With the financial crises ongoing in Greece and Venezuela, sovereign debt crises have become more and more prominent in the public eye. Thus, it has become important to be able to predict when nations will enter such debt crises. We collected publicly available data in order to train models to predict, given a nation’s economic status in one year, whether they would be in a debt crisis the next...

We consider the random walk among random conductances on Z. We assume that the conductances are independent, identically distributed and uniformly bounded away from 0 and infinity. We obtain a quantitative version of the central limit theorem for this random walk, which takes the form of a Berry-Esseen estimate with speed t−1/10 for d 6 2, and speed t−1/5 for d > 3, up to logarithmic corrections.

In this work we study variable-significance in classification using the Random Forest proximity matrix and local Importance matrix. We use the proximity m atrix t o g roup t he s amples acr oss a number of c lusters a nd use t hese clusters to s tratify th e importance of a variable. We apply t his a pproach t o a cardiovascular g enotype d ataset f or sample classification b ased o n coronary ...

Financial crime is a rampant but hidden threat. In spite of this, predictive policing systems disproportionately target “street crime” rather than white collar crime. This paper presents the White Collar Crime Early Warning System (WCCEWS), a white collar crime predictive model that uses random forest classifiers to identify high risk zones for incidents of financial crime.

We describe a method for human parsing that is straightforward and competes with state-of-the-art performance on standard datasets. Unlike the state-of-the-art, our method does not search for individual body parts or poselets. Instead, a regression forest is used to predict a body configuration in body-space. The output of this regression forest is then combined in a novel way. Instead of avera...

In the Life Sciences 'omics' data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques al...

AIMS This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. MATERIALS AND METHODS We used data from 5,646 adults aged between 18-7...

OBJECTIVE To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for...

Automatic recognition of multiple musical instruments in polyphonic and polytimbral music is a difficult task, but often attempted to perform by MIR researchers recently. In papers published so far, the proposed systems were validated mainly on audio data obtained through mixing of isolated sounds of musical instruments. This paper tests recognition of instruments in real recordings, using a re...