The Global Compact for Migration: what could it mean for women and gender relations?

ABSTRACT

Under half of international migrants throughout the world are women. While large movements of people, human rights and humanitarian crises, and migrant deaths are not new, the public attention given to the arrivals of refugees and migrants to the shores of Europe has compelled governments to engage in a multilateral manner. In September 2016, the United Nations General Assembly held its first-ever summit dedicated to large movements of refugees and migrants, reaffirming the importance of existing legal instruments to protect refugees and migrants, and also foreseeing the development of two new Global Compacts: one on refugees, and the other for safe, orderly and regular migration. This article examines the process to elaborate the Global Compact for Safe, Orderly and Regular Migration from a gender-responsive perspective. It takes into consideration the advocacy role that the Women in Migration Network and other civil society stakeholders have played in its development, identifies the various opportunities and gaps within the Global Compact, and explores how women’s organisations and development organisations can promote change for women in migration under the new Global Compact.     

Sparse discriminant analysis based on estimation of posterior probabilities

ABSTRACT

Fisher's linear discriminant analysis (FLDA) is known as a method to find a discriminative feature space for multi-class classification. As a theory of extending FLDA to an ultimate nonlinear form, optimal nonlinear discriminant analysis (ONDA) has been proposed. ONDA indicates that the best theoretical nonlinear map for maximizing the Fisher's discriminant criterion is formulated by using the Bayesian a posterior probabilities. In addition, the theory proves that FLDA is equivalent to ONDA when the Bayesian a posterior probabilities are approximated by linear regression (LR). Due to some limitations of the linear model, there is room to modify FLDA by using stronger approximation/estimation methods. For the purpose of probability estimation, multi-nominal logistic regression (MLR) is more suitable than LR. Along this line, in this paper, we develop a nonlinear discriminant analysis (NDA) in which the posterior probabilities in ONDA are estimated by MLR. In addition, in this paper, we develop a way to introduce sparseness into discriminant analysis. By applying L1 or L2 regularization to LR or MLR, we can incorporate sparseness in FLDA and our NDA to increase generalization performance. The performance of these methods is evaluated by benchmark experiments using last_exam17 standard datasets and a face classification experiment.     

Concordance-based estimation approaches for the optimal sufficient dimension reduction score

To characterize the dependence of a response on covariates of interest, a monotonic structure is linked to a multivariate polynomial transformation of the central subspace (CS) directions with unknown structural degree and dimension. Under a very general semiparametric model formulation, such a sufficient dimension reduction (SDR) score is shown to enjoy the existence, optimality, and uniqueness up to scale and location in the defined concordance probability function. In light of these properties and its single-index representation, two types of concordance-based generalized Bayesian information criteria are constructed to estimate the optimal SDR score and the maximum concordance index. The estimation criteria are further carried out by effective computational procedures. Generally speaking, the outer product of gradients estimation in the first approach has an advantage in computational efficiency and the parameterization system in the second approach greatly reduces the number of parameters in estimation. Different from most existing SDR approaches, only one CS direction is required to be continuous in the proposals. Moreover, the consistency of structural degree and dimension estimators and the asymptotic normality of the optimal SDR score and maximum concordance index estimators are established under some suitable conditions. The performance and practicality of our methodology are also investigated through simulations and empirical illustrations.     

考虑思考时滞的社交网络舆情最优控制模型

随着信息技术的不断发展，纷繁复杂的信息无时不刻的充斥着社交网络，这也使得负面舆情信息不断滋生并快速传播，严重影响了网络安全和社会稳定。本研究提出考虑思考时滞的舆情传播最优控制模型。首先，基于传染病模型构建引入思考者的舆情传播模型。其次，对模型中的参数进行敏感性分析，并引入对立舆论攻击机制，利用最优控制理论设计所提舆情传播最优控制模型，旨在最小化舆情采纳者密度和控制成本。实验表明，所提模型能够对社交网络中的舆情传播进行有效控制。     

集聚经济、城市生产率与最优规模

城市生产率的提高与城市规模的集聚效应密切相关。文章基于夜间灯光、全球人口分布等遥感影像数据重新测算了中国地级市的城市规模,通过建立城市生产率与城市规模的非线性模型,验证了二者之间存在显著的“倒U”型关系。根据遥感影像数据测算所得的城市规模数据,已达到最优规模的城市数量明显增加。分时段、分地区的实证结果表明,城市集聚经济效应呈现增强趋势,东南半壁城市发展的规模效应优于西北半壁城市。     

Bayesian optimization design for dose-finding based on toxicity and efficacy outcomes in phase I/II clinical trials

In phase I trials, the main goal is to identify a maximum tolerated dose under an assumption of monotonicity in dose–response relationships. On the other hand, such monotonicity is no longer applied to biologic agents because a different mode of action from that of cytotoxic agents potentially draws unimodal or flat dose–efficacy curves. Therefore, biologic agents require an optimal dose that provides a sufficient efficacy rate under an acceptable toxicity rate instead of a maximum tolerated dose. Many trials incorporate both toxicity and efficacy data, and drugs with a variety of modes of actions are increasingly being developed; thus, optimal dose estimation designs have been receiving increased attention. Although numerous authors have introduced parametric model-based designs, it is not always appropriate to apply strong assumptions in dose–response relationships. We propose a new design based on a Bayesian optimization framework for identifying optimal doses for biologic agents in phase I/II trials. Our proposed design models dose–response relationships via nonparametric models utilizing a Gaussian process prior, and the uncertainty of estimates is considered in the dose selection process. We compared the operating characteristics of our proposed design against those of three other designs through simulation studies. These include an expansion of Bayesian optimal interval design, the parametric model-based EffTox design, and the isotonic design. In simulations, our proposed design performed well and provided results that were more stable than those from the other designs, in terms of the accuracy of optimal dose estimations and the percentage of correct recommendations.     

考虑广告主投资竞争的关键词拍卖研究

关键词拍卖是搜索引擎盈利手段之一，同时给广告主带来高额回报。在搜索引擎注重质量权重的拍卖规则的推动下和广告主自身利益的驱使下，广告主通过投资来提高自身表现水平，赢得更好排位增加点击量。引入广告主投资，并用连续可变的努力水平来表征参与竞价的广告主投资过程中所付出的各种要素和资源投入，考虑投资和竞价两阶段模型，分析高低两类广告主的努力水平决策及均衡竞价策略。研究表明当满足初始投入最低努力水平时的边际成本大于边际收益的条件时，决定低类型广告主类型转换的估价阈值存在且唯一，并且在情形一中，潜力广告主的最优努力水平总是大于高类型广告主，在情形二中，随着估价的增大，两类广告主的最优努力水平趋于一致；同时，搜索引擎拍卖规则会影响广告主投资过程中最优努力水平的决策：搜索引擎给低类型广告主的质量权重越大，转换类型的估价阈值就越高，且潜力广告主取内点解时的最优努力水平和高类型广告主的最优努力水平均随之减少。最后，通过数值算例分析了两类广告主最优努力水平以及拍卖规则对广告主努力水平的影响。     

Forging a diagonal instrument for the global left: the vessel

ABSTRACT

This article takes up Samir Amin’s challenge to rethink the issue of global political organization by proposing the building of a diagonal political organization for the Global Left that would link local, national and world regional and global networks and prefigurational communities to coordinate contention for power in the world-system during the next few decades of the 21st century. The World Social Forum (WSF) process needs to be reinvented for the current period of rising neo-fascist and populist reactionary nationalism and to foster the emergence of a capable instrument that can confront and contend with the global power structure of world capitalism and aid local and national struggles. This will involve overcoming the fragmentation of progressive movements that have been an outcome of the rise of possessive individualism, the precariat, and social media. We propose a holistic approach to organizing a vessel for the global left based on struggles for climate justice, human rights, anti-racism, queer rights, feminism, sharing networks, peace alliances, taking back the city, progressive nationalism and confronting and defeating neo-fascism and new forms of conservative populism.     

Differential equation model of carbon dioxide emission using functional linear regression

Carbon dioxide is one of the major contributors to Global Warming. In the present study, we develop a differential equation to model the carbon dioxide emission data in the atmosphere using functional linear regression approach. In the proposed method, a differential operator is defined as data smoother and we use the penalized least square fitting criteria to smooth the data. The profile error sum of squares is optimized to estimate the differential operators using functional regression. The solution of the developed differential equation estimates and predicts the rate of change of carbon dioxide in the atmosphere at a particular time. We apply the proposed model to fit the emission of carbon dioxide data in the continental United States. Numerical simulations of a number of test cases depict a satisfactory agreement with real data.