Cooperative Data Exchange based on MDS Codes

The cooperative data exchange problem is studied for the fully connected network. In this problem, each node initially only possesses a subset of the $K$ packets making up the file. Nodes make broadcast transmissions that are received by all other nodes. The goal is for each node to recover the full file. In this paper, we present a polynomial-time deterministic algorithm to compute the optimal (i.e., minimal) number of required broadcast transmissions and to determine the precise transmissions to be made by the nodes. A particular feature of our approach is that {\it each} of the $K-d$ transmissions is a linear combination of {\it exactly} $d+1$ packets, and we show how to optimally choose the value of $d.$ We also show how the coefficients of these linear combinations can be chosen by leveraging a connection to Maximum Distance Separable (MDS) codes. Moreover, we show that our method can be used to solve cooperative data exchange problems with weighted cost as well as the so-called successive local omniscience problem.Comment: 21 pages, 1 figur

‘You don’t need influence … all you need is your first opportunity!’: The Early Broadcast Talent Show and the BBC

Popular histories of the reality talent show often position programmes like Opportunity Knocks as key generic precursors to the popular formats of today. But the visibility of such shows in such popular histories - and in popular memory - disguises the fact that the genre has been almost totally neglected in both television historiography and celebrity studies. In drawing upon archival documentation, this article looks at early examples of the broadcast talent show in Britain, with a particular focus on radio’s Opportunity Knocks, and examines the institutional and cultural discourses which surrounded them

The Role of Peer Influence in Churn in Wireless Networks

Subscriber churn remains a top challenge for wireless carriers. These carriers need to understand the determinants of churn to confidently apply effective retention strategies to ensure their profitability and growth. In this paper, we look at the effect of peer influence on churn and we try to disentangle it from other effects that drive simultaneous churn across friends but that do not relate to peer influence. We analyze a random sample of roughly 10 thousand subscribers from large dataset from a major wireless carrier over a period of 10 months. We apply survival models and generalized propensity score to identify the role of peer influence. We show that the propensity to churn increases when friends do and that it increases more when many strong friends churn. Therefore, our results suggest that churn managers should consider strategies aimed at preventing group churn. We also show that survival models fail to disentangle homophily from peer influence over-estimating the effect of peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing (Socialcom 2014), Best Paper Award Winne

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

Hybrid system of expert system and artificial neural networks for objective evaluation of product sensuous quality

Basic problems and the bottleneck of current approaches for objective assessment of product sensuous quality (PSQ) are discussed. As a solution, a new approach, an expert system (ES) based on artificial neural networks (ANNs) is proposed, in which the ES and ANNs co-operate in a superiority compensation way. T he knowledge base of the system can be effectively built and the evaluation of PSQ can be conducted on-line. As a case study, the new approach has been applied in leather handle test and it proves that the approach is capable of handling non-linear relationships among multiple measured PSQ parameters

Dynamic Matrix-Fracture Transfer Behaviour in Dual-Porosity Models

Imperial Users onl

An alternative SU(4) x SU(2)L x SU(2)R model

A simple alternative to the usual Pati-Salam model is proposed. The model allows quarks and leptons to be unified with gauge group $SU(4) \otimes SU(2)_L \otimes SU(2)_R$ at a remarkably low scale of about 1 TeV. Neutrino masses in the model arise radiatively and are naturally light.Comment: 9 pages, Latex (1 Figure

Revisiting Play School: A historical case study of the BBC’s address to the pre-school audience

Although clearly recognised in broader institutional histories of British children’s television as a significant moment in the BBC’s address toward the pre-school child, Play School (BBC 1964-88) has not been the focus of sustained archival analysis. This arguably reflects the fact that a good deal of work on children’s television in Britain adopts either an institutional or an audience focus, and the study of programmes cultures is often more neglected. This article seeks to revisit Play School using available historical documentation – including memos, scripts and press cuttings - from the BBC Written Archive Centre, as well as early surviving episodes (principally from 1964). In doing so, it seeks to explore how it fitted into BBC’s historical address to the pre-school child, how it intersected with discourses on pre-school education, and the range of institutional and social contexts surrounding its emergence. Key Words: Play School * Pre-school television * BBC * Child audienc

Single-Server Multi-Message Private Information Retrieval with Side Information

We study the problem of single-server multi-message private information retrieval with side information. One user wants to recover $N$ out of $K$ independent messages which are stored at a single server. The user initially possesses a subset of $M$ messages as side information. The goal of the user is to download the $N$ demand messages while not leaking any information about the indices of these messages to the server. In this paper, we characterize the minimum number of required transmissions. We also present the optimal linear coding scheme which enables the user to download the demand messages and preserves the privacy of their indices. Moreover, we show that the trivial MDS coding scheme with $K-M$ transmissions is optimal if $N>M$ or $N^2+N \ge K-M$. This means if one wishes to privately download more than the square-root of the number of files in the database, then one must effectively download the full database (minus the side information), irrespective of the amount of side information one has available.Comment: 12 pages, submitted to the 56th Allerton conferenc