698 research outputs found
Social machines: how recent technological advances have aided financialisation
No full textIn recent years, financial markets have been fundamentally transformed by innovations in information technology, in particular with regard to the web, social networks, high-speed computer networks and mobile technologies. We borrow the concept of Social Machines from Web Science as a single concept that captures the essence of all these recent technological changes to argue that the emergence of these Social Machines has aided the transformation of financial markets and society. This study explores the formation of these Social Machines with three sample disruptive technologies – automated/high-frequency trading, social network analytics and smart mobile technology. Through critical reflective analysis of these three case studies, we assess the impact of information technology innovation on financialisation. We adopt three case studies – automated trading; market information extraction using social media technologies; and information diffusion and trader decision-making with mobile technology on financial and real sector changes – which demonstrate the increasing trend of transaction velocity, speculative trading, increased complex information network, accelerated inequality and leverage. Our findings demonstrate that technologically enabled financial Social Machines harness crowd wisdom, engage disparate individual traders to produce more accurate price estimations, and have enhanced decision-making capability. However, these same changes can also have a simultaneously detrimental effect on financial and real sectors, in some situations exacerbating underlying distortions, such as misinformation due to complex information networks, speculative trading behaviour, and higher volatility with transaction velocity. Overall, we conclude that these innovations have transformed the fundamental nature of key aspects of the finance industry and society as a whole
From nominal to true a posteriori probabilities: an exact Bayesian theorem based probabilistic data association approach for iterative MIMO detection and decoding
No full textIt was conventionally regarded that the existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In this paper, however, we demonstrate that these probabilities are not the true APPs in the rigorous mathematicasense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the PDA algorithms' inner iteration on the design of PDA-aided IDD receivers. We demonstrate that introducing inner iterations into PDAs, which is common practice in PDA-aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratio (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. We also show that the IDD scheme employing the EB-Log-PDA without incorporating any inner PDA iterations has an achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered
Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures
Full text linkWhen estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the plug-in estimator particularly when the data contain noise. Krätschmer et al. [Comparative and qualitative robustness for law invariant risk measures. Financ. Stoch., 2014, 18, 271–295.] propose a new framework to examine the qualitative robustness of such estimators for the tail-dependent law invariant risk measures on Orlicz spaces, which is a step further from an earlier work by Cont et al. [Robustness and sensitivity analysis of risk measurement procedures. Quant. Finance, 2010, 10, 593–606] for studying the robustness of risk measurement procedures. In this paper, we follow this stream of research to propose a quantitative approach for verifying the statistical robustness of tail-dependent law invariant risk measures. A distinct feature of our approach is that we use the Fortet–Mourier metric to quantify variation of the true underlying probability measure in the analysis of the discrepancy between the law of the plug-in estimator of the risk measure based on the true data and the one based on perturbed data. This approach enables us to derive an explicit error bound for the discrepancy when the risk functional is Lipschitz continuous over a class of admissible sets. Moreover, the newly introduced notion of Lipschitz continuity allows us to examine the degree of robustness for tail-dependent risk measures. Finally, we apply our quantitative approach to some well-known risk measures to illustrate our results and give an example of the tightness of the proposed error bound
Combining POS tagging, lucene search and similarity metrics for entity linking
No full textEntity linking is to detect proper nouns or concrete concepts (a.k.a mentions) from documents, and to map them to the corresponding entries in a given knowledge base. In this paper, we propose an entity linking framework POSLS consisting of three components: mention detection, candidate selection and entity disambiguation. First, we use part of speech tagging and English syntactic rules to detect mentions. We then choose candidates with Lucene search. Finally, we identify the best matchings with a similarity based disambiguation method. Experimental results show that our approach has an acceptable accuracy
A framework for sharing heterogeneous grid resources in a campus environment
No full textGrid computing enables the access of heterogeneous computational resources across a dynamic set of physical organisations seamlessly. A campus grid is an example of the grid solution which is located within a single administrative domain. In the majority of installations these enable connection of all institutionally owned computational resources, making them transparently available to appropriately registered members of staff. In this paper we introduce the design and implementation of a lightweight campus grid framework. It manages a set of heterogeneous computational resources, using virtual organisation based policies for each individual grid resource and enables user's to transparently access resources through a unified user-friendly interface. The proposed framework can discover, aggregate and broker all of these heterogeneous resources with low management costs that matches users' requirements. With this framework, a campus grid enables university-wide, regional-wide, national and international grid resources become transparently available to university members through their university account without knowing how to access each individual resource. Such a framework allows scalable grid resource sharing, virtual organisation based resource management, dynamical and autonomous resource brokering with improved usability and manageability in a campus grid environmen
An ensemble approach to link prediction
No full textA network with n nodes contains O(n 2 ) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity on large networks. Hence, most known link prediction methods are designed for evaluating the link propensity on a specified subset of links, rather than on the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this article, we propose an ensemble enabled approach to scaling up link prediction, by decomposing traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with latent factor models, which can be effectively implemented on networks of modest size. By incorporating with the characteristics of link prediction, the ensemble approach further reduces the sizes of subproblems without sacrificing its prediction accuracy. The ensemble enabled approach has several advantages in terms of performance, and our experimental results demonstrate the effectiveness and scalability of our approach
On the quality of service of crash-recovery failure detectors
No full textWe model the probabilistic behavior of a system comprising a failure detector and a monitored crash-recovery target. We extend failure detectors to take account of failure recovery in the target system. This involves extending QoS measures to include the recovery detection speed and proportion of failures detected. We also extend estimating the parameters of the failure detector to achieve a required QoS to configuring the crash-recovery failure detector. We investigate the impact of the dependability of the monitored process on the QoS of our failure detector. Our analysis indicates that variation in the MTTF and MTTR of the monitored process can have a significant impact on the QoS of our failure detector. Our analysis is supported by simulations that validate our theoretical results
Towards automated verification of autonomous networks: a case study in self-configuration
No full textIn autonomic networks, the self-configuration of network entities is one of the most desirable properties. In this paper, we show how formal verification techniques can verify the correctness of self-configuration. As a case study, we describe the configuration of physical cell identifiers (PCIs), a radio configuration parameter in cellular base stations. We provide formal models of PCI assignment algorithms and their desired properties. We then demonstrate how the potential for conflicting PCI assignments can be detected using model checking and resolved in the design stage. Through this case study, we argue that both simulation and verification should be adopted and highlight the potential of runtime verification approaches in this spac
Evaluation of the QoS of crash-recovery failure detection
No full textCrash failure detection is a key topic in fault tolerance, and it is important to be able to assess the QoS of failure detection services. Most previous work on crash failure detectors has been based on the crash-stop or fail-free assumption. In this paper we study and model a crash-recovery service which has the ability to recover from the crash state. We analyse the QoS bounds for such a crash-recovery failure detection service. Our results show that the dependability metrics of the monitored service will have an impact on the QoS of the failure detection service. Our results are corroborated by simulation results, showing bounds on the Qo
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