Learning Boolean Halfspaces with Small Weights from Membership Queries

We consider the problem of proper learning a Boolean Halfspace with integer weights $\{0,1,\ldots,t\}$ from membership queries only. The best known algorithm for this problem is an adaptive algorithm that asks $n^{O(t^5)}$ membership queries where the best lower bound for the number of membership queries is $n^t$ [Learning Threshold Functions with Small Weights Using Membership Queries. COLT 1999] In this paper we close this gap and give an adaptive proper learning algorithm with two rounds that asks $n^{O(t)}$ membership queries. We also give a non-adaptive proper learning algorithm that asks $n^{O(t^3)}$ membership queries

65+ Membership Scheme

This report shares the journey of three partners' experiment with digital technology to address societal challenges linked to ageing and social isolation. In recent years artsdepot has seen enormous changes to its business model driven by the advance of digital communication tools, rapidly increasing use of social media, and changing consumer behaviours. The project team felt that digital innovation was typically aimed at the young but wondered if there was greater potential to increase attendance and sales while also addressing social isolation through creating digital innovations with older people. A range of partners worked on the project: artsdepot (arts partner and project lead) is a multi-art form venue based in North Finchley welcoming 130,000 audience members annually. DigiLab (research partner) is an R&D laboratory based at London College of Communication, University of the Arts London. Ingelby (tech partner) is a digital agency specialising in app building and mobile app development, as well as mobile website design , e-commerce development , customised CRM systems and creative digital media. This project aimed to explore digital technologies that could help create increased levels of arts attendance among older people by identifying barriers and designing digital solutions. artsdepot felt that by enabling increased arts attendance and therefore facilitating opportunities to socialise, they might help establish new friendships while providing enriching creative experiences. Additionally, the team felt there was an additional benefit in terms of demonstrating an economic model for engaging older audiences. Having learnt in the application phase that two thirds of older people have more disposable income than any other age group, artsdepot felt that if it could address other barriers to attendance, it could also improve ticket sales and build a case for increased focus on older audiences

Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201

State Union Membership, 2012

On January 23, the Bureau of Labor Statistics (BLS) released its estimates for union membership in the United States in 2012. This issue brief focuses on the union membership numbers by state. In addition to presenting the BLS estimates for overall union membership in each state, we also provide our own breakdown of state union membership in the private and public sector

Multidimensional Membership Mixture Models

We present the multidimensional membership mixture (M3) models where every dimension of the membership represents an independent mixture model and each data point is generated from the selected mixture components jointly. This is helpful when the data has a certain shared structure. For example, three unique means and three unique variances can effectively form a Gaussian mixture model with nine components, while requiring only six parameters to fully describe it. In this paper, we present three instantiations of M3 models (together with the learning and inference algorithms): infinite, finite, and hybrid, depending on whether the number of mixtures is fixed or not. They are built upon Dirichlet process mixture models, latent Dirichlet allocation, and a combination respectively. We then consider two applications: topic modeling and learning 3D object arrangements. Our experiments show that our M3 models achieve better performance using fewer topics than many classic topic models. We also observe that topics from the different dimensions of M3 models are meaningful and orthogonal to each other.Comment: 9 pages, 7 figure

Experimental Evidence for Quantum Structure in Cognition

We proof a theorem that shows that a collection of experimental data of membership weights of items with respect to a pair of concepts and its conjunction cannot be modeled within a classical measure theoretic weight structure in case the experimental data contain the effect called overextension. Since the effect of overextension, analogue to the well-known guppy effect for concept combinations, is abundant in all experiments testing weights of items with respect to pairs of concepts and their conjunctions, our theorem constitutes a no-go theorem for classical measure structure for common data of membership weights of items with respect to concepts and their combinations. We put forward a simple geometric criterion that reveals the non classicality of the membership weight structure and use experimentally measured membership weights estimated by subjects in experiments to illustrate our geometrical criterion. The violation of the classical weight structure is similar to the violation of the well-known Bell inequalities studied in quantum mechanics, and hence suggests that the quantum formalism and hence the modeling by quantum membership weights can accomplish what classical membership weights cannot do.Comment: 12 pages, 3 figure

Efficient asynchronous accumulators for distributed PKI

Cryptographic accumulators are a tool for compact set representation and secure set membership proofs. When an element is added to a set by means of an accumulator, a membership witness is generated. This witness can later be used to prove the membership of the element. Typically, the membership witness has to be synchronized with the accumulator value, and to be updated every time another element is added to the accumulator. In this work we propose an accumulator that, unlike any prior scheme, does not require strict synchronization. In our construction a membership witness needs to be updated only a logarithmic number of times in the number of subsequent element additions. Thus, an out-of-date witness can be easily made current. Vice versa, a verifier with an out-of-date accumulator value can still verify a current membership witness. These properties make our accumulator construction uniquely suited for use in distributed applications, such as blockchain-based public key infrastructures

Negotiating the membership

In cooperative games in which the players are partitioned into groups, we study the incentives of the members of a group to leave it and become singletons. In this context, we model a non-cooperative mechanism in which each player has to decide whether to stay in his group or to exit and act as a singleton. We show that players, acting myopically, always reach a Nash equilibrium.Cooperative game, coalition structure, Owen value, Nash equilibrium