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 $\sim$ 2) with the results measured on the first flight of EXITE2 from Palestine, Texas. The measured background flux at 100 keV is $\sim$ 4 $\times$ 10$^{-4}$ counts cm$^{-2}$ s$^{-1}$ keV$^{-1}$.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 $\mathcal{A}$ and $\mathcal{B}$ in a network and the nodes of profile $\mathcal{A}$ and profile $\mathcal{B}$ are susceptible to a highly spreading virus with probabilities $\beta_{\mathcal{A}}$ and $\beta_{\mathcal{B}}$ 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