A linear domain decomposition method for partially saturated flow in porous media

The Richards equation is a nonlinear parabolic equation that is commonly used for modelling saturated/unsaturated flow in porous media. We assume that the medium occupies a bounded Lipschitz domain partitioned into two disjoint subdomains separated by a fixed interface $\Gamma$. This leads to two problems defined on the subdomains which are coupled through conditions expressing flux and pressure continuity at $\Gamma$. After an Euler implicit discretisation of the resulting nonlinear subproblems a linear iterative ($L$-type) domain decomposition scheme is proposed. The convergence of the scheme is proved rigorously. In the last part we present numerical results that are in line with the theoretical finding, in particular the unconditional convergence of the scheme. We further compare the scheme to other approaches not making use of a domain decomposition. Namely, we compare to a Newton and a Picard scheme. We show that the proposed scheme is more stable than the Newton scheme while remaining comparable in computational time, even if no parallelisation is being adopted. Finally we present a parametric study that can be used to optimize the proposed scheme.Comment: 34 pages, 13 figures, 7 table

Challenges in Knowledge Management

Knowledge management is an ever researched area in the discipline of information systems. Though the terminology might change with the waves of fashion, how information systems can support the multiple dimensions of knowledge management is an underlying theme in many streams of research. This article examines literature on knowledge management in order to synthesize a number of key challenges, which emerge from a multidimensional and boundary-spanning view on knowledge management. Six interrelated issues attempt to explain some of the essential aspects of knowledge in the organizational context: these issues are (1) standardization of processes, structures, and systems, (2) contextualization, (3) invasiveness in natural ways of working, (4) strategic alignment, (5) intelligence, and (6) cultural environment

Are we contributing? The who, when, where, and what of the Blockchain Research Landscape

The blockchain technology discourse is diverse, and diffusion is increasing. It is estimated that USD39 billion will be spent within the blockchain ecosystem by 2025. One can view this as an exciting time to be involved in technology. Or another can potentially view this as wasteful spending and exploitation of scarce resources. Additionally, projects and start-ups fail at an alarming rate, making it critical to provide tools to aid decision-makers. Current blockchain research has not yet answered what blockchain is nor what situations it is best suited to. This paper problematises the current discourse on blockchain technology through a systematic literature review using bibliometric techniques. We present blockchain research on who, when, where, and what. This research also extends the multi-discipline discourse by synthesising how blockchain technology is enacted. We present a benchmarking tool for assessing solutions. Further research topics are also presented

Fast Offline Policy Optimization for Large Scale Recommendation

Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these systems can be achieved by a relaxation of the discrete problem resulting in policy learning or REINFORCE style learning algorithms. Unfortunately, this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world examples such as large catalogue recommender systems, severely limiting the usefulness of this method in practice. In this paper, we derive an approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show that our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.Comment: Accepted at AAAI 202