Testing Uniformity of Stationary Distribution

A random walk on a directed graph gives a Markov chain on the vertices of the graph. An important question that arises often in the context of Markov chain is whether the uniform distribution on the vertices of the graph is a stationary distribution of the Markov chain. Stationary distribution of a Markov chain is a global property of the graph. In this paper, we prove that for a regular directed graph whether the uniform distribution on the vertices of the graph is a stationary distribution, depends on a local property of the graph, namely if (u,v) is an directed edge then outdegree(u) is equal to indegree(v). This result also has an application to the problem of testing whether a given distribution is uniform or "far" from being uniform. This is a well studied problem in property testing and statistics. If the distribution is the stationary distribution of the lazy random walk on a directed graph and the graph is given as an input, then how many bits of the input graph do one need to query in order to decide whether the distribution is uniform or "far" from it? This is a problem of graph property testing and we consider this problem in the orientation model (introduced by Halevy et al.). We reduce this problem to test (in the orientation model) whether a directed graph is Eulerian. And using result of Fischer et al. on query complexity of testing (in the orientation model) whether a graph is Eulerian, we obtain bounds on the query complexity for testing whether the stationary distribution is uniform

Lower bounds for sparse recovery problems

Sparse recovery or compressed sensing is the problem of estimating a signal from noisy linear measurements of that signal. Sparse recovery has traditionally been used in areas like image acquisition, streaming algorithms, genetic testing, and, more recently, for image recovery tasks. Over the last decade many techniques have been developed for sparse recovery under various guarantees. We develop new lower bound techniques and show the tightness of existing results for the following variants of the sparse recovery problem: (i) adaptive sparse recovery, (ii) sparse recovery under high SNR, (iii) deterministic L2 heavy hitters, and, (iv) compressed sensing with generative models.Computer Science

Adaptively Secure Constrained Pseudorandom Functions

A constrained pseudo random function (PRF) behaves like a standard PRF, but with the added feature that the (master) secret key holder, having secret key K, can produce a constrained key, K_f, that allows for the evaluation of the PRF on a subset of the domain as determined by a predicate function f within some family F. While previous constructions gave constrained PRFs for poly-sized circuits, all reductions for such functionality were based in the selective model of security where an attacker declares which point he is attacking before seeing any constrained keys. In this paper we give new constrained PRF constructions for circuits that have polynomial reductions to indistinguishability obfuscation in the random oracle model. Our solution is constructed from two recently emerged primitives: an adaptively secure Attribute-Based Encryption (ABE) for circuits and a Universal Parameters as introduced by Hofheinz et al. Both primitives are constructible from indistinguishability obfuscation (iO) (and injective pseudorandom generators) with only polynomial loss

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Devising focused strategies to improve organ donor registrations: A cross-sectional study among professional drivers in coastal South India.

BACKGROUND:In India, annually, 500,000 people die due to non-availability of organs. Given the large proportion of brain death amongst road accident victims, any improvement in organ donation practices amongst this cohort could potentially address this deficit. In this study, we identify the potential areas for intervention to improve organ donation amongst professional drivers, a population more likely to suffer from road accidents. METHODS:300 participants were surveyed using a structured, orally-administered questionnaire to assess knowledge, attitudes and practices regarding organ donation. Multivariate analysis was performed to identify key variables affecting intent to practice. RESULTS:Nearly half our participants had unsatisfactory knowledge and attitude scores. Knowledge and attitude was positively correlated, rs (298) = .247, p < .001, with better scores associated with a higher likelihood of intent to practice organ donation [AOR: 2.23 (1.26-3.94), p = .006; AOR: 12.164 (6.85-21.59), p < .001 respectively]. Lack of family support and fear of donated organs going into medical research were the key barriers for the same [AOR: 0.43 (0.19-0.97), p = .04; AOR: 0.27 (0.09-0.85), p = .02 respectively]. CONCLUSION:Targeted health-education, behaviour change communication, and legal interventions, in conjunction, are key to improving organ donor registrations

Malaria Epidemiology and COVID-19 Pandemic:Are They Interrelated?

Coronavirus disease 2019 (COVID-19) is a systemic disease, impacting multiple organs in the human body. But COVID-19 also impacts other diseases of relevance to public and planetary health. To understand and respond to the COVID-19 pandemic, we need an intersectional conceptual lens and systems thinking. For example, the strain on health care systems due to COVID-19 has adversely impacted global malaria elimination programs. With many epidemiological, clinical, and biological parallels documented, we examined in this study the scenario of malaria and COVID-19 syndemic in India. The disruptive influence of COVID-19 on the National Framework for Malaria Elimination (NFME), impact of unintended chemoprophylaxis, population genetic influences, and the shifting patterns of epidemiology are compared. Importantly, a time series analysis forecasted the burden of malaria increasing in the upcoming years. Although reported malaria cases showed a decline in 2020 compared to the previous years, an increase in cases was documented in 2021, with nine states reporting an increase up to July 2021. Pandemics often cause crosscutting disruptions in health care. Reshaping the priorities of the malaria elimination program and a diligent implementation of the priorities in the NFME would, therefore, be well-advised: (1) vector control, (2) antimalarial therapy recommendations, (3) monitoring drug resistance, (4) prevention of the spread of asymptomatic disease-causing low-density transmission, and (5) large-scale testing measures. In conclusion, the findings from the present study inform future comparative studies in other world regions to better understand the broader, systemic, temporal, and spatial impacts of the COVID-19 pandemic on existing and future diseases across public health systems and services