Critical behaviours of contact near phase transitions

A central quantity of importance for ultracold atoms is contact, which measures two-body correlations at short distances in dilute systems. It appears in universal relations among thermodynamic quantities, such as large momentum tails, energy, and dynamic structure factors, through the renowned Tan relations. However, a conceptual question remains open as to whether or not contact can signify phase transitions that are insensitive to short-range physics. Here we show that, near a continuous classical or quantum phase transition, contact exhibits a variety of critical behaviors, including scaling laws and critical exponents that are uniquely determined by the universality class of the phase transition and a constant contact per particle. We also use a prototypical exactly solvable model to demonstrate these critical behaviors in one-dimensional strongly interacting fermions. Our work establishes an intrinsic connection between the universality of dilute many-body systems and universal critical phenomena near a phase transition.Comment: Final version published in Nat. Commun. 5:5140 doi: 10.1038/ncomms6140 (2014

Public vs private administration of rural health insurance schemes: a comparative study in Zhejiang of China.

: Since 2003, China has experimented in some of the country's counties with the private administration of the New Cooperative Medical Scheme (NCMS), a publicly subsidized health insurance scheme for rural populations. Our study compared the effectiveness and efficiency of private vs public administration in four counties in one of China's most affluent provinces in the initial stage of the NCMS's implementation. The study was undertaken in Ningbo city of Zhejiang province. Out of 10 counties in Ningbo, two counties with private administration for the NCMS (Beilun and Ninghai) were compared with two others counties with public administration (Zhenhai and Fenghua), using the following indicators: (1) proportion of enrollees who were compensated for inpatient care; (2) average reimbursement-expense ratio per episode of inpatient care; (3) overall administration cost; (4) enrollee satisfaction. Data from 2004 to 2006 were collected from the local health authorities, hospitals and the contracted insurance companies, supplemented by a randomized household questionnaire survey covering 176 households and 479 household members. In our sample counties, private administration of the NCMS neither reduced transaction costs, nor improved the benefits of enrollees. Enrollees covered by the publicly administered NCMS were more likely to be satisfied with the insurance scheme than those covered by the privately administered NCMS. Experience in the selected counties suggests that private administration of the NCMS did not deliver the hoped-for results. We conclude that caution needs to be exercised in extending private administration of the NCMS

Discovering causal interactions using Bayesian network scoring and information gain

Background: The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search. Results: We develop Exhaustive-IGain, which is like MBS-IGain but does an exhaustive search. We compare the performance of Exhaustive-IGain to MBS-IGain using low-dimensional simulated datasets based on interactions with marginal effects and ones based on interactions without marginal effects. Their performance is similar on the datasets based on marginal effects. However, Exhaustive-IGain compellingly outperforms MBS-IGain on the datasets based on 3 and 4-cause interactions without marginal effects. We apply Exhaustive-IGain to investigate how clinical variables interact to affect breast cancer survival, and obtain results that agree with judgements of a breast cancer oncologist. Conclusions: We conclude that the combined use of information gain and Bayesian network scoring enables us to discover higher order interactions with no marginal effects if we perform an exhaustive search. We further conclude that Exhaustive-IGain can be effective when applied to real data