15,788 research outputs found
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Although fully generative models have been successfully used to model the
contents of text documents, they are often awkward to apply to combinations of
text data and document metadata. In this paper we propose a
Dirichlet-multinomial regression (DMR) topic model that includes a log-linear
prior on document-topic distributions that is a function of observed features
of the document, such as author, publication venue, references, and dates. We
show that by selecting appropriate features, DMR topic models can meet or
exceed the performance of several previously published topic models designed
for specific data.Comment: Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008
Optimal Information Retrieval with Complex Utility Functions
Existing retrieval models all attempt to optimize one single utility function, which is often based on the topical relevance of a document with respect to a query. In real applications, retrieval involves more complex utility functions that may involve preferences on several different dimensions. In this paper, we present a general optimization framework for retrieval with complex utility functions. A query language is designed according to this framework to enable users to submit complex queries. We propose an efficient algorithm for retrieval with complex utility functions based on the a-priori algorithm. As a case study, we apply our algorithm to a complex utility retrieval problem in distributed IR. Experiment results show that our algorithm allows for flexible tradeoff between multiple retrieval criteria. Finally, we study the efficiency issue of our algorithm on simulated data
Vesicular systems for delivering conventional small organic molecules and larger macromolecules to and through human skin
The history of using vesicular systems for drug delivery to and through skin started nearly three decades ago with a study utilizing phospholipid liposomes to improve skin deposition and reduce systemic effects of triamcinolone acetonide. Subsequently, many researchers evaluated liposomes with respect to skin delivery, with the majority of them recording localized effects and relatively few studies showing transdermal delivery effects. Shortly after this, Transfersomes were developed with claims about their ability to deliver their payload into and through the skin with efficiencies similar to subcutaneous administration. Since these vesicles are ultradeformable, they were thought to penetrate intact skin deep enough to reach the systemic circulation. Their mechanisms of action remain controversial with diverse processes being reported. Parallel to this development, other classes of vesicles were produced with ethanol being included into the vesicles to provide flexibility (as in ethosomes) and vesicles were constructed from surfactants and cholesterol (as in niosomes). Thee ultradeformable vesicles showed variable efficiency in delivering low molecular weight and macromolecular drugs. This article will critically evaluate vesicular systems for dermal and transdermal delivery of drugs considering both their efficacy and potential mechanisms of action
On the Bayes-optimality of F-measure maximizers
The F-measure, which has originally been introduced in information retrieval,
is nowadays routinely used as a performance metric for problems such as binary
classification, multi-label classification, and structured output prediction.
Optimizing this measure is a statistically and computationally challenging
problem, since no closed-form solution exists. Adopting a decision-theoretic
perspective, this article provides a formal and experimental analysis of
different approaches for maximizing the F-measure. We start with a Bayes-risk
analysis of related loss functions, such as Hamming loss and subset zero-one
loss, showing that optimizing such losses as a surrogate of the F-measure leads
to a high worst-case regret. Subsequently, we perform a similar type of
analysis for F-measure maximizing algorithms, showing that such algorithms are
approximate, while relying on additional assumptions regarding the statistical
distribution of the binary response variables. Furthermore, we present a new
algorithm which is not only computationally efficient but also Bayes-optimal,
regardless of the underlying distribution. To this end, the algorithm requires
only a quadratic (with respect to the number of binary responses) number of
parameters of the joint distribution. We illustrate the practical performance
of all analyzed methods by means of experiments with multi-label classification
problems
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