44,345 research outputs found
The Discriminant Analysis Used by the IRS to Predict Profitable Individual Tax Return Audits
This paper discusses past and current methods the IRS uses to determine which individual income tax returns to audit. The IRS currently uses the discriminant function to give all individual tax returns two scores; one based on whether it should be audited or not and one based on if the return is likely to have unreported income. The discriminant function is determined by the IRS’s National Research Program, which takes a sample of returns and ensures their accuracy. Previously, the function was determined by the IRS’s Taxpayer Compliance Measurement Program. However, this was too burdensome and time consuming for taxpayers. The data mining techniques of decision trees, regression, and neural networks were researched to determine if the IRS should change its method. Unfortunately IRS tax data were not obtainable due to their confidentiality; therefore credit data from a German bank was used to compare discriminant analysis results to the three new methods. All of the methods were run to predict creditworthiness and were compared based on misclassification rates. The neural network had the best classification rate closely followed by regression, the decision tree, and then discriminant analysis. Since this comparison is not based on IRS tax data, no conclusion can be made whether the IRS should change its method or not, but because all methods had very close classification rates, it would be worthwhile for the IRS to look into them
First results from the CRESST-III low-mass dark matter program
The CRESST experiment is a direct dark matter search which aims to measure
interactions of potential dark matter particles in an earth-bound detector.
With the current stage, CRESST-III, we focus on a low energy threshold for
increased sensitivity towards light dark matter particles. In this manuscript
we describe the analysis of one detector operated in the first run of
CRESST-III (05/2016-02/2018) achieving a nuclear recoil threshold of 30.1eV.
This result was obtained with a 23.6g CaWO crystal operated as a cryogenic
scintillating calorimeter in the CRESST setup at the Laboratori Nazionali del
Gran Sasso (LNGS). Both the primary phonon/heat signal and the simultaneously
emitted scintillation light, which is absorbed in a separate
silicon-on-sapphire light absorber, are measured with highly sensitive
transition edge sensors operated at ~15mK. The unique combination of these
sensors with the light element oxygen present in our target yields sensitivity
to dark matter particle masses as low as 160MeV/c.Comment: 9 pages, 9 figure
From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
Cascades are ubiquitous in various network environments. How to predict these
cascades is highly nontrivial in several vital applications, such as viral
marketing, epidemic prevention and traffic management. Most previous works
mainly focus on predicting the final cascade sizes. As cascades are typical
dynamic processes, it is always interesting and important to predict the
cascade size at any time, or predict the time when a cascade will reach a
certain size (e.g. an threshold for outbreak). In this paper, we unify all
these tasks into a fundamental problem: cascading process prediction. That is,
given the early stage of a cascade, how to predict its cumulative cascade size
of any later time? For such a challenging problem, how to understand the micro
mechanism that drives and generates the macro phenomenons (i.e. cascading
proceese) is essential. Here we introduce behavioral dynamics as the micro
mechanism to describe the dynamic process of a node's neighbors get infected by
a cascade after this node get infected (i.e. one-hop subcascades). Through
data-driven analysis, we find out the common principles and patterns lying in
behavioral dynamics and propose a novel Networked Weibull Regression model for
behavioral dynamics modeling. After that we propose a novel method for
predicting cascading processes by effectively aggregating behavioral dynamics,
and propose a scalable solution to approximate the cascading process with a
theoretical guarantee. We extensively evaluate the proposed method on a large
scale social network dataset. The results demonstrate that the proposed method
can significantly outperform other state-of-the-art baselines in multiple tasks
including cascade size prediction, outbreak time prediction and cascading
process prediction.Comment: 10 pages, 11 figure
- …