Efficient data structures for backtrack search SAT solvers

The implementation of efficient Propositional Satisfiability (SAT) solvers entails the utilization of highly efficient data structures, as illustrated by most of the recent state-of-the-art SAT solvers. However, it is in general hard to compare existing data structures, since different solvers are often characterized by fairly different algorithmic organizations and techniques, and by different search strategies and heuristics. This paper aims the evaluation of data structures for backtrack search SAT solvers, under a common unbiased SAT framework. In addition, advantages and drawbacks of each existing data structure are identified. Finally, new data structures are proposed, that are competitive with the most efficient data structures currently available, and that may be preferable for the next generation SAT solvers.     

异质信息网络的复杂条件社区搜索

社区是信息网络的重要属性, 社区搜索旨在寻找满足用户给定条件的节点集合, 是信息网络分析的重要研究内容. 异质信息网络由于包含更加全面、丰富的结构和语义信息, 所以异质信息网络的社区搜索近年来受到人们的广泛关注. 针对现有异质信息网络的社区搜索方法难以满足复杂条件社区搜索要求的不足, 定义了复杂条件社区搜索问题, 提出了考虑非对称元路径、受限元路径和禁止节点约束的搜索算法. 3种算法分别通过元路径补全策略、调整带标签的批量搜索策略和拆分复杂搜索条件的方式搜索社区, 同时针对禁止节点约束的搜索算法设计了基于剪枝策略和近似策略的优化算法以提高搜索效率. 在真实数据集上进行了大量实验, 实验结果证明了所提算法的有效性和高效性.     

毫米波微波网络基于匹配算法的小区关联方法

由于毫米波网络具有间歇性及较高的路径损耗,对于毫米波与微波基站联合组网系统,传统的小区关联方法会导致严重的负载失衡问题.为此,提出一种分布式双连接小区关联算法.该算法基于效用函数,采用匹配算法求解系统效用最大化下的最优关联,并保证产生帕累托最优和双向稳定解.同时为基站设置最小配额约束,以平衡毫米波与微波基站负载.此外,...     

非对称信息下供应链库存系统Pareto优化模型

运用委托代理理论,研究了非对称信患条件下,由单供应商单分销商组成的供应链Pareto优化问题.在假定分销商所面临的需求是与销售价格有关的随机变量,以及供、销双方关于分销商销售价格信息不对称这两个前提下,将供应商作为委托人,分销商作为代理人,给出了供应商为吸引销售商选择对自己最有利的销售价格而设计的最优激励合同,比较了在不同信息条件下供销双方的最优决策.最后,给出了应用实例和灵敏度分析.     

A Classification and Survey of Preference Handling Approaches in Nonmonotonic Reasoning

In recent years, there has been a large amount of disparate work concerning the representation and reasoning with qualitative preferential information by means of approaches to nonmonotonic reasoning. Given the variety of underlying systems, assumptions, motivations, and intuitions, it is difficult to compare or relate one approach with another. Here, we present an overview and classification for approaches to dealing with preference. A set of criteria for classifying approaches is given, followed by a set of desiderata that an approach might be expected to satisfy. A comprehensive set of approaches is subsequently given and classified with respect to these sets of underlying principles.     

Hybrid strategy for selecting compact set of clustering partitions

The selection of the most appropriate clustering algorithm is not a straightforward task, given that there is no clustering algorithm capable of determining the actual groups present in any dataset. A potential solution is to use different clustering algorithms to produce a set of partitions (solutions) and then select the best partition produced according to a specified validation measure; these measures are generally biased toward one or more clustering algorithms. Nevertheless, in several real cases, it is important to have more than one solution as the output. To address these problems, we present a hybrid partition selection algorithm, HSS, which accepts as input a set of base partitions potentially generated from clustering algorithms with different biases and aims, to return a reduced and yet diverse set of partitions (solutions). HSS comprises three steps: (i) the application of a multiobjective algorithm to a set of base partitions to generate a Pareto Front (PF) approximation; (ii) the division of the solutions from the PF approximation into a certain number of regions; and (iii) the selection of a solution per region by applying the Adjusted Rand Index. We compare the results of our algorithm with those of another selection strategy, ASA. Furthermore, we test HSS as a post-processing tool for two clustering algorithms based on multiobjective evolutionary computing: MOCK and MOCLE. The experiments revealed the effectiveness of HSS in selecting a reduced number of partitions while maintaining their quality.     

禁忌搜索解决烘干窑调度问题

针对烘干窑生产周期长、成本高的问题,为提高其生产效率,缩短生产周期,对烘干窑的生产调度进行了优化,描述了一个现实中存在的烘干窑调度问题,提出了一种改进的融入了回溯思想的禁忌搜索算法,并给出了算法优化前后的生产计划比较。实际应用结果显示,该算法能有效地缩短生产周期,减少生产成本,提高设备利用率和合同准时率。     

公平偏好下的虚拟企业道德风险及激励机制研究

虚拟企业要达到预期目标,盟主必须制定有效机制成功规避运行中的道德风险。 针对盟主和盟员之间存在委托代理关系的虚拟企业,考虑盟员的公平偏好心理,建立了公平 偏好下的虚拟企业委托代理契约模型,分析了盟员公平偏好心理和道德风险问题之间的关系, 对比了传统委托代理模型和公平偏好下的委托代理模型。结果表明公平偏好因素改变了传统 委托代理模型的部分结论,盟员的公平偏好强度对努力水平、收益分享具有重要影响,盟主应 选择公平偏好强度较低的盟员作为合作对象以规避道德风险。     

融合时间特性和用户偏好的卷积序列化推荐

推荐系统如今已被广泛应用于生活中,大大便利了人们的生活.传统的推荐方法主要是针对用户与物品的交互情况进行分析,分析用户与物品的历史记录,得到的只是用户过去对于物品的喜好程度.序列化推荐系统通过分析用户近一段时间与物品交互的序列,来考虑用户前后行为的关联性,能够获得用户短期内对物品的喜好程度.然而,序列化方法强调的是用户...     

一种高效的多目标演化算法

为了提高非劣解向Pareto最优前沿收敛的速度及进一步提高解的精度,在设计了一种新的杂交算子并改进了NSGA-Ⅱ的拥挤操作的基础上,提出了一种基于分级策略的多目标演化算法。数值实验表明,新算法能够非常高效地处理高维的最优前沿为凸的、非凸的和不连续前沿的多目标测试函数,得到的非劣解具有很好的分布性质。但在处理高维的具有太多局部最优前沿的多峰函数时极易陷入局部最优前沿。     

Downscaling near-surface atmospheric fields with multi-objective Genetic Programming

We present a new Genetic Programming based method to derive downscaling rules (i.e., functions or short programs) generating realistic high-resolution fields of atmospheric state variables near the surface given coarser-scale atmospheric information and high-resolution information on land surface properties. Such downscaling rules can be applied in coupled subsurface-land surface-atmosphere simulations or to generate high-resolution atmospheric input data for offline applications of land surface and subsurface models. Multiple features of the high-resolution fields, such as the spatial distribution of subgrid-scale variance, serve as objectives. The downscaling rules take an interpretable form and contain on average about 5 mathematical operations. The method is applied to downscale 10 m-temperature fields from 2.8 km to 400 m grid resolution. A large part of the spatial variability is reproduced, also in stable nighttime situations, which generate very heterogeneous near-surface temperature fields in regions with distinct topography.     

一种并行的信息系统属性约简算法*

属性约简是信息系统中的一个重要操作,而分类是属性约简的基础,且直接在大数据集上进行属性约简往往存在效率低下的问题。故以分类为基础提出了一种基于信息熵的信息系统并行属性约简算法。该算法通过信息熵的计算,在属性约简的同时对原信息系统逐层分解成尽量均匀的子表,从而实现了属性约简的并行计算并缩小了搜索空间。对该算法的时间复杂度进行了分析,实验表明,该算法在效率方面优于传统算法。     

Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation

In the last two decades, multiobjective optimization has become main stream and various multiobjective evolutionary algorithms (MOEAs) have been suggested in the field of evolutionary computing (EC) for solving hard combinatorial and continuous multiobjective optimization problems. Most MOEAs employ single evolutionary operators such as crossover, mutation and selection for population evolution. In this paper, we suggest a multiobjective evolutionary algorithm based on multimethods (MMTD) with dynamic resource allocation for coping with continuous multi-objective optimization problems (MOPs). The suggested algorithm employs two well known population based stochastic algorithms namely MOEA/D and NSGA-II as constituent algorithms for population evolution with a dynamic resource allocation scheme. We have examined the performance of the proposed MMTD on two different MOPs test suites: the widely used ZDT problems and the recently formulated test instances for the special session on MOEAs competition of the 2009 IEEE congress on evolutionary computation (CEC’09). Experimental results obtained by the suggested MMTD are more promising than those of some state-of-the-art MOEAs in terms of the inverted generational distance (IGD)-metric on most test problems.     

Conditional Generative Adversarial Networks with Multi-scale Discriminators for Prostate MRI Segmentation

Neural Processing Letters - Accurate prostate MR image segmentation is a necessary preprocessing stage for computer-assisted diagnostic algorithms. Convolutional neural network, as a research focus...     

基于帕累托改进的多机器人动态任务分配算法

针对多机器人系统动态任务分配中存在的优化问题,在使用合同网初始任务分配的基础上提出了一种使用帕累托改进的任务二次分配算法。多机器人系统并行执行救火任务时,首先通过初始化任务分配将多机器人划分为若干子群;然后,每个子群承包某一救火任务,子群在执行任务的同时与就近子群进行帕累托改进确定需要迁移的机器人,实现两子群之间帕累托最优;最后,使用后序二叉树遍历对所有子群进行帕累托改进实现全局帕累托最优。理论分析和仿真结果表明,相较于强化学习算法和蚁群算法,所提算法的救火任务时间分别减少26.18%和37.04%;相较于传统合同网方法,所提算法在时间方面能够高效完成救火任务,在系统收益方面也具有明显优势。     

Image Fusion with Conditional Probability Networks for Monitoring the Salinization of Farmland

We show how a series of satellite images can be used in conjunction with data derived from a digital terrain model to monitor salinity in farmland. A conditional probability network (CPN) is constructed to produce salinity maps by combining uncertain information in images with uncertain knowledge or rules, where the rules are of a temporal and spatial nature. A specific model for extending conditional probability networks to handle the case of spatial context is given. To implement this model requires minor modifications to existing code for handling nonspatial CPN's.     

基于条件梯度Wasserstein生成对抗网络的图像识别

生成式对抗网络GAN功能强大,但是具有收敛速度慢、训练不稳定、生成样本多样性不足等缺点。该文结合条件深度卷积对抗网络CDCGAN和带有梯度惩罚的Wasserstein生成对抗网络WGAN-GP的优点,提出了一个混合模型-条件梯度Wasserstein生成对抗网络CDCWGAN-GP,用带有梯度惩罚的Wasserstein距离训练对抗网络保证了训练稳定性且收敛速度更快,同时加入条件c来指导数据生成。另外为了增强判别器提取特征的能力,该文设计了全局判别器和局部判别器一起打分,最后提取判别器进行图像识别。实验结果证明,该方法有效的提高了图像识别的准确率。     

一种改进的偏好融合组推荐方法

近年来,组推荐系统已经逐渐成为推荐系统领域的研究热点之一.在电影电视和旅游推荐中,用户常常是参与活动的一组人,这就需要为多个用户形成的群组进行推荐.作为解决群组推荐问题的有效手段,组推荐系统将单个用户推荐扩展为群组推荐,目前已经应用在新闻、音乐、电影、餐饮等诸多领域.现有的组推荐融合方法主要是模型融合与推荐融合,其效用好坏目前仍没有定论,并且它们各有自己的优缺点.模型融合存在着群组成员间的公平性问题,推荐融合忽视了群组成员间的交互.提出一种改进的偏好融合组推荐方法,它结合了两种融合方法的优点.同时根据实验得出了"群组偏好与个人偏好具有相似性"的结论,并将它结合在改进方法中.最后,通过在Movielens数据集上的实验分析,验证了该方法的有效性,证明了它能够有效地提高推荐准确率.     

Multi-Agent Constraint Systems with Preferences: Efficiency, Solution Quality, and Privacy Loss

In this paper, we consider multi-agent constraint systems with preferences, modeled as soft constraint systems in which variables and constraints are distributed among multiple autonomous agents. We assume that each agent can set some preferences over its local data, and we consider two different criteria for finding optimal global solutions: fuzzy and Pareto optimality. We propose a general graph-based framework to describe the problem to be solved in its generic form. As a case study, we consider a distributed meeting scheduling problem where each agent has a pre-existing schedule and the agents must decide on a common meeting that satisfies a given optimality condition. For this scenario we consider the topics of solution quality, search efficiency, and privacy loss, where the latter pertains to information about an agent's pre-existing meetings and available time-slots. We also develop and test strategies that trade efficiency for solution quality and strategies that minimize information exchange, including some that do not require inter-agent comparisons of utilities. Our experimental results demonstrate some of the relations among solution quality, efficiency, and privacy loss, and provide useful hints on how to reach a tradeoff among these three factors. In this work, we show how soft constraint formalisms can be used to incorporate preferences into multi-agent problem solving along with other facets of the problem, such as time and distance constraints. This work also shows that the notion of privacy loss can be made concrete so that it can be treated as a distinct, manipulable factor in the context of distributed decision making.     

时间依赖的网络中最小时间路径算法

时间依赖的网络与传统网络模型相比更具有现实意义，具有广泛的应用领域，交通网络和通信网络可以抽象为时间依赖的网络模型，当模型中弧的工度是时间依赖的变量，最短路径问题的求解变得非常困难，早期的研究者通过具体的网络实例认识到传统最短路径算法在这种情况下是不正确的，因此给出限制性条件使得传统最短路径算法是有效的。该文从最短路径算法的理论基础入手，从理论上证明了传统最短路径算法，如Dijkstra算法和标号设置算法，在时间依赖的网络上不能有效地求解最短路径问题，并且，在没有任何限制性条件下，给出了时间依赖的网络模型，理论基础，求解最小时间路径的优化条件和SPTDN算法，从理论上证明了SPTDN算法的正确性，算法的实验结果是正确的，最后给出了时间依赖的网络应用实例。