ABSTRACTIn many ‘smart city’ applications, congestion arises in part due to the nature of signals received by individuals from a central authority. In the model of , each agent uses one out of multiple resources at each time instant. The per-use cost of a resource depends on the number of concurrent users. A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time. In this paper, the interval to broadcast per resource is obtained by taking the minima and maxima of costs observed within a time window of length r, rather than by randomisation. We show that the resulting distribution of agents across resources also converges in distribution, under plausible assumptions about the evolution of the population over time.
Throughout, we are not aware of any theoretical guarantees on the behavior of policies similar to ours, as described in this paper and @cite_10 . Specifically, we are not aware of any other paper, which would study the broadcasting of intervals, instead of scalars, show its superiority, or study the behaviour of systems, where such signals are being provided. Compared to this paper, the set-up of @cite_10 is much simpler, and so are the proofs and simulations. Unlike @cite_10 , the approach presented in this paper does not employ randomisation, whose use may be unacceptable to the general public, allows for the same signal to be broadcast to all agents, such as at road-side displays, and considers a more elaborate model of the populational response, with risk-aware agents. Both this paper and @cite_10 suggest the importance of the control-theoretic aspects of information provision.
@cite_10
2104378250
We consider a multi-agent system where, at every time instant, many users choose to use one of multiple resources, whose performance depends on the number of concurrent users. In addition, a central agent has up-to-date knowledge of the congestion across all resources. What signals should the central agent broadcast to the individual users in order to reduce the total congestion? We model users with varying levels of aversion to risk, and a heterogeneous population of users over these levels. We consider signaling schemes that communicate for each resource an interval of possible congestion values, instead of scalar values. We show how to optimise over these intervals with respect to the social cost, under constraints that these intervals be consistent with past observations. Moreover, under mild assumptions, the resulting distribution of users across resources converges. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles.