This paper takes the synthesizing evaluation about industrial economic benefits by examples and proposes a new method named maximizing deviation method for multiindices decision. The new method can automatically deter...This paper takes the synthesizing evaluation about industrial economic benefits by examples and proposes a new method named maximizing deviation method for multiindices decision. The new method can automatically determine the weight coefficients among the multiindices and also can obtain the exact and reliable evaluation results without subjectivity.展开更多
Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment,the evaluation information of alternatives given by decision makers is often fuzzy and uncertain.As a generaliz...Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment,the evaluation information of alternatives given by decision makers is often fuzzy and uncertain.As a generalization of intuitionistic fuzzy set(IFSs)and Pythagoras fuzzy set(PFSs),q-rung orthopair fuzzy set(q-ROFS)is more suitable for expressing fuzzy and uncertain information.But,in actual multiple attribute decision making(MADM)problems,the weights of DMs and attributes are always completely unknown or partly known,to date,the maximizing deviation method is a good tool to deal with such issues.Thus,combine the q-ROFS and conventional maximizing deviation method,we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper.Firstly,we briefly introduce the basic concept of q-rung orthopair fuzzy sets(q-ROFSs)and q-rung interval-valued orthopair fuzzy sets(q-RIVOFSs).Then,combine the maximizing deviation method with q-rung orthopair fuzzy information,we establish two new decision making models.On this basis,the proposed models are applied to MADM problems with q-rung orthopair fuzzy information.Compared with existing methods,the effectiveness and superiority of the new model are analyzed.This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers(q-ROFNs)and whose attributes are incomplete.展开更多
Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the...Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the energy consumption problem and maximize the network lifetime, this paper proposes a Virtual Multiple Input Multiple Output based Cooperative Routing algorithm(VMIMOCR). VMIMOCR chooses cooperative relay nodes based on Virtual Multiple Input Multiple Output Model, and balances energy consumption by reasonable power allocation among transmitters, and decides the forwarding path finally. The experimental results show that VMIMOCR can improve network lifetime from 37% to 348% in the medium node density, compared with existing routing algorithms.展开更多
In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged withi...In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible,meanwhile without deteriorating imaging quality.For this end,we propose a novel methodology of arranging sensors in an optimal manner,exploring the concept of information capacity developed originally in the communication society.The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem,which can be resolved efficiently using widely adopted Simultaneous Perturbation and Statistical Algorithm(SPSA).Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.展开更多
A unique challenge in P2P network is that the peer dynamics (departure or failure) cause unavoidable disruption to the downstream peers. While many works have been dedicated to consider fault resilience in peer select...A unique challenge in P2P network is that the peer dynamics (departure or failure) cause unavoidable disruption to the downstream peers. While many works have been dedicated to consider fault resilience in peer selection, little understanding is achieved regarding the solvability and solution complexity of this problem from the optimization perspective. To this end, we propose an optimization framework based on the generalized flow theory. Key concepts introduced by this framework include resilience factor, resilience index, and generalized throughput, which collectively model the peer resilience in a probabilistic measure. Under this framework, we divide the domain of optimal peer selection along several dimensions including network topology, overlay organization, and the definition of resilience factor and generalized flow. Within each sub-problem, we focus on studying the problem complexity and finding optimal solutions. Simulation study is also performed to evaluate the effectiveness of our model and performance of the proposed algorithms.展开更多
The subdistrict office is the most grass-roots government organization, and is also the main unit providing services for the masses at the grass-roots level. In order to ensure the work efficiency of the subdistrict o...The subdistrict office is the most grass-roots government organization, and is also the main unit providing services for the masses at the grass-roots level. In order to ensure the work efficiency of the subdistrict office, it is necessary to strengthen the modernization of the subdistrict office. However, due to the influence of traditional management concepts for a long time, the street offices in many areas of our country still adopt traditional management methods when carrying out human resources management, which is difficult to motivate the staff. If this situation cannot be changed in time, it will affect the service efficiency of the street offices and the grassroots will be full of complaints, which will not only affect the image of the national government agencies, but also affect the stability of the street residents.展开更多
Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most exi...Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.展开更多
In this work,we focus on maximizing the ratio of two monotone DR-submodular functions on the integer lattice.It is neither submodular nor supermodular.We prove that the Threshold Decrease Algorithm is a 1-e^(1-kg)-εa...In this work,we focus on maximizing the ratio of two monotone DR-submodular functions on the integer lattice.It is neither submodular nor supermodular.We prove that the Threshold Decrease Algorithm is a 1-e^(1-kg)-εapproximation ratio algorithm.Additionally,we construct the relationship between maximizing the ratio of two monotone DR-submodular functions and maximizing the difference of two monotone DR-submodular functions on the integer lattice.Based on this relationship,we combine the dichotomy technique and Threshold Decrease Algorithm to solve maximizing the difference of two monotone DR-submodular functions on the integer lattice and prove its approximation ratio is f(x)-g(x)≥1-e^(1-kg)f(X^(*))-g(X^(*)).To the best of our knowledge,before us,there was no work to focus on maximizing the ratio of two monotone DR-submodular functions on integer lattice by using the Threshold Decrease Algorithm.展开更多
We discuss a variant of the multi-task n-vehicle exploration problem. Instead of requiring an optimal permutation of vehicles in every group, the new problem requires all vehicles in a group to arrive at the same dest...We discuss a variant of the multi-task n-vehicle exploration problem. Instead of requiring an optimal permutation of vehicles in every group, the new problem requires all vehicles in a group to arrive at the same destination. Given n tasks with assigned consume-time and profit, it may also be viewed as a maximization of every processor＇s average profit. Further, we propose a new kind of partition problem in fractional form and analyze its computational complexity. By regarding fractional partition as a special case, we prove that the average profit maximization problem is NP-hard when the number of processors is fixed and it is strongly NP- hard in general. At last, a pseudo-polynomial time algorithm for the average profit maximization problem and the fractional partition problem is presented, using the idea of the pseudo-polynomial time algorithm for the classical partition problem.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divide...Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.展开更多
The advantages and possible limitations of using video in ELT are examined, and some suggestions for maximizing its value are put forward. 1. Introduction SINCE the 1980s, there have been significant developments in t...The advantages and possible limitations of using video in ELT are examined, and some suggestions for maximizing its value are put forward. 1. Introduction SINCE the 1980s, there have been significant developments in the field of English language teaching ELT) in China, the most remarkable of which is the integration of educational technology into ELT.展开更多
Radio frequency identification (RFID) is one of today s most anticipated technologies for a broad range of enterprises. Based on the promise of lower operating costs combined with more accurate product and asset infor...Radio frequency identification (RFID) is one of today s most anticipated technologies for a broad range of enterprises. Based on the promise of lower operating costs combined with more accurate product and asset information, organizations .Rfrom manufacturers to government agencies, retailers to healthcare providers , Rare introducing RFID technologies in the supply chain, for asset tracking and management, and for security and regulatory purposes.展开更多
Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters ...Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via sociM links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected： how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user＇s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.展开更多
In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k ini...In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated groups.The GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider groups.Focusing on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes remain.Although the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of groups.Moreover,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM.Finally,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.展开更多
In this paper, we characterize the trees with the largest Laplacian and adjacency spectral radii among all trees with fixed number of vertices and fixed maximal degree, respectively.
Submodular optimization is widely used in large datasets.In order to speed up the problems solving,it is essential to design low-adaptive algorithms to achieve acceleration in parallel.In general,the function values a...Submodular optimization is widely used in large datasets.In order to speed up the problems solving,it is essential to design low-adaptive algorithms to achieve acceleration in parallel.In general,the function values are given by a value oracle,but in practice,the oracle queries may consume a lot of time.Hence,how to strike a balance between optimizing them is important.In this paper,we focus on maximizing a normalized and strictly monotone set function with the diminishing-return ratio under a cardinality constraint,and propose two algorithms to deal with it.We apply the adaptive sequencing technique to devise the first algorithm,whose approximation ratio is arbitrarily close to 1-e^(-γ)in O(logn·log(log k/γ)) adaptive rounds,and requires O(logn^(2)·log(log k/γ)) queries.Then by adding preprocessing and parameter estimation steps to the first algorithm,we get the second one.The second algorithm trades a small sacrifice in adaptive complexity for a significant improvement in query complexity.With the same approximation and adaptive complexity,the query complexity is improved to.To the best of our knowledge,this is the first paper of designing adaptive algorithms for maximizing a monotone function using the diminishing-return ratio.展开更多
In this paper,we investigate the maximization of the differences between a nonnegative monotone diminishing return submodular(DR-submodular)function and a nonnegative linear function on the integer lattice.As it is al...In this paper,we investigate the maximization of the differences between a nonnegative monotone diminishing return submodular(DR-submodular)function and a nonnegative linear function on the integer lattice.As it is almost unapproximable for maximizing a submodular function without the condition of nonnegative,we provide weak(bifactor)approximation algorithms for this problem in two online settings,respectively.For the unconstrained online model,we combine the ideas of single-threshold greedy,binary search and function scaling to give an efficient algorithm with a 1/2 weak approximation ratio.For the online streaming model subject to a cardinality constraint,we provide a one-pass(3-√5)/2 weak approximation ratio streaming algorithm.Its memory complexity is(k log k/ε),and the update time for per element is(log^(2)k/ε).展开更多
Motivation.As artificial intelligence(AI)workloads escalate exponentially,ultra-thin,high-efficiency voltage regulator modules(VRMs)with exceptional power density become essential for backside-mounted configurations[1...Motivation.As artificial intelligence(AI)workloads escalate exponentially,ultra-thin,high-efficiency voltage regulator modules(VRMs)with exceptional power density become essential for backside-mounted configurations[1].Thus,highdensity multiphase DC−DC converters are pivotal for implementing vertical power delivery(VPD)architectures in XPU platforms.Strategically positioning these converters beneath processors and maximizing spatial utilization enables core rail currents exceeding 2 kA while significantly reducing the power distribution network(PDN)losses compared to conventional solutions.The VPD configuration elevates system-level energy efficiency with>100 W power saving per processor,yielding megawatt-scale savings in a datacenter that uses~100000 processors.The synergy of 48 V power conversion architectures and advanced packaging techniques enables the industry’s commitment to balancing computational demands with CO_(2)emission reduction and environmental sustainability.展开更多
文摘This paper takes the synthesizing evaluation about industrial economic benefits by examples and proposes a new method named maximizing deviation method for multiindices decision. The new method can automatically determine the weight coefficients among the multiindices and also can obtain the exact and reliable evaluation results without subjectivity.
基金supported by the National Natural Science Foundation of China under Grant No.71571128the Humanities and Social Sciences Foundation of Ministry of Education of the People’s Republic of China(No.17XJA630003).
文摘Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment,the evaluation information of alternatives given by decision makers is often fuzzy and uncertain.As a generalization of intuitionistic fuzzy set(IFSs)and Pythagoras fuzzy set(PFSs),q-rung orthopair fuzzy set(q-ROFS)is more suitable for expressing fuzzy and uncertain information.But,in actual multiple attribute decision making(MADM)problems,the weights of DMs and attributes are always completely unknown or partly known,to date,the maximizing deviation method is a good tool to deal with such issues.Thus,combine the q-ROFS and conventional maximizing deviation method,we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper.Firstly,we briefly introduce the basic concept of q-rung orthopair fuzzy sets(q-ROFSs)and q-rung interval-valued orthopair fuzzy sets(q-RIVOFSs).Then,combine the maximizing deviation method with q-rung orthopair fuzzy information,we establish two new decision making models.On this basis,the proposed models are applied to MADM problems with q-rung orthopair fuzzy information.Compared with existing methods,the effectiveness and superiority of the new model are analyzed.This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers(q-ROFNs)and whose attributes are incomplete.
基金supported by the National Basic Research Program of China (973 program) (Grant No.2012CB315805)the National Natural Science Foundation of China (Grant No.61472130 and 61572184)
文摘Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the energy consumption problem and maximize the network lifetime, this paper proposes a Virtual Multiple Input Multiple Output based Cooperative Routing algorithm(VMIMOCR). VMIMOCR chooses cooperative relay nodes based on Virtual Multiple Input Multiple Output Model, and balances energy consumption by reasonable power allocation among transmitters, and decides the forwarding path finally. The experimental results show that VMIMOCR can improve network lifetime from 37% to 348% in the medium node density, compared with existing routing algorithms.
文摘In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible,meanwhile without deteriorating imaging quality.For this end,we propose a novel methodology of arranging sensors in an optimal manner,exploring the concept of information capacity developed originally in the communication society.The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem,which can be resolved efficiently using widely adopted Simultaneous Perturbation and Statistical Algorithm(SPSA).Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.
文摘A unique challenge in P2P network is that the peer dynamics (departure or failure) cause unavoidable disruption to the downstream peers. While many works have been dedicated to consider fault resilience in peer selection, little understanding is achieved regarding the solvability and solution complexity of this problem from the optimization perspective. To this end, we propose an optimization framework based on the generalized flow theory. Key concepts introduced by this framework include resilience factor, resilience index, and generalized throughput, which collectively model the peer resilience in a probabilistic measure. Under this framework, we divide the domain of optimal peer selection along several dimensions including network topology, overlay organization, and the definition of resilience factor and generalized flow. Within each sub-problem, we focus on studying the problem complexity and finding optimal solutions. Simulation study is also performed to evaluate the effectiveness of our model and performance of the proposed algorithms.
文摘The subdistrict office is the most grass-roots government organization, and is also the main unit providing services for the masses at the grass-roots level. In order to ensure the work efficiency of the subdistrict office, it is necessary to strengthen the modernization of the subdistrict office. However, due to the influence of traditional management concepts for a long time, the street offices in many areas of our country still adopt traditional management methods when carrying out human resources management, which is difficult to motivate the staff. If this situation cannot be changed in time, it will affect the service efficiency of the street offices and the grassroots will be full of complaints, which will not only affect the image of the national government agencies, but also affect the stability of the street residents.
基金supported by the Fundamental Research Funds for the Universities of Heilongjiang(Nos.145109217,135509234)the Youth Science and Technology Innovation Personnel Training Project of Heilongjiang(No.UNPYSCT-2020072)the Innovative Research Projects for Postgraduates of Qiqihar University(No.YJSCX2022048).
文摘Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.
基金upported by the China Scholarship Council(No.202004910755)supported by the National Natural Science Foundation of China(Nos.12071459 and 11991022)+2 种基金the Fundamental Research Funds for Central Universities(No.E1E40107X2)supported by the Natural Sciences and Engineering Research Council of Canada(No.283106)the National Natural Science Foundation of China(Nos.11771386 and 11728104)。
文摘In this work,we focus on maximizing the ratio of two monotone DR-submodular functions on the integer lattice.It is neither submodular nor supermodular.We prove that the Threshold Decrease Algorithm is a 1-e^(1-kg)-εapproximation ratio algorithm.Additionally,we construct the relationship between maximizing the ratio of two monotone DR-submodular functions and maximizing the difference of two monotone DR-submodular functions on the integer lattice.Based on this relationship,we combine the dichotomy technique and Threshold Decrease Algorithm to solve maximizing the difference of two monotone DR-submodular functions on the integer lattice and prove its approximation ratio is f(x)-g(x)≥1-e^(1-kg)f(X^(*))-g(X^(*)).To the best of our knowledge,before us,there was no work to focus on maximizing the ratio of two monotone DR-submodular functions on integer lattice by using the Threshold Decrease Algorithm.
基金Supported by Daqing oilfield company Project of PetroCHINA under Grant (dqc-2010-xdgl-ky-002)Key Laboratory of Management, Decision and Information Systems, Chinese Academy of SciencesBeijing Research Center of Urban System Engineering
文摘We discuss a variant of the multi-task n-vehicle exploration problem. Instead of requiring an optimal permutation of vehicles in every group, the new problem requires all vehicles in a group to arrive at the same destination. Given n tasks with assigned consume-time and profit, it may also be viewed as a maximization of every processor＇s average profit. Further, we propose a new kind of partition problem in fractional form and analyze its computational complexity. By regarding fractional partition as a special case, we prove that the average profit maximization problem is NP-hard when the number of processors is fixed and it is strongly NP- hard in general. At last, a pseudo-polynomial time algorithm for the average profit maximization problem and the fractional partition problem is presented, using the idea of the pseudo-polynomial time algorithm for the classical partition problem.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China(Grant No.12071092)Guangdong Basic and Applied Basic Research Foundation(Grant No.2025A1515012072)+1 种基金the Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH051298)the Scientific Research Foundation of Bozhou University(Grant No.BYKQ202419).
文摘Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.
文摘The advantages and possible limitations of using video in ELT are examined, and some suggestions for maximizing its value are put forward. 1. Introduction SINCE the 1980s, there have been significant developments in the field of English language teaching ELT) in China, the most remarkable of which is the integration of educational technology into ELT.
文摘Radio frequency identification (RFID) is one of today s most anticipated technologies for a broad range of enterprises. Based on the promise of lower operating costs combined with more accurate product and asset information, organizations .Rfrom manufacturers to government agencies, retailers to healthcare providers , Rare introducing RFID technologies in the supply chain, for asset tracking and management, and for security and regulatory purposes.
基金This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61373128, 61321491, 61472181, 91218302, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151392, Jiangsu Key Technique Project （Industry） under Grant No. BE2013116, EU FP7 IRSES MobileCloud Project under Grant No. 612212, the Program B for Outstanding Ph.D. Candidate of Nanjing University, and the Collaborative Innovation Center of Novel Software Technology and Industrialization of Jiangsu Province of China.
文摘Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via sociM links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected： how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user＇s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.
基金supported by the Natural Science Foundation of Fujian Province (No. 2020J01845)the Educational Research Project for Young and MiddleAged Teachers of Fujian Provincial Department of Education (No. JAT190613)+1 种基金the National Natural Science Foundation of China (Nos. 61772005 and 92067108)the Outstanding Youth Innovation Team Project for Universities of Shandong Province (No. 2020KJN008)。
文摘In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated groups.The GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider groups.Focusing on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes remain.Although the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of groups.Moreover,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM.Finally,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.
基金Foundation item： the National Natural Science Foundation of China （No. 10601001） the Natural Science Foundation of Anhui Province （Nos. 050460102+3 种基金 070412065） Natural Science Foundation of Department of Education of Anhui Province （No. 2005kj005zd） Project of Anhui University on leading Researchers Construction Foundation of Innovation Team on Basic Mathematics of Anhui University.
文摘In this paper, we characterize the trees with the largest Laplacian and adjacency spectral radii among all trees with fixed number of vertices and fixed maximal degree, respectively.
基金the National Natural Science Foundation of China(Nos.11971447 and 11871442)the Fundamental Research Funds for the Central Universities.
文摘Submodular optimization is widely used in large datasets.In order to speed up the problems solving,it is essential to design low-adaptive algorithms to achieve acceleration in parallel.In general,the function values are given by a value oracle,but in practice,the oracle queries may consume a lot of time.Hence,how to strike a balance between optimizing them is important.In this paper,we focus on maximizing a normalized and strictly monotone set function with the diminishing-return ratio under a cardinality constraint,and propose two algorithms to deal with it.We apply the adaptive sequencing technique to devise the first algorithm,whose approximation ratio is arbitrarily close to 1-e^(-γ)in O(logn·log(log k/γ)) adaptive rounds,and requires O(logn^(2)·log(log k/γ)) queries.Then by adding preprocessing and parameter estimation steps to the first algorithm,we get the second one.The second algorithm trades a small sacrifice in adaptive complexity for a significant improvement in query complexity.With the same approximation and adaptive complexity,the query complexity is improved to.To the best of our knowledge,this is the first paper of designing adaptive algorithms for maximizing a monotone function using the diminishing-return ratio.
基金supported by the National Natural Science Foundation of China(Nos.12001025 and 12131003)The second author is supported by the Natural Sciences and Engineering Research Council(No.06446),and the National Natural Science Foundation of China(Nos.11771386 and 11728104)+2 种基金The third author is supported by the National Natural Science Foundation of China(Nos.11501171 and 11771251)the Province Natural Science Foundation of Shandong(No.ZR2020MA028)The fourth author is supported by the National Natural Science Foundation of China(No.11701150)。
文摘In this paper,we investigate the maximization of the differences between a nonnegative monotone diminishing return submodular(DR-submodular)function and a nonnegative linear function on the integer lattice.As it is almost unapproximable for maximizing a submodular function without the condition of nonnegative,we provide weak(bifactor)approximation algorithms for this problem in two online settings,respectively.For the unconstrained online model,we combine the ideas of single-threshold greedy,binary search and function scaling to give an efficient algorithm with a 1/2 weak approximation ratio.For the online streaming model subject to a cardinality constraint,we provide a one-pass(3-√5)/2 weak approximation ratio streaming algorithm.Its memory complexity is(k log k/ε),and the update time for per element is(log^(2)k/ε).
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