Non-equilibrium fixed points of coupled Ising models

Driven-dissipative systems are expected to give rise to non-equilibrium phenomena that are absent in their equilibrium counterparts. However, phase transitions in these systems generically exhibit an effectively classical equilibrium behavior in spite of their non-equilibrium origin. In this paper, we show that multicritical points in such systems lead to a rich and genuinely non-equilibrium behavior. Specifically, we investigate a driven-dissipative model of interacting bosons that possesses two distinct phase transitions: one from a high- to a low-density phase---reminiscent of a liquid-gas transition---and another to an antiferromagnetic phase. Each phase transition is described by the Ising universality class characterized by an (emergent or microscopic) $\mathbb{Z}_2$ symmetry. They, however, coalesce at a multicritical point, giving rise to a non-equilibrium model of coupled Ising-like order parameters described by a $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry. Using a dynamical renormalization-group approach, we show that a pair of non-equilibrium fixed points (NEFPs) emerge that govern the long-distance critical behavior of the system. We elucidate various exotic features of these NEFPs. In particular, we show that a generic continuous scale invariance at criticality is reduced to a discrete scale invariance. This further results in complex-valued critical exponents and spiraling phase boundaries, and it is also accompanied by a complex Liouvillian gap even close to the phase transition. As direct evidence of the non-equilibrium nature of the NEFPs, we show that the fluctuation-dissipation relation is violated at all scales, leading to an effective temperature that becomes "hotter" and "hotter" at longer and longer wavelengths. Finally, we argue that this non-equilibrium behavior can be observed in cavity arrays with cross-Kerr nonlinearities.Comment: 19+11 pages, 7+9 figure

B2B platform for media content personalisation

This paper proposes a novel business model to support media content personalisation: an agent-based business-to-business (B2B) brokerage platform for media content producer and distributor businesses. Distributors aim to provide viewers with a personalised content experience and producers wish to en-sure that their media objects are watched by as many targeted viewers as possible. In this scenario viewers and media objects (main programmes and candidate objects for insertion) have profiles and, in the case of main programme objects, are annotated with placeholders representing personalisation opportunities, i.e., locations for insertion of personalised media objects. The MultiMedia Brokerage (MMB) platform is a multiagent multilayered brokerage composed by agents that act as sellers and buyers of viewer stream timeslots and/or media objects on behalf of the registered businesses. These agents engage in negotiations to select the media objects that best match the current programme and viewer profiles

Personalised Dynamic Viewer Profiling for Streamed Data

Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.info:eu-repo/semantics/publishedVersio

Product Placement Platform for Personalised Advertising

This paper proposes an integrated approach to personalised product placement involving advertisers, content distributors and viewers. This problem, which is a current challenge for media content producers and distributors, concerns the adaptation of the media content stream with personalised advertising in near real time. Our approach relies on a brokerage and product negotiation platform to match viewer profiles with products descriptions and relegates the actual product placement, i.e., the product rendering process, to the viewer platform. In this scenario, advertisers create and describe product advertisement objects together with the intended target audience using Moving Picture Experts Group (MPEG) standards and a pre-defined metadata schema, while distributors perform viewer profiling and provide viewers with data streams, including personalised products

Improving On-line Genre-based Viewer Profiling

Typically, recommendation algorithms are unable to make recommendations for new users due to the inherent lack of information, i.e., the cold start problem. To overcome this problem, this work addresses builds new viewer profiles by combining general and personal feature-based profiles using both the frequency and the rating of each feature. For each newly arrived viewer, we create a dynamic profile by combining the corresponding demographic stereotypical profile with the individual profile and, then, as the number of the viewergenerated events increases, we gradually fade the general component and strengthen the individual component. Specifically, we combine the genre frequency & rating of the viewer personal and demographic stereotype profiles. This novel viewer profiling algorithm was evaluated with the MovieLens 100k and 1M data sets, using content-based and collaborative stream mining recommendation techniques. When compared with the standard average user stereotype, the results with the demographic stereotypes show a significant improvement in terms of classification accuracy, identical prediction accuracy and an increase in run time

DataTV 2019: 1st International Workshop on Data-Driven Personalisation of Television

The first international workshop on Data-driven Personalisation of Television aims to highlight the significantly growing importance of data in the support of new television content consumption experiences. This includes automatic video summarization, dynamic insertion of content into media streams and object based media broadcasting, to serve the recommendation of TV content and personalization in media delivery. The workshop has two keynote talks alongside five paper presentations and several related demos.info:eu-repo/semantics/publishedVersio

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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Review and publication of protocol submissions to Trials - what have we learned in 10 years?

Abstract Trials has 10 years of experience in providing open access publication of protocols for randomised controlled trials. In this editorial, the senior editors and editors-in-chief of Trials discuss editorial issues regarding managing trial protocol submissions, including the content and format of the protocol, timing of submission, approaches to tracking protocol amendments, and the purpose of peer reviewing a protocol submission. With the clarification and guidance provided, we hope we can make the process of publishing trial protocols more efficient and useful to trial investigators and readers