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Until recently, descriptions of the Roman family routinely attributed to the head of household the right of life and death over his wife, children of any age, and slaves, and assumed he exercised it. Challenges to this position by Roman law specialists have gradually affected the way this right and its exercise are described by historians of the family. This article surveys these challenges, tracks their uptake by historians of the family, and notes the emerging consensus answer to the question What did the Roman father get by with? Relevant ancient sources are quoted and placed in context, and previously unexamined evidence from Roman playwrights and satirists is offered to support the emerging consensus answer, which is that he did not get away with murder.

"This book provides a forum for the cybernetics field in critical emerging technologies, including research into design, engineering, and technological aspects of cyborg creation and existence alongside humankind for issues in their potential acceptance, participation, policy, governance, and requisite socialization between individualization and corporate, global, networked, mechanized human and humanized machine experiences"--

"Sampling provides an up-to-date treatment of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations ... Organized into six sections, the book covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio and regression estimation; sufficient data, model, and design in practical sampling; useful designs such as stratified, cluster and systematic, multistage, double and network sampling; detectability methods for elusive populations; spatial sampling; and adaptive sampling designs."--Publisher

Abstract
In this paper, I discuss some of the wider uses of adaptive and network sampling designs. Three uses of sampling designs are to select units from a population to make inferences about population values, to select units to use in an experiment, and to distribute interventions to benefit a population. The most useful approaches for inference from adaptively selected samples are design-based methods and Bayesian methods. Adaptive link-tracing network sampling methods are important for sampling populations that are otherwise hard to reach. Sampling in changing populations involves temporal network or spatial sampling design processes with units selected both into and out of the sample over time. Averaging or smoothing fast-moving versions of these designs provides simple estimates of network-related characteristics. The effectiveness of intervention programs to benefit populations depends a great deal on the sampling and assignment designs used in spreading the intervention.

Few industries are buffeted from as many strong forces as healthcare. The industry is highly regulated, thus dramatically increasing costs and sometimes even interfering with the ability to deliver healthcare. New drugs, treatments, and medical technologies are so common that keeping track of them can be overwhelming, and incorporating them into patient care or administration can be costly and complicated. On the social side, different groups have different opinions on any given topic and often the right thing to do depends on your point of view. Third party payers add another level of complexity, and competition adds yet another layer of difficulty as organizations seek to grow patient volume by positioning themselves as distinguished in terms of cost, quality, accessibility, and quality of patient experience.

Few industries are buffeted from as many strong forces as healthcare. The industry is highly regulated, thus dramatically increasing costs and sometimes even interfering with the ability to deliver healthcare. New drugs, treatments, and medical technologies are so common that keeping track of them can be overwhelming, and incorporating them into patient care or administration can be costly and complicated. On the social side, different groups have different opinions on any given topic and often the right thing to do depends on your point of view. Third party payers add another level of complexity, and competition adds yet another layer of difficulty as organizations seek to grow patient volume by positioning themselves as distinguished in terms of cost, quality, accessibility, and quality of patient experience.

The ability to collect and disseminate individually identifiable microdata is becoming increasingly important in a number of arenas. This is especially true in health care and national security, where this data is considered vital for a number of public health and safety initiatives. In some cases legislation has been used to establish some standards for limiting the collection of and access to such data. However, all such legislative efforts contain many provisions that allow for access to individually identifiable microdata without the consent of the data subject. Furthermore, although legislation is useful in that penalties are levied for violating the law, these penalties occur after an individual's privacy has been compromised. Such deterrent measures can only serve as disincentives and offer no true protection. This paper considers security issues involved in releasing microdata, including individual identifiers. The threats to the confidentiality of the data subjects come from the users possessing statistical information that relates the revealed microdata to suppressed confidential information. The general strategy is to recode the initial data, in which some subjects are "safe" and some are at risk, into a data set in which no subjects are at risk. We develop a technique that enables the release of individually identifiable microdata in a manner that maximizes the utility of the released data while providing preventive protection of confidential data. Extensive computational results show that the proposed method is practical and viable and that useful data can be released even when the level of risk in the data is high.