Copula Density Estimation by Total Variation Penalized Likelihood with Linear Equality Constraints

A copula density is the joint probability density function (PDF) of a random vector with uniform marginals. An approach to bivariate copula density estimation is introduced that is based on a maximum penalized likelihood estimation (MPLE) with a total variation (TV) penalty term. The marginal unity and symmetry constraints for copula density are enforced by linear equality constraints. The TV-MPLE subject to linear equality constraints is solved by an augmented Lagrangian and operator-splitting algorithm. It offers an order of magnitude improvement in computational efficiency over another TV-MPLE method without constraints solved by log-barrier method for second order cone program. A data-driven selection of the regularization parameter is through K-fold cross-validation (CV). Simulation and real data application show the effectiveness of the proposed approach. The MATLAB code implementing the methodology is available online

Supporting Scholarly Research Ideation through Web Semantics

We develop new methods and technologies for supporting scholarly research ideation, the tasks in which researchers develop new ideas for their work, through web semantics, computational representations of information found on the web, capturing meaning involving people’s experiences of things of interest. To do so, we first conducted a qualitative study with established researchers on their practices, using sensitizing concepts from information science, creative cognition, and art as a basis for framing and deriving findings. We found that participants engage in and combine a wide range of activities, including citation chaining, exploratory browsing, and curation, to achieve their goals of creative ideation. We derived a new, interdisciplinary model to depict their practices. Our study and findings address a gap in existing research: the creative nature of what researchers do has been insufficiently investigated. The model is expected to guide future investigations. We then use in-context presentations of dynamically extracted semantic information to (1) address the issues of digression and disorientation, which arise in citation chaining and exploratory browsing, and (2) provide contextual information in researchers’ prior work curation. The implemented interface, Metadata In-Context Explorer (MICE), maintains context while allowing new information to be brought into and integrated with the current context, reducing the needs for switching between documents and webpages. Study shows that MICE supports participants in their citation chaining processes, thus supports scholarly research ideation. MICE is implemented with BigSemantics, a metadata type system and runtime integrating data models, extraction rules, and presentation hints into types. BigSemantics operationalizes type-specific, dynamic extraction and rich presentation of semantic information (a.k.a. metadata) found on the web. The metadata type system, runtime, and MICE are expected to help build interfaces supporting dynamic exploratory search, browsing, and other creative tasks involving complex and interlinked semantics