Link Prediction via Generalized Coupled Tensor Factorisation

This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com- mon latent factors using different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links

Digital Detection of Oxide Breakdown and Life-Time Extension in Submicron CMOS Technology

An approach is introduced to extend the lifetime of high-voltage analog circuits in CMOS technologies based on redundancy, like that known for DRAMS. A large power transistor is segmented into N smaller ones in parallel. If a sub-transistor is broken, it is removed automatically from the compound transistor. The principleis demonstrated in an RF CMOS Power Amplifier (PA) in standard 1.2V 90nm CMOS

On growth of spinodal instabilities in nuclear matter-II:asymmetric matter

As an extension of our previous work, the growth of density fluctuations in the spinodal region of charge asymmetric nuclear matter is investigated in the basis of the stochastic mean-field approach in the non-relativistic framework. A complete treatment of density correlation functions are presented by including collective modes and non-collective modes as well.Comment: 20 pages, 6 figures, Accepted by Physical Review

A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholder

A New Model for Crowdsourcing Innovation

On paper, crowdsourced innovation makes a lot of sense: If two heads are better than one, why not 20,000? Surely, some of those outsiders will have fresh solutions to your problem. But in practice, such programs have often not worked out as well as hoped. More often than not, even the best crowdsourced ideas disappear in a Bermuda Triangle of logistical difficulties, internal politics, and professional insecurity. The International Committee of the Red Cross has developed a new collaborative approach to crowdsourcing ideas that limits the competition to teams. It designed its Enable Makeathon project not only to generate good ideas of products to help people with disabilities but also to make sure those ideas reach the market

Predicted Adoption Rates of Contact Tracing App Configurations - Insights from a choice-based conjoint study with a representative sample of the UK population

Widespread adoption of a contact tracing app by the UK public is an important part of safely easing or lifting the lockdown. In this context, it is essential to understand how adoption rates are influenced by different configurations of a proposed contact tracing app. There are many implementation options that can impact app adoption. For example, which institution should be responsible for and have oversight of the app? What type of data is collected? Does it matter how long it is stored? This whitepaper provides data-driven insights into these and other questions to guide app implementation choices