On classes of non-Gaussian asymptotic minimizers in entropic uncertainty principles

In this paper we revisit the Bialynicki-Birula & Mycielski uncertainty principle and its cases of equality. This Shannon entropic version of the well-known Heisenberg uncertainty principle can be used when dealing with variables that admit no variance. In this paper, we extend this uncertainty principle to Renyi entropies. We recall that in both Shannon and Renyi cases, and for a given dimension n, the only case of equality occurs for Gaussian random vectors. We show that as n grows, however, the bound is also asymptotically attained in the cases of n-dimensional Student-t and Student-r distributions. A complete analytical study is performed in a special case of a Student-t distribution. We also show numerically that this effect exists for the particular case of a n-dimensional Cauchy variable, whatever the Renyi entropy considered, extending the results of Abe and illustrating the analytical asymptotic study of the student-t case. In the Student-r case, we show numerically that the same behavior occurs for uniformly distributed vectors. These particular cases and other ones investigated in this paper are interesting since they show that this asymptotic behavior cannot be considered as a "Gaussianization" of the vector when the dimension increases

On some entropy functionals derived from R\'enyi information divergence

We consider the maximum entropy problems associated with R\'enyi $Q$-entropy, subject to two kinds of constraints on expected values. The constraints considered are a constraint on the standard expectation, and a constraint on the generalized expectation as encountered in nonextensive statistics. The optimum maximum entropy probability distributions, which can exhibit a power-law behaviour, are derived and characterized. The R\'enyi entropy of the optimum distributions can be viewed as a function of the constraint. This defines two families of entropy functionals in the space of possible expected values. General properties of these functionals, including nonnegativity, minimum, convexity, are documented. Their relationships as well as numerical aspects are also discussed. Finally, we work out some specific cases for the reference measure $Q(x)$ and recover in a limit case some well-known entropies

Food insecurity and mental health during the COVID-19 pandemic in cystic fibrosis households.

BACKGROUND: The COVID-19 pandemic impacted many households due to shelter-in-place orders and economic hardship. People with cystic fibrosis (CF) experienced increased food insecurity compared to the general population before the pandemic, even though adequate food access is needed to maintain nutrition goals associated with improved health-related outcomes. Little is known about the impact the pandemic had on the food insecurity of people with CF and their families. OBJECTIVE: To investigate how the COVID-19 pandemic impacted food insecurity, mental health, and self-care in people with CF. METHODS: Adults with CF and parents/guardians of children with CF were recruited via social media to complete online questionnaires from May 2020 to February 2021. Questionnaires in English and Spanish included USDA 2-question food insecurity screening, Patient Health Questionnaire-4 for mental health screening, and directed questions on the impact of the pandemic. RESULTS: Of 372 respondents, 21.8% of the households experienced food insecurity during the pandemic compared to 18.8% prepandemic (p < .001). More food insecure patients with CF reported weight loss (32.1% vs. 13.1%, p < .001), worse airway clearance adherence (13.6% vs. 5.8%, p < .01), and worse medication adherence (12.4% vs. 1.7%, p < .01) compared to food secure patients. Food insecure subjects were more likely to have an abnormal mental health screen compared to food secure subjects (53.1% vs. 16.2%, p < .001). CONCLUSION: Food insecurity increased in the CF population during the COVID-19 pandemic. Food insecure subjects reported worse mental health and self-care during the pandemic compared to food secure subjects

Time to be blunt: Substance use in cystic fibrosis.

BACKGROUND: As the population of people with cystic fibrosis (pwCF) continues to age, attention is shifting towards addressing the unique challenges teenagers and adults face, including substance use. Changing attitudes and legality regarding marijuana and cannabidiol (CBD) may influence their use among pwCF, but data on the rate of use, reasons for use, and administration methods are lacking. OBJECTIVE: Investigate marijuana, CBD, e-cigarette, and cigarette usage among pwCF and explore differences in demographics, disease severity, and cystic fibrosis transmembrane receptor (CFTR) modulator use between recent users and nonusers. METHODS: This cross-sectional study used a one-time electronic survey to assess marijuana, CBD, e-cigarette, and cigarette use in pwCF aged >13 years. Demographic and clinical characteristics were compared between recent users and nonusers. The association between recent substance use and CFTR modulator use was analyzed using logistic regressions. RESULTS: Among 226 participants, 29% used marijuana, 22% used CBD, 27% used e-cigarettes, and 22% used cigarettes in the last 12 months. Users of all substances were more likely to be college-educated or aged 29-39 years than nonusers. E-cigarette users were 2.9 times more likely to use CFTR modulators (95% confidence interval [95% CI]: 0.98-11.00, p = .08) and marijuana users were 2.5 times more likely to use CFTR modulators compared to nonusers, adjusted for confounders. CBD, e-cigarettes, and cigarettes users were more likely to have an abnormal mental health screen compared to nonusers. A high proportion of never-users of marijuana and CBD expressed interest in using. CONCLUSION: Substance use is more prevalent among pwCF than previously reported and needs to be addressed by healthcare providers

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them. OBJECTIVES: To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee. EVIDENCE ACQUISITION: The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. EVIDENCE SYNTHESIS: There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation. PATIENT SUMMARY: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training

Entropic graphs for image registration

Given 2D or 3D images gathered via multiple sensors located at different positions, the multi-sensor image registration problem is to align the images so that they have an identical pose in a common coordinate system. Image registration methods depend crucially upon a robust image similarity measure to guide the image alignment. This thesis concerns itself with a new class of such similarity measures. The launching point of this thesis is the entropic graph based estimate of Renyi's alpha-entropy developed by Ma for image registration. This thesis extends this initial work to develop other entropic graph-based divergence measures to be used with advanced higher dimensional features. A detailed analysis of entropic graphs is followed by a demonstration of their performance advantages relative to conventional similarity measures. This thesis introduces techniques to extend image registration to higher dimension feature spaces using Renyi's generalized alpha-entropy. The alpha-entropy is estimated directly through continuous quasi-additive power-weighted graphs such as the minimal spanning tree (MST) and k-Nearest Neighbor graph (kNN). Entropic graph methods are further used to approximate similarity measures like the alpha-mutual information, non-linear correlation coefficient, alpha-Jensen divergence, Henze-Penrose affinity and Geometric-Arithmetic mean affinity. Entropic-graph similarity measures are applied to problems in breast Ultrasound image registration for cancer management, geo-stationary satellite registration, feature clustering and classification and for atlas based multi-image registration. This last work is a novel and significant application of divergence estimation for registering several images simultaneously. These similarity measures offer robust registration benefits in a multisensor environment. Higher dimensional features used for this work include basis functions like multidimensional wavelets, independent component analysis (ICA) and discrete cosine transforms.Ph.D.Applied SciencesBiomedical engineeringHealth and Environmental SciencesMedical imagingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124899/2/3163898.pd

SimVenture -- A Start-Up Business Simulation

"SimVenture is a business start-up simulation. It focus-es on the first 3 years of a business and deals with issues that an entrepreneur who starts a business from scratch would face. SimVenture offers a tool called Scenarios, which al-lows the user to build different business situations. Partici-pants will be able to experience different starting points including business start up, cash flow crisis and growing pains - representing varied parts of a business. It is a software simulation that can be used in-class or individually with written direction from the tutor.