890 research outputs found
If Fed becomes super regulator, politicians would be its kryptonite
If given broader regulatory authority, the Fed will probably face new challenges in executing its traditional responsibilities and in preserving its independence against political pressure.Risk management ; Federal Reserve System ; Federal Reserve System - Independence
The Federal Reserve seeks to protect consumers
The Federal Reserve plays a key role in protecting consumers when they seek financial services. That role revolves around four pillars: rulemaking, enforcement, community affairs and consumer education.Consumer protection
Combining Thesaurus Knowledge and Probabilistic Topic Models
In this paper we present the approach of introducing thesaurus knowledge into
probabilistic topic models. The main idea of the approach is based on the
assumption that the frequencies of semantically related words and phrases,
which are met in the same texts, should be enhanced: this action leads to their
larger contribution into topics found in these texts. We have conducted
experiments with several thesauri and found that for improving topic models, it
is useful to utilize domain-specific knowledge. If a general thesaurus, such as
WordNet, is used, the thesaurus-based improvement of topic models can be
achieved with excluding hyponymy relations in combined topic models.Comment: Accepted to AIST-2017 conference (http://aistconf.ru/). The final
publication will be available at link.springer.co
Exploratory Analysis of Highly Heterogeneous Document Collections
We present an effective multifaceted system for exploratory analysis of
highly heterogeneous document collections. Our system is based on intelligently
tagging individual documents in a purely automated fashion and exploiting these
tags in a powerful faceted browsing framework. Tagging strategies employed
include both unsupervised and supervised approaches based on machine learning
and natural language processing. As one of our key tagging strategies, we
introduce the KERA algorithm (Keyword Extraction for Reports and Articles).
KERA extracts topic-representative terms from individual documents in a purely
unsupervised fashion and is revealed to be significantly more effective than
state-of-the-art methods. Finally, we evaluate our system in its ability to
help users locate documents pertaining to military critical technologies buried
deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery
and Data Minin
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to capture a larger subset of household objects by extending a state
of the art object recognition method. This leads to a significant increase in
robustness of recognition over a larger set of commonly used objects.Comment: 12 page
Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span
BACKGROUND: The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made from analysis of molecular sequence and profiling data. Here, the potential of such modeling is demonstrated by examining the 5,225 free-text items in the Caenorhabditis Genetic Center (CGC) Bibliography using techniques from statistical information retrieval. Items in the CGC biomedical text corpus were modeled using the Latent Dirichlet Allocation (LDA) model. LDA is a hierarchical Bayesian model which represents a document as a random mixture over latent topics; each topic is characterized by a distribution over words. RESULTS: An LDA model estimated from CGC items had better predictive performance than two standard models (unigram and mixture of unigrams) trained using the same data. To illustrate the practical utility of LDA models of biomedical corpora, a trained CGC LDA model was used for a retrospective study of nematode genes known to be associated with life span modification. Corpus-, document-, and word-level LDA parameters were combined with terms from the Gene Ontology to enhance the explanatory value of the CGC LDA model, and to suggest additional candidates for age-related genes. A novel, pairwise document similarity measure based on the posterior distribution on the topic simplex was formulated and used to search the CGC database for "homologs" of a "query" document discussing the life span-modifying clk-2 gene. Inspection of these document homologs enabled and facilitated the production of hypotheses about the function and role of clk-2. CONCLUSION: Like other graphical models for genetic, genomic and other types of biological data, LDA provides a method for extracting unanticipated insights and generating predictions amenable to subsequent experimental validation
Peliosis Hepatis With Intrahepatic Hemorrhage: Successful Embolization of the Hepatic Artery
Peliosis hepatis is defined as the appearance of
blood filled lakes in the hepatic parenchyma. It has
been associated with various pharmacological
agents and infections. Treatment has been primarily
symptomatic and includes discontinuation of offending
medications, partial hepatectomy or occasionally
liver transplantation. We report a 58 year
old white female on hormone replacement therapy
who developed symptomatic peliosis hepatis and
underwent successful superselective hepatic artery
embolization with control of bleeding
Using topic information to improve non-exact keyword-based search for mobile applications
Considering the wide offer of mobile applications available nowadays, effective search engines are imperative for an user to find applications that provide a specific desired functionality. Retrieval approaches that leverage topic similarity between queries and applications have shown promising results in previous studies. However, the search engines used by most app stores are based on keyword-matching and boosting. In this paper, we explore means to include topic information in such approaches, in order to improve their ability to retrieve relevant applications for non-exact queries, without impairing their computational performance. More specifically, we create topic models specialized on application descriptions and explore how the most relevant terms for each topic covered by an application can be used to complement the information provided by its description. Our experiments show that, although these topic keywords are not able to provide all the information of the topic model, they provide a sufficiently informative summary of the top- ics covered by the descriptions, leading to improved performance.info:eu-repo/semantics/publishedVersio
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