2,849 research outputs found
Removing the influence of a group variable in high-dimensional predictive modelling
In many application areas, predictive models are used to support or make
important decisions. There is increasing awareness that these models may
contain spurious or otherwise undesirable correlations. Such correlations may
arise from a variety of sources, including batch effects, systematic
measurement errors, or sampling bias. Without explicit adjustment, machine
learning algorithms trained using these data can produce poor out-of-sample
predictions which propagate these undesirable correlations. We propose a method
to pre-process the training data, producing an adjusted dataset that is
statistically independent of the nuisance variables with minimum information
loss. We develop a conceptually simple approach for creating an adjusted
dataset in high-dimensional settings based on a constrained form of matrix
decomposition. The resulting dataset can then be used in any predictive
algorithm with the guarantee that predictions will be statistically independent
of the group variable. We develop a scalable algorithm for implementing the
method, along with theory support in the form of independence guarantees and
optimality. The method is illustrated on some simulation examples and applied
to two case studies: removing machine-specific correlations from brain scan
data, and removing race and ethnicity information from a dataset used to
predict recidivism. That the motivation for removing undesirable correlations
is quite different in the two applications illustrates the broad applicability
of our approach.Comment: Update. 18 pages, 3 figure
Spaceborne VHSIC multiprocessor system for AI applications
A multiprocessor system, under design for space-station applications, makes use of the latest generation symbolic processor and packaging technology. The result will be a compact, space-qualified system two to three orders of magnitude more powerful than present-day symbolic processing systems
Simulations and Measurements of the Background Encountered by a High-Altitude Balloon-Borne Experiment for Hard X-ray Astronomy
We have modelled the hard X-ray background expected for a high-altitude
balloon flight of the Energetic X-ray Telescope Experiment (EXITE2), an imaging
phoswich detector/telescope for the 20--600 keV energy range. Photon and
neutron-induced contributions to the background are considered. We describe the
code and the results of a series of simulations with different shielding
configurations. The simulated hard X-ray background for the actual flight
configuration agrees reasonably well (within a factor of 2) with the
results measured on the first flight of EXITE2 from Palestine, Texas. The
measured background flux at 100 keV is 4 10 counts
cm s keV.Comment: 17 pages Latex (uses aaspp4.sty) plus 7 postscript figures: available
in file figs.tar.g
Cultural health assets of Somali and Oromo refugees and immigrants in Minnesota: Findings from a community-based participatory research project
This community-based participatory research study sought to identify the cultural health assets of the Somali and Oromo communities in one Minnesota neighborhood that could be mobilized to develop culturally appropriate health interventions. Community asset mappers conducted 76 interviews with Somali and Oromo refugees in in Minnesota regarding the cultural assets of their community. A community-university data analysis team coded data for major themes. Key cultural health assets of the Somali and Oromo refugee communities revealed in this study include religion and religious beliefs, religious and cultural practices, a strong culture of sharing, interconnectedness, the prominence of oral traditions, traditional healthy eating and healthy lifestyles, traditional foods and medicine, and a strong cultural value placed on health. These cultural health assets can be used as building blocks for culturally relevant health interventions.published_or_final_versio
The bronchodilator response in preschool children: A systematic review
BACKGROUND: The bronchodilator response (BDR) is frequently used to support diagnostic and therapeutic decision-making for children who wheeze. However, there is little evidence-based guidance describing the role of BDR testing in preschool children and it is unclear whether published cut-off values, which are derived from adult data, can be applied to this population. METHODS: We searched MEDLINE, EMBASE, Web of Science, and Cochrane databases (inception-September 2015) for studies reporting response to a bronchodilator in healthy preschool children, response following placebo inhalation, and the diagnostic efficacy of BDR compared with a clinical diagnosis of asthma/recurrent wheezing. FINDINGS: We included 14 studies. Thirteen studies provided BDR data from healthy preschool children. Two studies reported response to placebo in preschool children with asthma/recurrent wheezing. Twelve studies compared BDR measurements from preschool children with asthma/recurrent wheeze to those from healthy children and seven of these studies reported diagnostic efficacy. Significant differences between the BDR measured in healthy preschool children compared with that in children with asthma/recurrent wheeze were demonstrated in some, but not all studies. Techniques such as interrupter resistance, oscillometry, and plethysmography were more consistently successfully completed than spirometry. Between study heterogeneity precluded determination of an optimum technique. INTERPRETATION: There is little evidence to suggest spirometry-based BDR can be used in the clinical assessment of preschool children who wheeze. Further evaluation of simple alternative techniques is required. Future studies should recruit children in whom airways disease is suspected and should evaluate the ability of BDR testing to predict treatment response
Virus Propagation in Multiple Profile Networks
Suppose we have a virus or one competing idea/product that propagates over a
multiple profile (e.g., social) network. Can we predict what proportion of the
network will actually get "infected" (e.g., spread the idea or buy the
competing product), when the nodes of the network appear to have different
sensitivity based on their profile? For example, if there are two profiles
and in a network and the nodes of profile
and profile are susceptible to a highly spreading
virus with probabilities and
respectively, what percentage of both profiles will actually get infected from
the virus at the end? To reverse the question, what are the necessary
conditions so that a predefined percentage of the network is infected? We
assume that nodes of different profiles can infect one another and we prove
that under realistic conditions, apart from the weak profile (great
sensitivity), the stronger profile (low sensitivity) will get infected as well.
First, we focus on cliques with the goal to provide exact theoretical results
as well as to get some intuition as to how a virus affects such a multiple
profile network. Then, we move to the theoretical analysis of arbitrary
networks. We provide bounds on certain properties of the network based on the
probabilities of infection of each node in it when it reaches the steady state.
Finally, we provide extensive experimental results that verify our theoretical
results and at the same time provide more insight on the problem
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