Sequential design of computer experiments for the estimation of a probability of failure

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\mathbb{R}^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of $f$ and aim at performing evaluations of $f$ as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.Comment: This is an author-generated postprint version. The published version is available at http://www.springerlink.co

Personalized peptide-based vaccination for treatment of colorectal cancer: rational and progress

Colorectal cancer (CRC) is one of the most common cancers globally and is associated with a high rate of morbidity and mortality. A large proportion of patients with early stage CRC who undergo conventional treatments develop local recurrence or distant metastasis and in this group of advanced disease, the survival rate is low. Furthermore there is often a poor response and/or toxicity associated with chemotherapy and chemo-resistance may limit continuing conventional treatment alone. Choosing novel and targeted therapeutic approaches based on clinicopathological and molecular features of tumors in combination with conventional therapeutic approach could be used to eradicate residual micrometastasis and therefore improve patient prognosis and also be used preventively. Peptide-based vaccination therapy is one class of cancer treatment that could be used to induce tumor-specific immune responses, through the recognition of specific antigen-derived peptides in tumor cells, and this has emerged as a promising anti-cancer therapeutic strategy. The aim of this review was to summarize the main findings of recent studies in exciting field of peptide-based vaccination therapy in CRC patients as a novel therapeutic approach in treatment of CRC

Guidance on a better integration of aquaculture, fisheries, and other activities in the coastal zone: from tools to practical examples

This guidance document provides a comprehensive assessment of the conflicts and synergies between fisheries, aquaculture and other activities in the coastal zone in six COEXIST case study areas. It forms deliverable D5.2 of the COEXIST project and synthesises deliverable D5.1, which provides a more detailed description of the methods used and results. This document also accounts for the views and expectations of stakeholders that were raised at the COEXIST stakeholder workshop held in Bergen, Norway, parallel to the ICES (International Council for the Exploration of the Sea) Annual Science Conference 2012. Over 30 stakeholders representing a variety of sectors, including aquaculture, fisheries, coastal zone management, tourism and energy, as well as 20 members from the COEXIST project and ICES representatives, attended this event. The stakeholders and COEXIST members were from Denmark, Finland, France, Germany, Ireland, Italy, Norway, Portugal, Spain, the Netherlands and the United Kingdom. The workshop aims were firstly to communicate the COEXIST project results and progress to stakeholders and the second major aim was to receive stakeholder feedback on the development of best practice guidance for spatial planning to integrate fisheries, aquaculture and further demands in the coastal zone

A Quarter of Century in Artificial Intelligence and Law: Projects, Personal Trajectories, a Subjective PerspectiveLanguage, Culture, Computation. Computing of the Humanities, Law, and Narratives

This article describes projects in the domain of artificial intelligence and law, which resulted from the research of the five authors listed, when they formed teams (of the first author named and each one of the other authors). Therefore, the present paper offers a subjective perspective, from the viewpoint of personal trajectories within AI & Law. Several, though not all, of the projects concerned dealt with facets of legal evidence. These projects include: ALIBI (an AI planner generating exonerating accounts); a representation of Italy’s regional constitutions in a nested-relation representation (a precursor of XML); the application of kappa calculus and a probabilistic interpretation to a Scandinavian approach to evidential strength; the application of Petri Nets for representing temporal relations in mutual wills; Daedalus (Judge Asaro’s software assisting Italy’s examining magistrates with inquiries, and then when they turn prosecutors); a study in occurrences in court of allegations echoing the pretext archetype “The dog ate my homework” (even when the claim was not pretextuous); an application of Wigmore Charts to an analysis of both the argumentation and the rhetoric of an Italian arringa (final submissions to the court) from a real court case; editorial projects which promoted the emergence of evidence as a conspicuous field within AI & Law (thus overturning previous neglect); and a magnum opus (Nissan 2012a) which presents the state of the art of computational applications to legal evidence, police inquiries, or argumentation