219,269 research outputs found
Global existence and blow-up of solutions for a general class of doubly dispersive nonlocal nonlinear wave equations
This study deals with the analysis of the Cauchy problem of a general class
of nonlocal nonlinear equations modeling the bi-directional propagation of
dispersive waves in various contexts. The nonlocal nature of the problem is
reflected by two different elliptic pseudodifferential operators acting on
linear and nonlinear functions of the dependent variable, respectively. The
well-known doubly dispersive nonlinear wave equation that incorporates two
types of dispersive effects originated from two different dispersion operators
falls into the category studied here. The class of nonlocal nonlinear wave
equations also covers a variety of well-known wave equations such as various
forms of the Boussinesq equation. Local existence of solutions of the Cauchy
problem with initial data in suitable Sobolev spaces is proven and the
conditions for global existence and finite-time blow-up of solutions are
established.Comment: 17 page
Characterizing lab instructors' self-reported learning goals to inform development of an experimental modeling skills assessment
The ability to develop, use, and refine models of experimental systems is a
nationally recognized learning outcome for undergraduate physics lab courses.
However, no assessments of students' model-based reasoning exist for
upper-division labs. This study is the first step toward development of
modeling assessments for optics and electronics labs. In order to identify test
objectives that are likely relevant across many institutional contexts, we
interviewed 35 lab instructors about the ways they incorporate modeling in
their course learning goals and activities. The study design was informed by
the Modeling Framework for Experimental Physics. This framework conceptualizes
modeling as consisting of multiple subtasks: making measurements, constructing
system models, comparing data to predictions, proposing causes for
discrepancies, and enacting revisions to models or apparatus. We found that
each modeling subtask was identified by multiple instructors as an important
learning outcome for their course. Based on these results, we argue that test
objectives should include probing students' competence with most modeling
subtasks, and test items should be designed to elicit students' justifications
for choosing particular modeling pathways. In addition to discussing these and
other implications for assessment, we also identify future areas of research
related to the role of modeling in optics and electronics labs.Comment: 24 pages, 2 figures, 5 tables; submitted to Phys. Rev. PE
Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation
Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
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