Safety, Absoluteness, and Computability

The semantic notion of dependent safety is a common generalization of the notion of absoluteness used in set theory and the notion of domain independence used in database theory for characterizing safe queries. This notion has been used in previous works to provide a unified theory of constructions and operations as they are used in different branches of mathematics and computer science, including set theory, computability theory, and database theory. In this paper we provide a complete syntactic characterization of general first-order dependent safety. We also show that this syntactic safety relation can be used for characterizing the set of strictly decidable relations on the natural numbers, as well as for characterizing rudimentary set theory and absoluteness of formulas within it

Characterizing downwards closed, strongly first order, relativizable dependencies

In Team Semantics, a dependency notion is strongly first order if every sentence of the logic obtained by adding the corresponding atoms to First Order Logic is equivalent to some first order sentence. In this work it is shown that all nontrivial dependency atoms that are strongly first order, downwards closed, and relativizable (in the sense that the relativizations of the corresponding atoms with respect to some unary predicate are expressible in terms of them) are definable in terms of constancy atoms. Additionally, it is shown that any strongly first order dependency is safe for any family of downwards closed dependencies, in the sense that every sentence of the logic obtained by adding to First Order Logic both the strongly first order dependency and the downwards closed dependencies is equivalent to some sentence of the logic obtained by adding only the downwards closed dependencies

Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data.

Neurodevelopmental maturation takes place in a social environment in addition to a neurobiological one. Characterization of social environmental factors that influence this process is therefore an essential component in developing an accurate model of adolescent brain and neurocognitive development, as well as susceptibility to change with the use of marijuana and other drugs. The creation of the Culture and Environment (CE) measurement component of the ABCD protocol was guided by this understanding. Three areas were identified by the CE Work Group as central to this process: influences relating to CE Group membership, influences created by the proximal social environment, influences stemming from social interactions. Eleven measures assess these influences, and by time of publication, will have been administered to well over 7,000 9-10 year-old children and one of their parents. Our report presents baseline data on psychometric characteristics (mean, standard deviation, range, skewness, coefficient alpha) of all measures within the battery. Effectiveness of the battery in differentiating 9-10 year olds who were classified as at higher and lower risk for marijuana use in adolescence was also evaluated. Psychometric characteristics on all measures were good to excellent; higher vs. lower risk contrasts were significant in areas where risk differentiation would be anticipated

Reasoning About the Reliability of Multi-version, Diverse Real-Time Systems

This paper is concerned with the development of reliable real-time systems for use in high integrity applications. It advocates the use of diverse replicated channels, but does not require the dependencies between the channels to be evaluated. Rather it develops and extends the approach of Little wood and Rush by (for general systems) by investigating a two channel system in which one channel, A, is produced to a high level of reliability (i.e. has a very low failure rate), while the other, B, employs various forms of static analysis to sustain an argument that it is perfect (i.e. it will never miss a deadline). The first channel is fully functional, the second contains a more restricted computational model and contains only the critical computations. Potential dependencies between the channels (and their verification) are evaluated in terms of aleatory and epistemic uncertainty. At the aleatory level the events ''A fails" and ''B is imperfect" are independent. Moreover, unlike the general case, independence at the epistemic level is also proposed for common forms of implementation and analysis for real-time systems and their temporal requirements (deadlines). As a result, a systematic approach is advocated that can be applied in a real engineering context to produce highly reliable real-time systems, and to support numerical claims about the level of reliability achieved

The development of a measure of social care outcome for older people. Funded/commissioned by: Department of Health

An essential element of identifying Best Value and monitoring cost-effective care is to be able to identify the outcomes of care. In the field of health services, use of utility-based health related quality of life measures has become widespread, indeed even required. If, in the new era of partnerships, social care outcomes are to be valued and included we need to develop measures that reflect utility or welfare gain from social care interventions. This paper reports on a study, commissioned as part of the Department of Health’s Outcomes of Social Care for Adults Initiative, that developed an instrument and associated utility indexes that provide a tool for evaluating social care interventions in both a research and service setting. Discrete choice conjoint analysis used to derive utility weights provided us with new insights into the relative importance of the core domains of social care to older people. Whilst discrete choice conjoint analysis is being increasingly used in health economics, this is the first study that has attempted to use it to derive a measure of outcome

Causal Inference by Stochastic Complexity

The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes

Quantifying dependencies for sensitivity analysis with multivariate input sample data

We present a novel method for quantifying dependencies in multivariate datasets, based on estimating the R\'{e}nyi entropy by minimum spanning trees (MSTs). The length of the MSTs can be used to order pairs of variables from strongly to weakly dependent, making it a useful tool for sensitivity analysis with dependent input variables. It is well-suited for cases where the input distribution is unknown and only a sample of the inputs is available. We introduce an estimator to quantify dependency based on the MST length, and investigate its properties with several numerical examples. To reduce the computational cost of constructing the exact MST for large datasets, we explore methods to compute approximations to the exact MST, and find the multilevel approach introduced recently by Zhong et al. (2015) to be the most accurate. We apply our proposed method to an artificial testcase based on the Ishigami function, as well as to a real-world testcase involving sediment transport in the North Sea. The results are consistent with prior knowledge and heuristic understanding, as well as with variance-based analysis using Sobol indices in the case where these indices can be computed

Rehabilitation Therapy in Older Acute Heart Failure Patients (REHAB-HF) trial: Design and rationale.

BACKGROUND: Acute decompensated heart failure (ADHF) is a leading cause of hospitalization in older persons in the United States. Reduced physical function and frailty are major determinants of adverse outcomes in older patients with hospitalized ADHF. However, these are not addressed by current heart failure (HF) management strategies and there has been little study of exercise training in older, frail HF patients with recent ADHF. HYPOTHESIS: Targeting physical frailty with a multi-domain structured physical rehabilitation intervention will improve physical function and reduce adverse outcomes among older patients experiencing a HF hospitalization. STUDY DESIGN: REHAB-HF is a multi-center clinical trial in which 360 patients ≥60 years hospitalized with ADHF will be randomized either to a novel 12-week multi-domain physical rehabilitation intervention or to attention control. The goal of the intervention is to improve balance, mobility, strength and endurance utilizing reproducible, targeted exercises administered by a multi-disciplinary team with specific milestones for progression. The primary study aim is to assess the efficacy of the REHAB-HF intervention on physical function measured by total Short Physical Performance Battery score. The secondary outcome is 6-month all-cause rehospitalization. Additional outcome measures include quality of life and costs. CONCLUSIONS: REHAB-HF is the first randomized trial of a physical function intervention in older patients with hospitalized ADHF designed to determine if addressing deficits in balance, mobility, strength and endurance improves physical function and reduces rehospitalizations. It will address key evidence gaps concerning the role of physical rehabilitation in the care of older patients, those with ADHF, frailty, and multiple comorbidities