Multi-User Interactive TV: the Next Step in Personalization

In the past few years there has been an increasing trend towards personalization in the TV world. IMS-based IPTV is a good example of a highly personalized IPTV architecture, featuring an advanced identity management subsystem. This article studies a next step in the personalization of the television experience: concurrent use of TV services that are supported by the IMSbased IPTV system. That is, multiple users using the same television set at the same or at different times, where each user as personalized interaction with the service and content. Our analysis of IMS-based IPTV use cases shows that current architectures were not designed with concurrent use in mind. However, we demonstrate that the combination of concurrent use and personalized TV services can yield interesting and viable use cases in the areas of interactive game shows, personalized electronic program guides and channel lists, and other. Finally, an analysis of the IMS-based IPTV system and architecture shows that it has all the ingredients to implement these new concurrent TV use cases, and that the main challenges will be in the area of usability. The article concludes that personalization and concurrency are not contradictory for television services, neither from a use case perspective, nor technologically. In particular, the IMS-based IPTV system is able to facilitate an enhanced and personalized experience to concurrent TV users

Pre-Congestion Notification (PCN) Architecture

This document describes a general architecture for flow admission and termination based on pre-congestion information in order to protect the quality of service of established, inelastic flows within a single Diffserv domain.\u

Distributed mobility management - framework & analysis

Mobile operators consider the distribution of mobility anchors to enable offloading some traffic from their core network. The Distributed Mobility Management (DMM) Working Group is investigating the impact of decentralized mobility management to existing protocol solutions, while taking into account well defined requirements, which are to be met by a future solution. This document discusses DMM using a functional framework. Functional Entities to support DMM as well as reference points between these Functional Entities are introduced and described. The described functional framework allows distribution and co-location of Functional Entities and build a DMM architecture that matches the architecture of available protocols. Such methodology eases the analysis of best current practices with regard to functional and protocol gaps

Resource Management in Diffserv On DemAnd (RODA) PHR

The purpose of this draft is to present the Resource Management in Diffserv (RMD) On DemAnd (RODA) Per Hop Reservation (PHR) protocol. The RODA PHR protocol is used on a per-hop basis in a Differentiated Services (Diffserv) domain and extends the Diffserv Per Hop Behavior (PHB) with resource provisioning and control

Analysis and Optimisation of Building Efficiencies through Data Analytics and Machine Learning

Productivity of workers is greatly affected by their comfort in the workplace. Research has shown that thermal comfort is one of the most influential parameters on worker productivity, and that the running costs of a Heating, Ventilation and Air Conditioning (HVAC) system could be up to ten times lower compared to productivity losses that would be incurred in a free-runing building. With the increased availability of Internet of Things (IoT) devices, it is now possible to continuously monitor multiple variables that influence a user’s thermal comfort and to act pre-emptively to prevent discomfort situations. Smart buildings make use of technology that enable them to become more efficient, reduce costs and emissions and become more transparent in terms of operation. To this end, this work has the following aims; develop a machine learning model to predict setpoint temperatures in an HVAC system; use exploratory data analysis techniques to evaluate the current operation and energy performance of an HVAC system in an office block; and finally, identify and compare patterns and trends between BMS parameters and thermal comfort standards

A multi-factorial genetic model for prognostic assessment of high risk melanoma patients receiving adjuvant interferon

Purpose: IFNa was the first cytokine to demonstrate anti-tumor activity in advanced melanoma. Despite the ability of high-dose IFNa reducing relapse and mortality by up to 33%, large majority of patients experience side effects and toxicity which outweigh the benefits. The current study attempts to identify genetic markers likely to be associated with benefit from IFN-a2b treatment and predictive for survival. Experimental design: We tested the association of variants in FOXP3 microsatellites, CTLA4 SNPs and HLA genotype in 284 melanoma patients and their association with prognosis and survival of melanoma patients who received IFNa adjuvant therapy. Results: Univariate survival analysis suggested that patients bearing either the DRB1*15 or HLA-Cw7 allele suffered worse OS while patients bearing either HLA-Cw6 or HLA-B44 enjoyed better OS. DRB1*15 positive patients suffered also worse RFS and conversely HLA-Cw6 positive patients had better RFS. Multivariate analysis revealed that a five-marker genotyping signature was prognostic of OS independent of disease stage. In the multivariate Cox regression model, HLA-B38 (p = 0.021), HLA-C15 (p = 0.025), HLA-C3 (p = 0.014), DRB1*15 (p = 0.005) and CT60*G/G (0.081) were significantly associated with OS with risk ratio of 0.097 (95% CI, 0.013-0.709), 0.387 (95% CI, 0.169-0.889), 0.449 (95% CI, 0.237-0.851), 1.948 (95% CI, 1.221-3.109) and 1.484 (95% IC, 0.953-2.312) respectively. Conclusion: These results suggest that gene polymorphisms relevant to a biological occurrence are more likely to be informative when studied in concert to address potential redundant or conflicting functions that may limit each gene individual contribution. The five markers identified here exemplify this concept though prospective validation in independent cohorts is needed

Requirements for Signaling of Pre-Congestion Information in a Diffserv Domain

Pre-Congestion Notification (PCN) is a means for protecting quality of service for inelastic traffic admitted to a Diffserv domain. The overall PCN architecture is described in RFC 5559. This memo describes the requirements for the signaling applied within the PCN- domain: (1) PCN-feedback-information is carried from the PCN-egress-node to the Decision Point; (2) the Decision Point may ask the PCN-ingress-node to measure, and report back, the rate of sent PCN-traffic between that PCN-ingress-node and PCN-egress-node. The Decision Point may be either collocated with the PCN-ingress-node or a centralized node (in the first case, (2) is not required). The signaling requirements pertain in particular to two edge behaviors, Controlled Load (CL) and Single Marking (SM)

Ice Model Calibration using Semi-continuous Spatial Data

Rapid changes in Earth’s cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting the future of Arctic and Antarctic ice sheets. However, these models are typically subject to large parametric uncertainties due to poorly constrained model input parameters that govern the behavior of simulated ice sheets. Computer model calibration provides a formal statistical framework to infer parameters using observational data, and to quantify the uncertainty in projections due to the uncertainty in these parameters. Calibration of ice sheet models is often challenging because the relevant model output and observational data take the form of semi-continuous spatial data, with a point mass at zero and a right-skewed continuous distribution for positive values. Current calibration approaches cannot handle such data. Here we introduce a hierarchical latent variable model that handles binary spatial patterns and positive continuous spatial patterns as separate components. To overcome challenges due to high-dimensionality we use likelihood-based generalized principal component analysis to impose low-dimensional structures on the latent variables for spatial dependence. We apply our methodology to calibrate a physical model for the Antarctic ice sheet and demonstrate that we can overcome the aforementioned modeling and computational challenges. As a result of our calibration, we obtain improved future ice-volume change projections