Distance adaptive spectrum allocation exploits the tradeoff between spectrum width and reach to improve resource utilization by tailoring the modulation format to the level of impairments along the path. We first show...
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ISBN:
(纸本)9781479959532
Distance adaptive spectrum allocation exploits the tradeoff between spectrum width and reach to improve resource utilization by tailoring the modulation format to the level of impairments along the path. We first show that the distance-adaptive routing and spectrum assignment (DA-RSA) problem in mesh networks is a special case of a multiprocessor scheduling problem. We then develop a suite of efficient and effective DA-RSA algorithms that build upon list scheduling concepts. Our work explores the tradeoffs involved in DA-RSA algorithm design, and opens up new research directions that may leverage the vast literature in scheduling theory.
There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens su...
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There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and society is another means to achieve responsible and accountable algorithms that is largely overlooked in the literature. We present a case study of the development of an algorithmic tool to estimate the populations of refugee camps to manage the delivery of emergency supplies. This case study demonstrates how in different stages of development of the tool-data selection and pre-processing, training of the algorithm and post-processing and adoption-inclusion of knowledge from the field led to changes to the algorithm. Co-design supported responsibility of the algorithm in the selection of big data sources and in preventing reinforcement of biases. It contributed to accountability of the algorithm by making the estimations transparent and explicable to its users. They were able to use the tool for fitting purposes and used their discretion in the interpretation of the results. It is yet unclear whether this eventually led to better servicing of refugee camps.
Within the broad ambit of algorithm design, the study of dynamic graph algorithms continues to be a thriving area of research. Commensurate with this interest is an extensive literature on the topic. Not surprisingly,...
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Within the broad ambit of algorithm design, the study of dynamic graph algorithms continues to be a thriving area of research. Commensurate with this interest is an extensive literature on the topic. Not surprisingly, dynamic algorithms for all varieties of shortest path problems, in view of their practical importance, occupy a preeminent position. Relevant to this paper are fully dynamic algorithms for chordal graphs. Surprisingly, to the best of our knowledge, there seems to be no reported results for the problem of dynamic algorithms for strongly chordal graphs. To redress this gap, in this paper, we propose a semi-dynamic algorithm for edge-deletions and a semi-dynamic algorithm for edge-insertions in a strongly chordal graph, G. The query complexity of an edge-deletion is O(d(u)(2)d(v)(2)), where d(u) and d(v) are the degrees of the vertices u and v of the candidate edge {u,v}, while the query complexity of an edge-insertion is O(n(2)), where n is the number of vertices of G.
The importance of skyline and dominance relationship analysis has been well recognized in multicriteria decision-making applications. In this paper, we propose the problem of "Finding Skyline with Customer Prefer...
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ISBN:
(纸本)9781509001897
The importance of skyline and dominance relationship analysis has been well recognized in multicriteria decision-making applications. In this paper, we propose the problem of "Finding Skyline with Customer Preferences and Cost Constraints", which utilizes the concept of dominance for business analysis from a microeconomic perspective. Our problem aims to discover the dominance relationship between products and potential customers. Given a set of customer preferences, we want to help the company to design set of competitive products so that the products can satisfy as many customer requirements as possible and the cost of producing the products is within a specified threshold. By "competitive products" we mean that the products cannot be dominated by other products in the market. We formally define the problem and discuss the difficulty of the problem. We also present the foundation and the intuition of our pruning methods. Then we proposed three efficient algorithms that utilize our pruning methods to address the problem. In addition, we also prove the correctness of our proposed algorithms.
The focus of this special issue is optimization methods for problems whose solutions have simple *** structures include sparsity (the solution has very few nonzero entries),low-rankness (the solution is a matrix of ve...
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The focus of this special issue is optimization methods for problems whose solutions have simple *** structures include sparsity (the solution has very few nonzero entries),low-rankness (the solution is a matrix of very low rank),consensus (the solution is a set of identical vectors),and *** authors of the articles in this special issue use certain functions and constraints to ensure their solutions to have these *** functions are typically nonsmooth,and such constraints involve all components of variables,thus posing a challenge to algorithm *** algorithms using (sub)gradients and projections are either non-applicable or performing ***,the authors study new methods for better efficiency.
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