tumor immunotherapy via the activation of the cGAS-STING pathway.In this critical review,we aimed to summarize the recent progress of this field,focusing on the following three aspects.First,we briefly introduced the signaling pathway of cGAS-STING activation,and its regulation effect on the antitumor immunity cycle has been discussed.Along with this,several agonists of the cGAS-STING pathway were introduced with their potential as immunotherapeutic drugs.Then,the basic biological functions of Mn^(2+)have been illustrated,focusing on its critical roles in the cGAS-STING pathway activation.Next,we systematically reviewed the Mn^(2+)-based strategies for tumor immunotherapy,which can be classified by the methods based on Mn^(2+)alone or Mn^(2+)combined with other therapeutic modalities.We finally speculated the future perspectives of the field and provided rational suggestions to develop better Mn^(2+)-based therapeutics.展开更多
Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effecti...Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effectively alleviating the bandwidth pressure during data transmission.However,CS has many shortcomings in the transmission of hyperspectral image(HSI)data.This work aims to consider the application of CS in the transmission of hyperspectral image(HSI)data,and provides a feasible research scheme for CS of HSI data.HSI has rich spectral information and spatial information in bands,which can reflect the physical properties of the target.Most of the hyperspectral image compressed sensing(HSICS)algorithms cannot effectively use the inter-band information of HSI,resulting in poor reconstruction effects.In this paper,A three-stage hyperspectral image compression sensing algorithm(Three-stages HSICS)is proposed to obtain intra-band and inter-band characteristics of HSI,which can improve the reconstruction accuracy of HSI.Here,we establish a multi-objective band selection(Mop-BS)model,amulti-hypothesis prediction(MHP)model and a residual sparse(ReWSR)model for HSI,and use a staged reconstruction method to restore the compressed HSI.The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS,and it performs best among all comparison algorithms.展开更多
In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total b...In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells.Furthermore,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput.The problem is formulated as a mixed-integer nonconvex optimization(MINCP)problem which is difficult to solve in general.The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively.For the spectrum allocation,we model it as a auction problem and a combinatorial auction approach is proposed to tackle it.In addition,the DC programming method is adopted to optimize the power allocation subproblem.To decrease the signaling and computational overhead,we propose a distributed algorithm based on the Lagrangian dual method.Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.展开更多
University timetabling problems are a yearly challenging task and are faced repeatedly each semester.The problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they...University timetabling problems are a yearly challenging task and are faced repeatedly each semester.The problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal timetable.Several techniques have been used to solve university timetabling problems,and most of them use optimization techniques.This paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling problems.The comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the evaluation.It is noted that meta-heuristic methodologies are widely used in the literature.Additionally,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling solutions.Finally,trends and future directions in university timetabling problems are provided.This paper provides good information for students,researchers,and specialists interested in this area of research.The challenges and possibilities for future research prospects are also explored.展开更多
The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA...The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.展开更多
In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the...In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.展开更多
Combinatorial enzyme technology was applied for the conversion of wheat insoluble arabinoxylan to oligosaccharide structural variants. The digestive products were fractionated by Bio-Gel P4 column and screened for bio...Combinatorial enzyme technology was applied for the conversion of wheat insoluble arabinoxylan to oligosaccharide structural variants. The digestive products were fractionated by Bio-Gel P4 column and screened for bioactivity. One fraction pool was observed to exhibit antimicrobial property resulting in the suppression of cell growth of the test organism ATCC 8739 E. coli. It has a MIC value of 1.5% (w/v, 35°C, 20 hr) and could be useful as a new source of prebiotics or preservatives. The present results further confirm the science and useful application of combinatorial enzyme approach.展开更多
Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over m...Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.展开更多
Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with low...Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distin- guishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore （MP） is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of per- oxisome proliferator receptor-alpha （PPARα） agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.展开更多
文摘The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicability and reliability of upward recursive formula in principle is amended.An optimal scheme of upward-and downward-joint recursions has been developed for the sequential F(z) computations.No additional accuracy is needed with the fundamental term of recursion because the absolute error of Fn(z) always decreases with the recursive approach.The scheme can be employed in modifying any of existent subprograms for Fn<z> computations.In the case of p-d-f-and g-type Gaussians,combining this method with Schaad's formulas can reduce,at least,the additive operations by a factor 40%;the multiplicative and exponential operations by a factor 60%.
基金This study is part of the results in HEIFE supported by the National Natural Science Foundation of China.
文摘A combinatory method of determining the turbulent fluxes in the surface layer has been developed and their general representations have been thus obtained.The universal functions of the (M-O) similarity in the surface layer can be de- termined by the method.The results calculated by using the ITCE's data indicate that the method is feasible.
基金supported by the National Key Research and Development Program of China (No.2022YFC2806102)the National Natural Science Foundation of China (No.52171287,52325107)+3 种基金High-tech Ship Research Project of Ministry of Industry and Information Technology (No.2023GXB01-05-004-03,No.GXBZH2022-293)the Science Foundation for Distinguished Young Scholars of Shandong Province (No.ZR2022JQ25)the Taishan Scholars Project (No.tsqn201909063)the Fundamental Research Funds for the Central Universities (No.24CX10006A)。
文摘The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively.
基金supported by the Open Project of Xiangjiang Laboratory (22XJ02003)Scientific Project of the National University of Defense Technology (NUDT)(ZK21-07, 23-ZZCX-JDZ-28)+1 种基金the National Science Fund for Outstanding Young Scholars (62122093)the National Natural Science Foundation of China (72071205)。
文摘Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金the Beijing Natural Science Foundation(Grant No.2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212).
文摘To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。
基金Supported by Zhoukou Normal University High-Level Talents Start-Up Funds Research Project(Grant No.ZKNUC2022007)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX240725).
文摘In this paper,we firstly establish a combinatorial identity with a free parameter x,and then by means of derivative operation,several summation formulae concerning classical and generalized harmonic numbers,as well as binomial coefficients are derived.
文摘In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.
基金Project supported by the National High Technique Planning Foundation
文摘I. INTRODUCTION The exploration for a unified basis of the combinatory logic and the predicate calculus will promote laying a strict and thorough mathematical foundation of the programming language possessing itself of the functional and logic paradigms. The purpose of this note, proceeding from the algebraic oersoective, is to formulize the first-order mathematical
基金a post-doctor grant of the Chinese Academy of Sciences.
文摘In the light of a question of J. L. Krivine about the consistency of an extensional λ-theory,an extensional combinatory logic ECL+U(G)+RU_∞+ is established, with its consistency model provedtheoretically and it is shown the it is not equivalent to any system of universal axioms. It is expressed bythe theory in first order logic that, for every given group G of order n, there simultaneously exist infinitelymany universal retractions and a surjective n-tuple notion, such that each element of G acts as a permutationof the components of the n-tuple, and as an Ap-automorphism of the model; further each of the universalretractions is invarian under the action of the Ap-automorphisms induced by G The difference between thetheory and that of Krivine is the G need not be a symmetric group.
基金supported by the Natural Science Foundation of Jilin Province(No.20220101017JC)National Natural Science Foundation of China(No.11675063)Key Laboratory of Nuclear Data Foundation(JCKY2020201C157).
文摘In this study,a microscopic method for calculating the nuclear level density(NLD)based on the covariant density functional theory(CDFT)is developed.The particle-hole state density is calculated by a combinatorial method using single-particle level schemes obtained from the CDFT,and the level densities are then obtained by considering collective effects such as vibration and rotation.Our results are compared with those of other NLD models,including phenomenological,microstatisti-cal and nonrelativistic Hartree–Fock–Bogoliubov combinatorial models.This comparison suggests that the general trends among these models are essentially the same,except for some deviations among the different NLD models.In addition,the NLDs obtained using the CDFT combinatorial method with normalization are compared with experimental data,including the observed cumulative number of levels at low excitation energies and the measured NLDs.The CDFT combinatorial method yields results that are in reasonable agreement with the existing experimental data.
基金National Natural Science Foundation of China(No.U1903125,82073799)Natural Science Foundation of Hunan province in China(No.2021JJ20084)the Science and Technology Innovation Program of Hunan Province(No.2021RC3020)。
文摘Immunotherapy has efficiently revolutionized the treatment of human neoplastic diseases.However,the overall responsive rate of current immunotherapy is still unsatisfactory,benefiting only a small proportion of patients.Therefore,significant attention has been paid to the modulation of tumor microenvironment(TME)for the enhancement of immunotherapy.Interestingly,recent studies have shown that cyclic GMP-AMP synthasestimulator of interferon gene(cGAS-STING)was initially found as an innate immune sensor to recognize cytoplasmic DNA(such as bacterial,viral,micronuclei,and mitochondrial).It is a promising signaling pathway to activate antitumor immune responses via type I interferon production.Notably,Mn^(2+)was found to be a critical molecule to sensitize the activation of the cGAS-STING pathway for better immunotherapy.This activation led to the development of Mn^(2+)-based strategies for tumor immunotherapy via the activation of the cGAS-STING pathway.In this critical review,we aimed to summarize the recent progress of this field,focusing on the following three aspects.First,we briefly introduced the signaling pathway of cGAS-STING activation,and its regulation effect on the antitumor immunity cycle has been discussed.Along with this,several agonists of the cGAS-STING pathway were introduced with their potential as immunotherapeutic drugs.Then,the basic biological functions of Mn^(2+)have been illustrated,focusing on its critical roles in the cGAS-STING pathway activation.Next,we systematically reviewed the Mn^(2+)-based strategies for tumor immunotherapy,which can be classified by the methods based on Mn^(2+)alone or Mn^(2+)combined with other therapeutic modalities.We finally speculated the future perspectives of the field and provided rational suggestions to develop better Mn^(2+)-based therapeutics.
基金supported by the National Natural Science Foundation of China under Grant No.61806138Key R&D program of Shanxi Province(High Technology)under Grant No.201903D121119Science and Technology Development Foundation of the Central Guiding Local under Grant No.YDZJSX2021A038.
文摘Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effectively alleviating the bandwidth pressure during data transmission.However,CS has many shortcomings in the transmission of hyperspectral image(HSI)data.This work aims to consider the application of CS in the transmission of hyperspectral image(HSI)data,and provides a feasible research scheme for CS of HSI data.HSI has rich spectral information and spatial information in bands,which can reflect the physical properties of the target.Most of the hyperspectral image compressed sensing(HSICS)algorithms cannot effectively use the inter-band information of HSI,resulting in poor reconstruction effects.In this paper,A three-stage hyperspectral image compression sensing algorithm(Three-stages HSICS)is proposed to obtain intra-band and inter-band characteristics of HSI,which can improve the reconstruction accuracy of HSI.Here,we establish a multi-objective band selection(Mop-BS)model,amulti-hypothesis prediction(MHP)model and a residual sparse(ReWSR)model for HSI,and use a staged reconstruction method to restore the compressed HSI.The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS,and it performs best among all comparison algorithms.
基金supported in part by the Guangxi Natural Science Foundation under Grant 2021GXNSFBA196076in part by the General Project of Guangxi Natural Science Foundation Project(Guangdong-Guangxi Joint Fund Project)under Grant 2021GXNSFAA075031+1 种基金in part by the basic ability improvement project of young and middle-aged teachers in Guangxi Universities under Grant 2022KY0579in part by the Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology under Grant DH202007.
文摘In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells.Furthermore,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput.The problem is formulated as a mixed-integer nonconvex optimization(MINCP)problem which is difficult to solve in general.The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively.For the spectrum allocation,we model it as a auction problem and a combinatorial auction approach is proposed to tackle it.In addition,the DC programming method is adopted to optimize the power allocation subproblem.To decrease the signaling and computational overhead,we propose a distributed algorithm based on the Lagrangian dual method.Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.
基金This research work was supported by the University Malaysia Sabah,Malaysia.
文摘University timetabling problems are a yearly challenging task and are faced repeatedly each semester.The problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal timetable.Several techniques have been used to solve university timetabling problems,and most of them use optimization techniques.This paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling problems.The comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the evaluation.It is noted that meta-heuristic methodologies are widely used in the literature.Additionally,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling solutions.Finally,trends and future directions in university timetabling problems are provided.This paper provides good information for students,researchers,and specialists interested in this area of research.The challenges and possibilities for future research prospects are also explored.
基金NSFC http://www.nsfc.gov.cn/for the support through Grants No.61877045Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants No.JCYJ2016042815-3956266.
文摘The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.
文摘In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.
文摘Combinatorial enzyme technology was applied for the conversion of wheat insoluble arabinoxylan to oligosaccharide structural variants. The digestive products were fractionated by Bio-Gel P4 column and screened for bioactivity. One fraction pool was observed to exhibit antimicrobial property resulting in the suppression of cell growth of the test organism ATCC 8739 E. coli. It has a MIC value of 1.5% (w/v, 35°C, 20 hr) and could be useful as a new source of prebiotics or preservatives. The present results further confirm the science and useful application of combinatorial enzyme approach.
文摘Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.
文摘Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distin- guishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore （MP） is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of per- oxisome proliferator receptor-alpha （PPARα） agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.
