Order-Revealing Encryption and the Hardness of Private Learning

An order-revealing encryption scheme gives a public procedure by which two ciphertexts can be compared to reveal the ordering of their underlying plaintexts. We show how to use order-revealing encryption to separate computationally efficient PAC learning from efficient $(\epsilon, \delta)$-differentially private PAC learning. That is, we construct a concept class that is efficiently PAC learnable, but for which every efficient learner fails to be differentially private. This answers a question of Kasiviswanathan et al. (FOCS '08, SIAM J. Comput. '11). To prove our result, we give a generic transformation from an order-revealing encryption scheme into one with strongly correct comparison, which enables the consistent comparison of ciphertexts that are not obtained as the valid encryption of any message. We believe this construction may be of independent interest.Comment: 28 page

A General Analysis of Corrections to the Standard See-saw Formula in Grand Unified Models

In realistic grand unified models there are typically extra vectorlike matter multiplets at the GUT scale that are needed to explain the family hierarchy. These contain neutrinos that, when integrated out, can modify the usual neutrino see-saw formula. A general analysis is given. It is noted that such modifications can explain why the neutrinos do not exhibit a strong family hierarchy like the other types of fermions.Comment: 30 page

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Notorious places: image, reputation, stigma: the role of newspapers in area reputations for social housing estates

This paper reviews work in several disciplines to distinguish between image, reputation and stigma. It also shows that there has been little research on the process by which area reputations are established and sustained through transmission processes. This paper reports on research into the portrayal of two social housing estates in the printed media over an extended period of time (14 years). It was found that negative and mixed coverage of the estates dominated, with the amount of positive coverage being very small. By examining the way in which dominant themes were used by newspapers in respect of each estate, questions are raised about the mode of operation of the press and the communities' collective right to challenge this. By identifying the way regeneration stories are covered and the nature of the content of positive stories, lessons are drawn for programmes of area transformation. The need for social regeneration activities is identified as an important ingredient for changing deprived-area reputations

Age-specific vaccine effectiveness of seasonal 2010/2011 and pandemic influenza A(H1N1) 2009 vaccines in preventing influenza in the United Kingdom

An analysis was undertaken to measure age-specific vaccine effectiveness (VE) of 2010/11 trivalent seasonal influenza vaccine (TIV) and monovalent 2009 pandemic influenza vaccine (PIV) administered in 2009/2010. The test-negative case-control study design was employed based on patients consulting primary care. Overall TIV effectiveness, adjusted for age and month, against confirmed influenza A(H1N1)pdm 2009 infection was 56% (95% CI 42–66); age-specific adjusted VE was 87% (95% CI 45–97) in <5-year-olds and 84% (95% CI 27–97) in 5- to 14-year-olds. Adjusted VE for PIV was only 28% (95% CI x6 to 51) overall and 72% (95% CI 15–91) in <5-year-olds. For confirmed influenza B infection, TIV effectiveness was 57% (95% CI 42–68) and in 5- to 14-year-olds 75% (95% CI 32–91). TIV provided moderate protection against the main circulating strains in 2010/2011, with higher protection in children. PIV administered during the previous season provided residual protection after 1 year, particularly in the <5 years age group

Novel therapies for children with acute myeloid leukaemia

Significant improvements in survival for children with acute myeloid leukaemia (AML) have been made over the past three decades, with overall survival rates now approximately 60-70%. However, these gains can be largely attributed to more intensive use of conventional cytotoxics made possible by advances in supportive care, and although over 90% of children achieve remission with frontline therapy, approximately one third in current protocols relapse. Furthermore, late effects of therapy cause significant morbidity for many survivors. Novel therapies are therefore desperately needed. Early-phase paediatric trials of several new agents such as clofarabine, sorafenib and gemtuzumab ozogamicin have shown encouraging results in recent years. Due to the relatively low incidence of AML in childhood, the success of paediatric early-phase clinical trials is largely dependent upon collaborative clinical trial design by international cooperative study groups. Successfully incorporating novel therapies into frontline therapy remains a challenge, but the potential for significant improvement in the duration and quality of survival for children with AML is high

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page