全断面隧道掘进机工作参数匹配规律的挖掘与利用

为提高隧道掘进机工作效能和利用率，以采集的秦岭隧道工作数据为对象，分析研究了不同掘进状况下工作参数间的匹配规律，对比分析了各类掘进状况下的切削比能，获得了掘进机正常掘进状况下的FPI与切割系数C的制约方程，得到了软、硬岩条降下的推进力、扭矩需求方程，并且从统计角度获得了切割系数的概率密度分布函数。所获得的相关规律以及处理方法，为掘进状况的预测和掘进参数的优化提供了一定的知识和技术支持，对于掘进机的性能评价和掘进机设计部具有重要的参考价值。     

基于Sai-GA-SVR的我国IPO 制度与新股市场特征研究

针对“中国IPO 高抑价之谜”,提出了Sai-GA-SVＲ方法,从市场特征的角度来判断新股是抑价还是溢价,分析了我国1997年―2011 年不同制度下的IPO 市场特征． 研究发现: 审批制和价格管制导致了较为严重的抑价,但是随着市场化改革进程的不断推进,新股市场已表现出极大程度的溢价现象; 询价制前的新股发行价格仅仅反映公司内在价值,不能反映IPO的市场供求关系与风险水平,同时过于依赖机构投资者的询价制扭曲了制度租金的公平分配．     

“Super-covariates”: Using predicted control group outcome as a covariate in randomized clinical trials

The power of randomized controlled clinical trials to demonstrate the efficacy of a drug compared with a control group depends not just on how efficacious the drug is, but also on the variation in patients' outcomes. Adjusting for prognostic covariates during trial analysis can reduce this variation. For this reason, the primary statistical analysis of a clinical trial is often based on regression models that besides terms for treatment and some further terms (e.g., stratification factors used in the randomization scheme of the trial) also includes a baseline (pre-treatment) assessment of the primary outcome. We suggest to include a “super-covariate”—that is, a patient-specific prediction of the control group outcome—as a further covariate (but not as an offset). We train a prognostic model or ensembles of such models on the individual patient (or aggregate) data of other studies in similar patients, but not the new trial under analysis. This has the potential to use historical data to increase the power of clinical trials and avoids the concern of type I error inflation with Bayesian approaches, but in contrast to them has a greater benefit for larger sample sizes. It is important for prognostic models behind “super-covariates” to generalize well across different patient populations in order to similarly reduce unexplained variability whether the trial(s) to develop the model are identical to the new trial or not. In an example in neovascular age-related macular degeneration we saw efficiency gains from the use of a “super-covariate”.     

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.     

传统“人机关系”的马克思主义哲学审思

在哲学史上,对“人机关系”的思考亘古有之。“传统人机关系”是指“手工工具”“近代机器”两个阶段的人机关系,可概括为“机械式”“有机式”“分离式”三种关系。“机械式”人机关系总体上固然具有唯物主义特点,但是却陷入了形而上学的思维怪圈。“有机式”的人机关系论述纷繁复杂,其中恩格斯的阐发最具科学性,克服了以往唯心主义与机械唯物主义的人机关系论述弊端。“分离式”的人机关系论述以马克思为代表,触及机器“异化”人的本质问题,体现了人与机器的残酷对立与非人性化分离。从马克思主义哲学视角对传统人机关系进行把握,有助于破除关于“人机关系”哲学理解上的思想“迷雾”,引导构建智能技术革命背景下和谐的新型“人机关系”。     

Packing trees in communication networks

Given an undirected edge-capacitated graph and given (possibly) different subsets of vertices, we consider the problem of selecting a maximum (weighted) set of Steiner trees, each tree spanning a subset of vertices, without violating the capacity constraints. This problem is motivated by applications in multicast communication networks. We give an integer linear programming (ILP) formulation for the problem, and observe that its linear programming (LP) relaxation is a fractional packing problem with exponentially many variables and a block (sub-)problem that cannot be solved in polynomial time. To this end, we take an r-approximate block solver (a weak block solver) to develop a (1−ε)/r-approximation algorithm for the LP relaxation. The algorithm has a polynomial coordination complexity for any ε∈(0,1). To the best of our knowledge, this is the first approximation result for fractional packing problems with only weak block solvers (with arbitrarily large approximation ratio) and a coordination complexity that is polynomial in the input size. This leads also to an approximation algorithm for the underlying tree packing problem. Finally, we extend our results to an important multicast routing and wavelength assignment problem in optical networks, where each Steiner tree is to be assigned one of a limited set of given wavelengths, so that trees crossing the same fiber are assigned different wavelengths. A preliminary version of this paper appeared in the Proceedings of the 1st Workshop on Internet and Network Economics (WINE 2005), LNCS, vol. 3828, pp. 688–697. Research supported by a MITACS grant for all the authors, an NSERC post doctoral fellowship for the first author, the NSERC Discovery Grant #5-48923 for the second and fourth author, NSERC Discovery Grant #15296 for the third author, the Canada Research Chair Program for the second author, and an NSERC industrial and development fellowship for the fourth author.     

基于混合模型的国际原油价格预测研究

由于国际原油价格的剧烈波动,使得准确的原油价格预测极具挑战。为此,提出一种基于变分模态分解、季节性差分自回归滑动平均模型和果蝇优化最小二乘支持向量机的混合模型。利用变分模态分解方法将国际原油价格序列分解成一系列模态分量;针对周期性和非线性特征分量,分别建立季节性差分自回归滑动平均模型和果蝇优化最小二乘支持向量机模型进行预测;将各分量的预测值求和作为最终的预测结果。实证研究结果表明:所提混合模型相较对比模型能够明显提高国际原油价格的预测精度。     

基于改进SMOTE的小额贷款公司客户信用风险非均衡SVM分类

研究了小额贷款公司对客户进行信用风险评估时面临的问题,构建了信用风险评估指标体系,改进了支持向量机(Support Vector Machine, SVM)对非均衡样本分类时分类超平面偏移的不足。首先分析小额贷款公司业务区域性强、信用数据来源不规范、评价标准不一致等特点,给出用于客户信用风险评估的四个维度指标。针对传统SMOTE算法在处理非均衡数据时对全部少数类样本操作的问题,提出仅对错分样本人工合成的改进思想,给出具体算法步骤。将改进算法用于某小额贷款公司客户信用风险评估案例中,分类精确度较其他算法有所提升,表明该方法的可行性和有效性。     

浅谈受限辅助翻译系统的商品化应用

从实用的角度看,全自动、高质量的机器翻译是近期难以实现的目标。而受限辅助翻译系统是应用于特定用户与特定材料的人机互助式机器翻译,对受限辅助翻译系统的商品化研究与开发能够在近期内给人们的工作和生活带来更实际的帮助。     

基于PSO和SVM的上市公司财务危机预警模型

提出了一种将经过改进的离散粒子群(PSO)算法和支持向量机(SVM)相结合的算法,以选择最优的指标集并用于财务危机预警。将此算法应用于上市公司的数据,检验模型提前3年的预警效果,最后与常见的主成分分析方法与SVM相结合的模型进行对比,结果证明了PSO-SVM模型的合理性和优越性。