EVALUATING THE EFFECTS OF RANDOMNESS ON MISSING DATA IN ARCHAEOLOGICAL NETWORKS

Network science shows promise for archaeologists who want to explore past social dynamics using material culture. Yet, archaeological data is subject to important caveats that exist for all datasets. Almost all archaeological datasets are biased, and these biases are often unknown or only partially understood. Prior research has examined the effects of missing nodes on archaeological networks. Here, we instead focus on the impact of missing links on such networks. We used an agent-based model (ArchMatNet) to generate a simulated, unbiased assemblage of artifacts deposited at sites. We link those sites through the similarity of their artifacts to form the complete network. We also include an obsidian dataset from the US Southwest to compare differences between real and simulated data. We explore how random and nonrandom sampling of the two datasets affect the accuracy of the network reconstructed. Our analysis confirms prior research demonstrating that random samples are representative of the original network, even when they are small, but that biased samples of any size are significantly problematic. This research highlights the need to consider bias in archaeological data and demonstrates the utility of agent-based models in testing archaeological methods. Furthermore, this simulated dataset can better inform how archaeologists judge bias and will help us develop new methods to mitigate the effects of biased data.

Psychology’s medicalization of male baldness

Male baldness is physically benign though it is increasingly described as a “disease” based on claims that it is profoundly distressing. The medicalization of baldness was assessed using data extracted from a review of 37 male baldness psychosocial impact studies. Findings revealed most studies likely had commercial influences (78%), represented baldness as a disease (77%), were conducted on biased samples (68%), and advocated for baldness products/services (60%), omitting their limitations (68%). Health psychologists should challenge baldness medicalization so that men can make informed choices about what, if anything, they do with their baldness.

Snowball Sampling and Facebook: How Social Media Can Help Access Hard-to-Reach Populations

ABSTRACT The snowball sampling method (SSM) is one of the most widely used methods to collect information about hard-to-reach populations and in low-information contexts. This article argues that SSM can be enhanced by the use of Facebook in at least three ways. This social-networking site helps to address some of the inherent limitations of SSM by learning about local contexts and identifying principal political actors, by gathering background information about and establishing contact with them, and by triangulating information they provide and reaching out to additional key informants. Thus, this article shows how Facebook can provide leverage in overcoming the problem of biased samples and offers concrete empirical illustrations from research on local politics in Latin American countries.

Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug–Drug Interaction Information Retrieval

Clinical drug–drug interactions (DDIs) have been a major cause for not only medical error but also adverse drug events (ADEs). The published literature on DDI clinical toxicity continues to grow significantly, and high-performance DDI information retrieval (IR) text mining methods are in high demand. The effectiveness of IR and its machine learning (ML) algorithm depends on the availability of a large amount of training and validation data that have been manually reviewed and annotated. In this study, we investigated how active learning (AL) might improve ML performance in clinical safety DDI IR analysis. We recognized that a direct application of AL would not address several primary challenges in DDI IR from the literature. For instance, the vast majority of abstracts in PubMed will be negative, existing positive and negative labeled samples do not represent the general sample distributions, and potentially biased samples may arise during uncertainty sampling in an AL algorithm. Therefore, we developed several novel sampling and ML schemes to improve AL performance in DDI IR analysis. In particular, random negative sampling was added as a part of AL since it has no expanse in the manual data label. We also used two ML algorithms in an AL process to differentiate random negative samples from manually labeled negative samples, and updated both the training and validation samples during the AL process to avoid or reduce biased sampling. Two supervised ML algorithms, support vector machine (SVM) and logistic regression (LR), were used to investigate the consistency of our proposed AL algorithm. Because the ultimate goal of clinical safety DDI IR is to retrieve all DDI toxicity–relevant abstracts, a recall rate of 0.99 was set in developing the AL methods. When we used our newly proposed AL method with SVM, the precision in differentiating the positive samples from manually labeled negative samples improved from 0.45 in the first round to 0.83 in the second round, and the precision in differentiating the positive samples from random negative samples improved from 0.70 to 0.82 in the first and second rounds, respectively. When our proposed AL method was used with LR, the improvements in precision followed a similar trend. However, the other AL algorithms tested did not show improved precision largely because of biased samples caused by the uncertainty sampling or differences between training and validation data sets.

Sildenafil and suicide in Sweden

Much theory asserts that sexual intimacy sustains mental health. Experimental tests of such theory remain rare and have not provided compelling evidence because ethical, practical, and cultural constraints bias samples and results. An epidemiologic approach would, therefore, seem indicated given the rigor the discipline brings to quasi-experimental research. For reasons that remain unclear, however, epidemiologist have largely ignored such theory despite the plausibility of the processes implicated, which engender, for example, happiness, feelings of belonging and self-worth, and protection against depression. We use an intent-to-treat design, implemented via interrupted time-series methods, to test the hypothesis that the monthly incidence of suicide, a societally important distal measure of mental health in a population, decreased among Swedish men aged 50–59 after July 2013 when patent rights to sildenafil (i.e., Viagra) ceased, prices fell, and its use increased dramatically. The test uses 102 pre, and 18 post, price-drop months. 65 fewer suicides than expected occurred among men aged 50–59 over test months following the lowering of sildenafil prices. Our findings could not arise from shared trends or seasonality, biased samples, or reverse causation. Our results would appear by chance fewer than once in 10,000 experiments. Our findings align with theory indicating that sexual intimacy reinforces mental health. Using suicide as our distal measure of mental health further implies that public health programming intended to address the drivers of self-destructive behavior should reduce barriers to intimacy in the middle-aged populations.

Asymmetric Hysteresis Loops in Structured Ferromagnetic Nanoparticles with Hard/Soft Areas

Horizontally shifted and asymmetric hysteresis loops are often associated with exchange-biased samples, consisting of a ferromagnet exchange coupled with an antiferromagnet. In purely ferromagnetic samples, such effects can occur due to undetected minor loops or thermal effects. Simulations of ferromagnetic nanostructures at zero temperature with sufficiently large saturation fields should not lead to such asymmetries. Here we report on micromagnetic simulations at zero temperature, performed on sputtered nanoparticles with different structures. The small deviations of the systems due to random anisotropy orientations in the different grains can not only result in strong deviations of magnetization reversal processes and hysteresis loops, but also lead to distinctly asymmetric, horizontally shifted hysteresis loops in purely ferromagnetic nanoparticles.

Asymmetric Hysteresis Loops and Horizontal Loop Shifts in Purely Ferromagnetic Nanoparticles

Horizontally shifted and asymmetric hysteresis loops are often associated with exchange-biased samples, consisting of a ferromagnet exchange-coupled with an antiferromagnet. In purely ferromagnetic samples, such effects can occur due to undetected minor loops or thermal effects. Simulations of ferromagnetic nanostructures at zero temperature with sufficiently large saturation fields should not lead to such asymmetries. Here we report on micromagnetic simulations at zero temperature, performed on sputtered nanoparticles with different shapes. The small deviations of the systems due to random anisotropy orientations in the different grains can not only result in strong deviations of magnetization reversal processes and hysteresis loops, but also to distinctly asymmetric, horizontally shifted hysteresis loops in purely ferromagnetic nanoparticles.