Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Cystic fibrosis mice carrying the missense mutation G551D replicate human genotype phenotype correlations

We have generated a mouse carrying the human G551D mutation in the cystic fibrosis transmembrane conductance regulator gene (CFTR) by a one-step gene targeting procedure. These mutant mice show cystic fibrosis pathology but have a reduced risk of fatal intestinal blockage compared with 'null' mutants, in keeping with the reduced incidence of meconium ileus in G551D patients. The G551D mutant mice show greatly reduced CFTR-related chloride transport, displaying activity intermediate between that of cftr(mlUNC) replacement ('null') and cftr(mlHGU) insertional (residual activity) mutants and equivalent to approximately 4% of wild-type CFTR activity. The long-term survival of these animals should provide an excellent model with which to study cystic fibrosis, and they illustrate the value of mouse models carrying relevant mutations for examining genotype-phenotype correlations

Parametric Evaluation of Racking Performance of Platform Timber Framed Walls

This paper provides a quantitative assessment of the racking performance of partially anchored timber framed walls, based on experimental tests. A total of 17 timber framed wall specimens, constructed from a combination of materials under different load configurations, were tested. The experimental study was designed toexamine the influence of a range of geometrical parameters, such as fastener size and spacings, wall length, arrangement of studs and horizontal members, as well as the effect of vertical loading on the racking strength and stiffness of the walls. The experimental results were then compared with results obtained from design rules,as given in the relevant European standards, to determine the racking performance of the walls, and are discussed in the paper

Revisiting Clifford algebras and spinors III: conformal structures and twistors in the paravector model of spacetime

This paper is the third of a series of three, and it is the continuation of math-ph/0412074 and math-ph/0412075. After reviewing the conformal spacetime structure, conformal maps are described in Minkowski spacetime as the twisted adjoint representation of the group Spin_+(2,4), acting on paravectors. Twistors are then presented via the paravector model of Clifford algebras and related to conformal maps in the Clifford algebra over the lorentzian R{4,1}$ spacetime. We construct twistors in Minkowski spacetime as algebraic spinors associated with the Dirac-Clifford algebra Cl(1,3)(C) using one lower spacetime dimension than standard Clifford algebra formulations, since for this purpose the Clifford algebra over R{4,1} is also used to describe conformal maps, instead of R{2,4}. Although some papers have already described twistors using the algebra Cl(1,3)(C), isomorphic to Cl(4,1), the present formulation sheds some new light on the use of the paravector model and generalizations.Comment: 17 page

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorization algorithm using stochastic gradient MCMC. Our algorithm, based on Distributed Stochastic Gradient Langevin Dynamics, can not only match the prediction accuracy of standard MCMC methods like Gibbs sampling, but at the same time is as fast and simple as stochastic gradient descent. In our experiments, we show that our algorithm can achieve the same level of prediction accuracy as Gibbs sampling an order of magnitude faster. We also show that our method reduces the prediction error as fast as distributed stochastic gradient descent, achieving a 4.1% improvement in RMSE for the Netflix dataset and an 1.8% for the Yahoo music dataset

Racking performance of Platform timber framed walls assessed by rigid body relaxation technique

A new method to assess the raking performance of Platform timber framed walls, is provided in this study: each component of the unit wall assembly is assumed as rigid, hence allowing to drastically reduce the overall number of DoFs involved within the model. The timber frame in particular, is modelled as a mechanism, having only two DoFs (regardless of the number of studs) corresponding to the horizontal and rotational displacements of the header beam. For a given imposed horizontal displacement ∆_h , the corresponding racking load P (∆_h) is computed by numerical relaxation, allowing to consider a continuous function to represent the load-slip curves of the connections. A comparison of the numerical analysis against laboratory test results is provided, showing the method's capability in predicting the raking strength of the wall, despite the assumed reduced number of DoFs

On the Decomposition of Clifford Algebras of Arbitrary Bilinear Form

Clifford algebras are naturally associated with quadratic forms. These algebras are Z_2-graded by construction. However, only a Z_n-gradation induced by a choice of a basis, or even better, by a Chevalley vector space isomorphism Cl(V) \bigwedge V and an ordering, guarantees a multi-vector decomposition into scalars, vectors, tensors, and so on, mandatory in physics. We show that the Chevalley isomorphism theorem cannot be generalized to algebras if the Z_n-grading or other structures are added, e.g., a linear form. We work with pairs consisting of a Clifford algebra and a linear form or a Z_n-grading which we now call 'Clifford algebras of multi-vectors' or 'quantum Clifford algebras'. It turns out, that in this sense, all multi-vector Clifford algebras of the same quadratic but different bilinear forms are non-isomorphic. The usefulness of such algebras in quantum field theory and superconductivity was shown elsewhere. Allowing for arbitrary bilinear forms however spoils their diagonalizability which has a considerable effect on the tensor decomposition of the Clifford algebras governed by the periodicity theorems, including the Atiyah-Bott-Shapiro mod 8 periodicity. We consider real algebras Cl_{p,q} which can be decomposed in the symmetric case into a tensor product Cl_{p-1,q-1} \otimes Cl_{1,1}. The general case used in quantum field theory lacks this feature. Theories with non-symmetric bilinear forms are however needed in the analysis of multi-particle states in interacting theories. A connection to q-deformed structures through nontrivial vacuum states in quantum theories is outlined.Comment: 25 pages, 1 figure, LaTeX, {Paper presented at the 5th International Conference on Clifford Algebras and their Applications in Mathematical Physics, Ixtapa, Mexico, June 27 - July 4, 199

It is Hobbes, not Rousseau:an experiment on voting and redistribution

We perform an experiment which provides a laboratory replica of some important features of the welfare state. In the experiment, all individuals in a group decide whether to make a costly effort, which produces a random (independent) outcome for each one of them. The group members then vote on whether to redistribute the resulting and commonly known total sum of earnings equally amongst themselves. This game has two equilibria, if played once. In one of them, all players make effort and there is little redistribution. In the other one, there is no effort and nothingWe thank Iris Bohnet, Tim Cason, David Cooper, John Duffy, Maia Guell, John Van Huyck and Robin Mason for helpful conversations and encouragement. The comments of the Editor and two referees helped improve the paper. We gratefully acknowledge the financial support from Spain’s Ministry of Science and Innovation under grants CONSOLIDER INGENIO 2010 CSD2006-0016 (all authors), ECO2009-10531 (Cabrales), ECO2008-01768 (Nagel) and the Comunidad de Madrid under grant Excelecon (Cabrales), the Generalitat de Catalunya and the CREA program (Nagel), and project SEJ2007-64340 of Spain’s Ministerio de Educación y Ciencia (Rodríguez Mora).Publicad