Conformant planning is a computationally challenging task that generates an action sequence to achieve goal condition with uncertain initial states and non-deterministic actions. The generate-and-complete (in short, GC) approach shows superior performance on conformant planning, which iteratively enumerates the solution of a planning subproblem for a single initial state and attempts to extend it for all initial states until a conform solution is found. However, two major drawbacks of the GC approach hinder its performance: the computational overhead due to state exploration and the insertion of many redundant actions. To overcome the above drawbacks, we improve both verification and completion procedures. Experimental results show that the improved GC planner has significant improvements over the original GC approach in many instances with a large number of initial states. Our approach also outperforms all of state-of-the-art planners, solving 989 instances in comparison to 784, which is the most solved by DNF.

In modern data centers, many flow scheduling schemes are proposed to accelerate data transfer and improve user experience. However, these schemes assume ideally the prior knowledge of the flow size information, which, unfortunately, is hard to obtain without modifying data center applications. The sketch-based approaches measure the flow size at switch with a compact memory structure, high throughput, and acceptable accuracy loss. However, existing sketches commonly focus on large or specific flows, while most flows in data center networks are small, resulting in missing or overestimated size information about small flows. We propose Strainer Sketch, which enables accurate and fast measurement of small flows with small memory and flexible deployment in a variety of scheduling algorithms. Specifically, Strainer Sketch uses the hierarchical structure to mitigate hash collisions between large and small flows, and the probabilistic counting algorithm to mitigate overestimation due to hash collisions between small flows. Furthermore, we propose a packet scheduling algorithm SW-PIFO, which provides the flow discrimination for a huge number of small flows by using a limited number of queues. Through the testbed experiments and simulations of typical data center applications, we show that our scheme reduces the small flow completion time (FCT) by up to 56.7 <?TeX $\%$?> Math 1 compared with flow scheduling using classic sketches.

The uncertainty of the trust relationships between decision makers (DMs) or the uncertainty of individual weights will lead to the uncertainty of fairness and consensus management in group. To provide a solid solution, we propose a robust maximum fairness consensus model with limited cost under the uncertain trust relationships and individual weights (RTRMFCM). Specifically, this article constructs an uncertainty set to more precisely describe the uncertainty of the trust relationship between DMs. Based on individual fair preference considerations from the trust network, we adopt a robust optimization method to reduce the risk of DMs feeling unfair. Furthermore, a robust approach based on the combination of uncertain individual weights has been developed to reduce the risk of consensus being destroyed. Then, some theoretical analyzes are presented. We also provide a numerical analysis to validate the applicability of the RTRMFCM. Finally, the simulation analyses have revealed the characteristics of the RTRMFCM, and show that the RTRMFCM is more effective than the maximum fairness consensus model with limited cost under trust relationship (TRMFCM).

Opinion and action have important roles in human society, but few studies have considered the interactive evolution of opinion and action. Inspired by the social network DeGroot (SNDG) model and the social network opinions and actions evolution (SNOAE) model, an opinion and action interactive evolution (OAIE) model based on social network leadership and opinion estimation of action is proposed, and the stable state and consensus of agents’ opinions and actions are investigated. The results show that in the interactive evolution of opinion and action, when each agent can accurately estimate the opinions of other agents based on their actions in the final stage, all agents are stable agents. If the opinion leader exists, a consensus and unified action can be formed by all agents; otherwise, all agents can reach a consensus and unified action in the subnetwork. In addition, if each agent is always unable to estimate the true opinions of other agents, then all agents are oscillatory agents.

Inspired by the continuous opinion and discrete action (CODA) model, bounded confidence and social networks, the bounded confidence evolution of opinions and actions in social networks is investigated and a social network opinions and actions evolutions (SNOAEs) model is proposed. In the SNOAE model, it is assumed that each agent has a CODA for a certain issue. Agents' opinions are private and invisible, that is, an individual agent only knows its own opinion and cannot obtain other agents' opinions unless there is a social network connection edge that allows their communication; agents' actions are public and visible to all agents and impact other agents' actions. Opinions and actions evolve in a directed social network. In the limitation of the bounded confidence, other agents' actions or agents' opinions noticed or obtained by network communication, respectively, are used by agents to update their opinions. Based on the SNOAE model, the evolution of the opinions and actions with bounded confidence is investigated in social networks both theoretically and experimentally with a detailed simulation analysis. Theoretical research results show that discrete actions can attract agents who trust the discrete action, and make agents to express extreme opinions. Simulation experiments results show that social network connection probability, bounded confidence, and the opinion threshold of action choice parameters have strong impacts on the evolution of opinions and actions. However, the number of agents in the social network has no obvious influence on the evolution of opinions and actions.

When people express their opinions, they often cannot provide exact opinions but express uncertain opinions, such as numerical interval opinions. Moreover, due to the differences in the cultural background and character of agents, people who encounter numerical interval opinions often show different uncertainty tolerances. By taking different numerical interval opinions and different uncertainty tolerances into account, in this article, we propose a numerical interval opinion dynamics model to investigate the process of forming collective opinions in a group of interaction agents under an uncertain and social network context. We propose the theoretical analysis and algorithms to identify the stable agents whose opinions will be becoming stable and the oscillation agents whose opinions will always be fluctuating in the opinion evolution process. Furthermore, we study the conditions under which a consensus opinion can be built among the stable agents and estimate the opinion ranges of the oscillation agents. Finally, numerical examples and analysis are used to show the feasibility and effectiveness of the proposed theories and algorithms.

When people express their opinions about a certain issue, they often give uncertain opinions rather than exact opinions. Particularly, these uncertain opinions will evolve in social networks. Therefore, in this paper, we focus on investigating uncertain opinion formation with social networks under bounded confidence. Specifically, we define the uncertain opinions by numerical interval opinions, whose ranges are between zero and one, and the larger width of numerical interval opinions means the more uncertainty of the opinions. Meanwhile, we describe social network structures by ER random graphs with different agents' scales and network connected probabilities. Then, we present the detailed simulation experiments to reveal the strong impact of social network structures on uncertain opinion formation. Simulation results show that: 1) larger agents' scales will yield the smaller ratios of agents expressing the uncertain opinions and larger average widths of uncertain opinions; 2) the average stable time starts increasing and then decreases with the increase in the network connected probabilities; and 3) larger network connected probabilities will yield less opinion clusters and the smaller ratios of the extremely small clusters in all clusters. The obtained results are helpful for the government and public opinion management departments to understand and manage uncertain public opinion evolution effectively.

In this study, we propose a social network uncertain opinion formation model in the framework of bounded confidence to investigate the process of forming collective opinions in a group of interaction agents under uncertain and social networks context. By taking different uncertain opinions and different uncertainty tolerances into account, the simulations analysis conducted on data describing users' relationships in social media platforms in opinion formation from two aspects: different social networks connection probability and the numbers of agents. Based on simulation analysis, we provide the explanations of the observations obtained.

BACKGROUND: The early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research. RESULTS: The mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10-6 or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity. CONCLUSION: The ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods.