Group testing in graphs

This paper studies the group testing problem in graphs as follows. Given a graph G=(V,E), determine the minimum number t(G) such that t(G) tests are sufficient to identify an unknown edge e with each test specifies a subset X⊆V and answers whether the unknown edge e is in G[X] or not. Damaschke proved that ⌈log ₂ e(G)⌉≤t(G)≤⌈log ₂ e(G)⌉+1 for any graph G, where e(G) is the number of edges of G. While there are infinitely many complete graphs that attain the upper bound, it was conjectured by Chang and Hwang that the lower bound is attained by all bipartite graphs. Later, they proved that the conjecture is true for complete bipartite graphs. Chang and Juan verified the conjecture for bipartite graphs G with e(G)≤2⁴ or for k≥5. This paper proves the conjecture for bipartite graphs G with e(G)≤2⁵ or for k≥6. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday. J.S.-t.J. is supported in part by the National Science Council under grant NSC89-2218-E-260-013. G.J.C. is supported in part by the National Science Council under grant NSC93-2213-E002-28. Taida Institute for Mathematical Sciences, National Taiwan University, Taipei 10617, Taiwan. National Center for Theoretical Sciences, Taipei Office.     

The social power of algorithms

ABSTRACT

This article explores the questions associated with what might be thought of as the social power of algorithms. The article, which introduces a special issue on the same topic, begins by reflecting on how we might approach algorithms from a social scientific perspective. The article is then split into two sections. The first deals with the issues that might be associated with an analysis of the power of the algorithms themselves. This section outlines a series of issues associated with the functionality of the algorithms and how these functions are powerfully deployed within social world. The second section then focuses upon the notion of the algorithm. In this section, the article argues that we need to look beyond the algorithms themselves, as a technical and material presence, to explore how the notion or concept of the algorithm is also an important feature of their potential power. In this section, it is suggested that we look at the way that notions of the algorithm are evoked as a part of broader rationalities and ways of seeing the world. Exploring the notion of the algorithm may enable us to see how algorithms also play a part in social ordering processes, both in terms of how the algorithm is used to promote certain visions of calculative objectivity and also in relation to the wider governmentalities that this concept might be used to open up.     

Algorithms (and the) everyday

ABSTRACT

Our everyday practices are increasingly mediated through online technologies, entailing the navigation and also oft-simultaneous creation of large quantities of information and communication data. The scale and types of activities being undertaken, the data that are being created and engaged with, and the possibilities for analysis, archiving and distribution are now so extensive that technical constructs are necessarily required as a way to manage, interpret and distribute these. These constructs include the platforms, the software, the codes and the algorithms. This paper explores the place of the algorithm in shaping and engaging with the contemporary everyday. It does this via an exploration of some particular instances of algorithmic sorting and presentation as well as considering some of the ways these contribute to shaping our everyday practices and understandings. In doing so, it raises questions about understandings of agency and power, shifting world views and our complex relationship with technologies.     

Bayesian inference for joint location and scale nonlinear models with skew-normal errors

A regression model with skew-normal errors provides a useful extension for ordinary normal regression models when the dataset under consideration involves asymmetric outcomes. In this article, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis for joint location and scale nonlinear models with skew-normal errors, which relax the normality assumption and include the normal one as a special case. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of MCMC methods to simulate samples from the joint posterior distribution. Finally, simulation studies and a real example are used to illustrate the proposed methodology.     

Not every Gibbs sampler is a special case of the Metropolis–Hastings algorithm

It is commonly asserted that the Gibbs sampler is a special case of the Metropolis–Hastings (MH) algorithm. While this statement is true for certain Gibbs samplers, it is not true in general for the version that is taught and used most often, namely, the deterministic scan Gibbs sampler. In this note, I prove that that there exist deterministic scan Gibbs samplers that do not exhibit detailed balance and hence cannot be considered MH samplers. The nuances of various Gibbs sampling schemes are discussed.     

Integrating Social Network Effects in the Share‐Of‐Choice Problem

Accounting for social network effects in marketing strategies has become an important issue. Taking a step back, we seek to incorporate and analyze social network effects on new product development and then propose a model to engineer product diffusion over a social network. We build upon the share‐of‐choice (SOC) problem, which is a strategic combinatorial optimization problem used commonly as one of the methods to analyze conjoint analysis data by marketers in order to identify a product with largest market share, and show how to incorporate social network effects in the SOC problem. We construct a genetic algorithm to solve this computationally challenging (NP‐Hard) problem and show that ignoring social network effects in the design phase results in a significantly lower market share for a product. In this setting, we introduce the secondary operational problem of determining the least expensive way of influencing individuals and strengthening product diffusion over a social network. This secondary problem is of independent interest, as it addresses contagion models and the issue of intervening in diffusion over a social network, which are of significant interest in marketing and epidemiological settings.     

加快文化大都市建设，大力提升城市文化软实力

“十二五”是上海城市转型发展的关键时期,上海迫切需要以新的理念和新的思路加快国际文化大都市建设。文化建设的内容主要包括社会主义核心价值体系、公共文化服务体系、文化及创意产业体系等。在此基础上,上海应积极营造有利于文化发展的综合环境,并提出构建国际文化大都市的具体目标和重点任务。     

区域一体化与广佛都市圈产业体系的构建

区域一体化已成为许多地方政府实现经济发展与产业转型的主体战略,从2000年首次提出“广佛都市圈”概念到现在,广佛合作已经从单纯的基础设施对接迈向产业合作的新阶段。文章在分析广佛都市圈现状产业发展特点和存在问题的基础上,通过明晰区域产业分工演进规律,明确了未来广佛都市圈产业体系发展格局,提出了广佛都市圈产业体系构建的措施与建议。     

Application of a Risk Analysis Tool to Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Outbreak in Saudi Arabia

The Grunow–Finke assessment tool (GFT) is an accepted scoring system for determining likelihood of an outbreak being unnatural in origin. Considering its high specificity but low sensitivity, a modified Grunow–Finke tool (mGFT) has been developed with improved sensitivity. The mGFT has been validated against some past disease outbreaks, but it has not been applied to ongoing outbreaks. This study is aimed to score the outbreak of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia using both the original GFT and mGFT. The publicly available data on human cases of MERS-CoV infections reported in Saudi Arabia (2012–2018) were sourced from the FluTrackers, World Health Organization, Saudi Ministry of Health, and published literature associated with MERS outbreaks investigations. The risk assessment of MERS-CoV in Saudi Arabia was analyzed using the original GFT and mGFT criteria, algorithms, and thresholds. The scoring points for each criterion were determined by three researchers to minimize the subjectivity. The results showed 40 points of total possible 54 points using the original GFT (likelihood: 74%), and 40 points of a total possible 60 points (likelihood: 67%) using the mGFT, both tools indicating a high likelihood that human MERS-CoV in Saudi Arabia is unnatural in origin. The findings simply flag unusual patterns in this outbreak, but do not prove unnatural etiology. Proof of bioattacks can only be obtained by law enforcement and intelligence agencies. This study demonstrated the value and flexibility of the mGFT in assessing and predicting the risk for an ongoing outbreak with simple criteria.     

Convergence Behavior of the em algorithm for the multivariate t -distribution

Iterative reweighting (IR) is a popular method for computing M-estimates of location and scatter in multivariate robust estimation. When the objective function comes from a scale mixture of normal distributions the iterative reweighting algorithm can be identified as an EM algorithm. The purpose of this paper is to show that in the special case of the multivariate t-distribution, substantial improvements to the convergence rate can be obtained by modifying the EM algorithm.