一种利用机器学习优化MIMO窃听信道天线选择的方法

为保障多输入多输出窃听信道系统中信息传输的保密性，提出了一种基于机器学习的天线选择方案。首先利用机器学习解决分类问题准确率高、处理大数据高效这一优势，设计了基于奇异值分解的特征值提取、基于信干噪比的标签赋值方案，建立了k最近邻分类器和逻辑回归分类器选择最优天线最大化保密性能（可达保密速率和保密中断概率）。与传统天线选择方案相比，所提方案获得了几乎一致的保密性能，并且大幅降低了系统的选择复杂度和误比特率。     

气候障碍因子对城市绿化适生树种选择的 影响——以上海世博公园为例

通过对上海近30年的气候因子汇总分析，结合对上 海世博公园主要绿化树种生长状况调查和年度生物量计算，通 过典型相关性分析发现，极端高低温、强风和暴雨等5个气候 因子对树木的生长产生了不同程度的影响，有些因子严重阻碍 了树木的正常生长，成为障碍因子。经过10年的绿化实践验 证，上海世博公园对骨干树种的选择，以乡土树种为主，群落 结构较为稳定，且气候障碍因子对乡土树种的影响较小。在引 种外来树种时，采用气候相似植物区系法，结合植物耐寒、耐 热带图，把种源地划分为2个气候带和3个植物区系，并确定了 最南引种线，成功引种了红花槭等20种观花色叶乔木树种。     

Policy lessons from spatiotemporal enrollment patterns of payment for ecosystem service programs in Argentina

Payment for ecosystem services schemes (PES) are lauded as a market-based solution to curtail deforestation and restore degraded ecosystems. However, PES programs often fail to conserve sites under strong long-term deforestation pressures. Underperformance, in part, is likely due to adverse selection. Spatial adverse selection occurs when landowners are more likely to enroll parcels with low deforestation pressure than parcels with high deforestation pressure. Temporal adverse selection arises when parcels are enrolled for short time periods. In both cases, financial resources are allocated without having a sizeable impact on long-term land use change. Improving program performance to overcome these shortcomings requires understanding attributes of landowners and their parcels across large scales to identify spatial and temporal enrollment patterns that drive adverse selection. In this paper, we examine these patterns in Argentina’s PES program in Chaco forest, a global deforestation hotspot. Our study area covers 252,319 km². Results from multinomial logistic regression models showed that large parcels of enrolled land and parcels owned by absentee landowners exhibit greater evidence of spatiotemporal adverse selection than smaller parcels or parcels owned by local landowners. Furthermore, parcels managed under land use plans for conservation and restoration are more likely to be associated with adverse selection than parcels managed for financial returns such as harvest of non-timber forest products, silviculture, and silvopasture. However, prior to recommending that PES programs focus on land uses with higher potential earnings, a greater understanding is needed of the degree to which these land uses meet ecological and biodiversity goals of PES programs. We suggest that increased spatial targeting of enrollment, along with enrollment of local landowners and further incentives for land uses that support conservation and restoration, could promote long-term conservation of forest lands.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Existence of solutions to principal–agent problems with adverse selection under minimal assumptions

We prove an existence result for the principal–agent problem with adverse selection under general assumptions on preferences and allocation spaces. Instead of assuming that the allocation space is finite-dimensional or compact, we consider a more general coercivity condition which takes into account the principal’s cost and the agents’ preferences. Our existence proof is simple and flexible enough to adapt to partial participation models as well as to the case of type-dependent budget constraints.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

新型煤炭企业物资供应人才内涵与选用标准

介绍了新型煤炭企业物资供应人才的内涵,构建了安全效益、经济效益和管理效益3个维度;分析了选用新型煤炭企业物资供应人才的3大标准,提出了从道德品质、素质能力、态度观念等方面着手的观点,对实践具有重要指导和参照意义。     

基于改进TOPSIS法的供应商选择模型研究

为有效地帮助企业快速找到合适的供应商合作伙伴,采用直觉模糊集、评分函数等方法对TOPSIS评估法进行优化,并以此为基础建立了一种供应商选择模型。首先,采集和评估供应商的产品质量、产品价格、产品交货的可靠性、供应位置、财务情况、库存水平、劳资关系、发展能力和技术能力等相关信息,由专家给出主观评估信息,汇总为综合属性值;然后,通过直觉模糊熵确定各评估指标的权重;最后,综合考虑供应商选择决策过程中的多个目标和标准,应用改进TOPSIS法的对供应商进行分类选择。结果显示,基于改进TOPSIS法的供应商选择模型能够较准确地反映出各供应商的真实水平和对企业的潜在价值,可以有效地解决不确定条件下对供应商的选择问题,提高了供应商选择结果的可靠性。改进后的模型简便易行,具有良好的稳定性,对于合理制定企业供应商选择标准以及进一步优化决策模型具有一定的借鉴意义。     

少数民族社区旅游的舞台化特征研究——以云南若干村镇为例

云南民族众多、人文和自然景观富饶,少数民族社区旅游发展起步较早,发展较成熟,在长期发展过程中,呈现出了明显的舞台化特征.在考察了大理古城、周城,丽江束河古镇,香格里拉霞给村,西双版纳傣族园等较为典型的旅游村镇的基础上,归纳认为少数民族社区旅游的舞台化特征主要体现在3个方面:社区旅游圈、东道主行为前台化和民俗节事舞台化.这种舞台化有利于少数民族传统村镇在空间特质和精神特质上的分区保护;有利于提高居民参与社区旅游发展的参与意识和传统文化意识;有利于游客在有限的时空范围内体验少数民族的风土人情.     

旅游市场中的逆向选择和重复博弈——以海南旅游业为例

由于旅游服务产品质量的信息在游客和旅游企业之间的分布是不对称的,这就使得劣质旅游企业对优质旅游企业的逆向淘汰成为可能.本文以海南旅游业为例,分阶段对旅游市场中的逆向选择和重复博弈问题进行了探讨,系统论证了劣质市场均衡出现、维护和强化的条件和成因.