Anomaly Detection and Removal Using Non-Stationary Gaussian Processes

This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process models for both the physical phenomenon and the fault. In order to facilitate fault removal we introduce the Markov Region Link kernel for handling non-stationary Gaussian processes. This kernel is piece-wise stationary but guarantees that functions generated by it and their derivatives (when required) are everywhere continuous. We apply this kernel to the removal of drift and bias errors in faulty sensor data and also to the recovery of EOG artifact corrupted EEG signals.Comment: 9 pages, 14 figure

The Third Space: The Meeting of Jew and Christian in the Act of Remembering, Restoring, and Reconciling - A Case Study of the Matzevah Foundation

Problem Due to long-standing religious, racial, and cultural tensions, a complex and challenging relationship exists between Jews and Christians. The resulting breach isolates and separates these two faith groups from each other. Consequently, they struggle to interact and engage in meaningful dialogue, which could repair the breach and lead to forgiveness and reconciliation. Dialogue bridges the gap between Jew and Christian allowing them to meet in the third space—the liminal space of the Jewish cemetery in Poland. Jews and Christians may deal with the evil of the past through what researchers term as loving acts. Method This study was conducted as a qualitative case study of the work of The Matzevah Foundation (TMF) in its efforts to bring Jew and Christian together in the space of the Polish-Jewish cemetery to work cooperatively to care for and restore cemeteries. The study employed a purposeful sampling method that selected specific people, who have had contact with TMF and its work. Sources of data for the study were derived from individual and corporate interviews, observations, documents, artifacts, and personal reflective journals. Through inquiry of the interaction of Jews and Christians in the liminal space of the Polish-Jewish cemetery, the study sought to understand how acts of loving-kindness influence attitudes and create mutual bridges of understanding as the underpinning for dialogue. The investigation asked two primary questions. First, how have Jews and Christians responded to the work of TMF? Second, in what ways did Jews and Christians learn how to dialogue within their interaction in the work of TMF? Results It was discovered that Jews and Christians reacted to the work of TMF in five ways: developing relationships, engaging in loving acts, remembering, restoring, and reconciling. These reactions produced the footing for dialogue. The data revealed a framework for dialogue that emerged from Jewish and Christian interaction, which consisted of seven components: addressing proselytism, developing common ground, gaining understanding, building a sense of community, speaking about matters of faith, confronting the present past, and overcoming differences among them. Conclusions The study discovered a potential model for Jewish and Christian dialogue and contributed a greater understanding of the experience of dialogue. Instead of meeting and talking, the distinctive difference of dialogue as encountered in this study is the creation of a nexus within the liminality of a cemetery in which Jews and Christians may mutually interact and cooperate in the restoration of Jewish cemeteries in Poland

Leading in the Third Space

For nearly two millennia, Jews and Christians have struggled to interact with each other and engage in meaningful dialogue. The tragedy of the Shoah only deepened and enlarged the chasm that exists between these two faith groups. How can this fracture be healed, and reconciliation or even dialogue emerge? This article explores the work of The Matzevah Foundation in its efforts to create a nexus within the liminality of a Jewish cemetery in which Jews and Christians may mutually interact and cooperate as they care for and restore Jewish cemeteries in Poland. By examining acts of loving-kindness, Jews’ and Christians’ attitudes are influenced, creating mutual bridges of understanding. This study suggests a framework and a potential model for Jewish and Christian dialogue and highlights critical aspects of the experience of dialogue

A Multi-Dimensional Trust Model for Heterogeneous Contract Observations

In this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dimensional contracts. We consider the specific case of an agent attempting to procure a bundle of services that are subject to correlated quality of service failures (e.g. due to use of shared resources or infrastructure), and where the direct experience of other agents within the system consists of contracts over different combinations of these services. To this end, we present a formalism based on the Kalman filter that represents trust as a vector estimate of the probability that each service will be successfully delivered, and a covariance matrix that describes the uncertainty and correlations between these probabilities. We describe how the agents’ direct experiences of contract outcomes can be represented and combined within this formalism, and we empirically demonstrate that our formalism provides significantly better trustworthiness estimates than the alternative of using separate single-dimensional trust models for each separate service (where information regarding the correlations between each estimate is lost)

Efficient State-Space Inference of Periodic Latent Force Models

Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and application of LFMs can be inhibited by their computational cost, especially when closed-form solutions for the LFM are unavailable, as is the case in many real world problems where these latent forces exhibit periodic behaviour. Given this, we develop a new sparse representation of LFMs which considerably improves their computational efficiency, as well as broadening their applicability, in a principled way, to domains with periodic or near periodic latent forces. Our approach uses a linear basis model to approximate one generative model for each periodic force. We assume that the latent forces are generated from Gaussian process priors and develop a linear basis model which fully expresses these priors. We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes. We also apply our approach within queueing theory in which quasi-periodic arrival rates are modelled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs. Further, we show that state estimates obtained using periodic latent force models can reduce the root mean squared error to 17% of that from non-periodic models and 27% of the nearest rival approach which is the resonator model.Comment: 61 pages, 13 figures, accepted for publication in JMLR. Updates from earlier version occur throughout article in response to JMLR review