Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape

Local Causal States and Discrete Coherent Structures

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht

Impact of Fuel Metal Impurities on the Durability of a Light-Duty Diesel Aftertreatment System

Alkali and alkaline earth metal impurities found in diesel fuels are potential poisons for diesel exhaust catalysts. A set of diesel engine production exhaust systems was aged to 150,000 miles. These exhaust systems included a diesel oxidation catalyst, selective catalytic reduction (SCR) catalyst, and diesel particulate filter (DPF). Four separate exhaust systems were aged, each with a different fuel: ultralow sulfur diesel containing no measureable metals, B20 (a common biodiesel blend) containing sodium, B20 containing potassium, and B20 containing calcium, which were selected to simulate the maximum allowable levels in B100 according to ASTM D6751. Analysis included Federal Test Procedure emissions testing, bench-flow reactor testing of catalyst cores, electron probe microanalysis (EPMA), and measurement of thermo-mechanical properties of the DPFs. EPMA imaging found that the sodium and potassium penetrated into the washcoat, while calcium remained on the surface. Bench-flow reactor experiments were used to measure the standard nitrogen oxide (NOx) conversion, ammonia storage, and ammonia oxidation for each of the aged SCR catalysts. Vehicle emissions tests were conducted with each of the aged catalyst systems using a chassis dynamometer. The vehicle successfully passed the 0.2 gram/mile NOx emission standard with each of the four aged exhaust systems