OPTIMAL SCHEME FOR SEQUENTIAL COMPUTATIONS OF F_m(z) INTEGRALS IN AB INITIO CALCULATIONS----COMBINATORY USE OF UPWARD AND DOWNWARD RECURSIONS

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%.

THE COMBINATORY METHOD FOR DETERMINATION OF THE TURBULENT FLUXES AND UNIVERSAL FUNCTIONS IN THE SURFACE LAYER

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.

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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.

Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

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.

COMBINATORY LOGIC AS THE FIRST-ORDER MATHEMATICAL THEORY

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

Consistency and axiomatization of a natural extensional combinatory logic

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.

Mn-based cGAS-STING activation for tumor therapy

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.

Three-Stages Hyperspectral Image Compression Sensing with Band Selection

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.

Low Complexity Joint Spectrum Resource and Power Allocation for Ultra Dense Networks

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.

Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

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.

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

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.

Calculation of microscopic nuclear level densities based on covariant density functional theory

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.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

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.

Combinatorial Enzyme Approach to Convert Wheat Insoluble Arabinoxylan to Bioactive Oligosaccharides

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.

Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies

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.

Estrogen Receptor α and β Expressions in Hypothalamus-pituitary-ovary Axis in Rats Exposed Lactationally to Soy Isoflavones and Bisphenol A

Objectives This paper aims to investigate the uterotrophic activities of lactational exposure to combination of soy isoflavones （SIF） and bisphenol A （BPA） and to examine estrogen receptor α （ERα） and estrogen receptor β （ERβ） expressions in hypothalamus-pituitary-ovary axis and uterus.Methods Maternal rats that were breeding about 8 litters were randomly divided into four groups with seven dams in each group.Dams in different treatment groups received corn oil （control）,150 mg/kg BW of SIF,150 mg/kg BW of BPA or combination of 150 mg/kg BW of SIF and 150 mg/kg BW of BPA,respectively,from postnatal day 5 to 11 （PND5-11） by gavage.On PND12 and PND70,10 female litters were killed and hypothalamus,pituitary,ovary and uterus were collected.ERα and ERβ expressions in these organs were detected with Western blotting assay.And vaginal opening time and estrus cycle were examined in animals fed for PND70.Results On PND12,the relative uterine weight of rats treated with ISF or BPA or their combination was significantly higher than that of untreated rats （P〈0.05）.But the relative uterine weight of rats in the co-exposure group was slightly lower than that in the group only exposed to SIF or BPA.On PND 70,however,the relative uterine weight in each treatment group was not statistically different from that in the control group （P〈0.05）.Vaginal opening time and estrus cycle in groups treated with SIF or BPA or their combination were similar to those in the control group （P〈0.05）.Exposure to SIF or BPA or their combination could up-regulate or down-regulate ERα and ERβ expressions in hypothalamus,pituitary,ovary and uterus on PND12 and PND70.These regulation patterns for ERα and ERβ were different in different organs at different time points.Conclusion Lactational exposure to ISF or BPA or their combination could induce uterotrophic responses in neonate rats,which disappeared in later life.But these data fail to suggest a possibility for synergic actions between SIF and BPA.It was also demonstrated that the uterotrophic effects of SIF and BPA exposure might,at least,involve modification of ERα or ERβ expressions in the hypothalamus-pituitary-ovary axis.

BER Performance of Soft Decision Modulations Based on PCSS System

Recently,soft decision modulations become the highlight of parallel combinatory spread spectrum ( PCSS) system. Existing soft decision BPSK and APK modulations are given and compared in the thesis. In order to apply soft decision QPSK modulation based on PCSS system,the correlation of superposition PN sequences is discussed. A weighted summation algorithm is adopted in QPSK demodulation to recover the whole orthogonal correlation of the superposition sequences; meanwhile the bit error rate of weighting soft decision QPSK modulation is simulated. The simulation results show that the bit error rate performance of proposed soft decision QPSK modulation based on PCSS system is better than that of hard decision modulation. The method proposed can be widely adopted in engineering application.

Gated recurrent unit model for a sequence tagging problem

Combinatory categorial grammer(CCG) supertagging is an important subtask that takes place before full parsing and can benefit many natural language processing(NLP) tasks like question answering and machine translation. CCG supertagging can be regarded as a sequence labeling problem that remains a challenging problem where each word is assigned to a CCG lexical category and the number of the probably associated CCG supertags to each word is large. To address this, recently recurrent neural networks(RNNs), as extremely powerful sequential models, have been proposed for CCG supertagging and achieved good performances. In this paper, a variant of recurrent networks is proposed whose design makes it much easier to train and memorize information for long range dependencies based on gated recurrent units(GRUs), which have been recently introduced on some but not all tasks. Results of the experiments revealed the effectiveness of the proposed method on the CCGBank datasets and show that the model has comparable accuracy with the previously proposed models for CCG supertagging.

U-Type Designs via New Generalized Partially Balanced Incomplete Block Designs with <i>m</i>= 4, 5 and 7 Associated Classes

The traditional combinatorial designs can be used as basic designs for constructing designs of computer experiments which have been used successfully till now in various domains such as engineering, pharmaceutical industry, etc. In this paper, a new series of generalized partially balanced incomplete blocks PBIB designs with m associated classes (m = 4, 5 and 7) based on new generalized association schemes with number of treatments v arranged in w arrays of n rows and l columns (w ≥ 2, n ≥ 2, l ≥ 2) is defined. Some construction methods of these new PBIB are given and their parameters are specified using the Combinatory Method (s). For n or l even and s divisor of n or l, the obtained PBIB designs are resolvable PBIB designs. So the Fang RBIBD method is applied to obtain a series of particular U-type designs U (wnl;) (r is the repetition number of each treatment in our resolvable PBIB design).

基于CombBLAS的同辈压力图聚类并行算法的设计与实现

图聚类是指把图中相对连接紧密的顶点及其相关的边分组形成一个子图的过程,在包括机器学习、数据挖掘、模式识别、图像分析及生物信息等领域有着广泛应用。但是,随着大数据时代的到来,图数据海量增长。面对广泛的大规模图计算需求,由于图结构本身的不规则性,单机算法运行效率低下,用传统的并行计算方法进行图计算难以获得高性能。使用线性代数的方法在Combinatorial BLAS上实现了同辈压力(Peer Pressure)图聚类的分布式算法,首先将该图聚类的算法转换为对稀疏矩阵的运算,从而结构化表示图的不规则数据结构及接入模式,然后基于MPI编程模型将其并行实现。实验结果表明,在并行处理规模达到43亿的由稀疏矩阵表示的超大规模图时,基于线性代数表示的同辈压力图聚类算法在曙光超级计算机上取得了较高的并行性能及良好的可扩展性,在64个核上获得了40.1的并行加速。