Policing Democracy

2011 Winner of the Charles H. Levine Memorial Book Prize of the International Political Science AssociationLatin America’s crime rates are astonishing by any standard—the region’s homicide rate is the world’s highest. This crisis continually traps governments between the need for comprehensive reform and the public demand for immediate action, usually meaning iron-fisted police tactics harking back to the repressive pre-1980s dictatorships. In Policing Democracy, Mark Ungar situates Latin America at a crossroads between its longstanding form of reactive policing and a problem-oriented approach based on prevention and citizen participation. Drawing on extensive case studies from Argentina, Bolivia, and Honduras, he reviews the full spectrum of areas needing reform: criminal law, policing, investigation, trial practices, and incarceration. Finally, Policing Democracy probes democratic politics, power relations, and regional disparities of security and reform to establish a framework for understanding the crisis and moving beyond it

Developing Methodologies to Assess Organized Crime Strategies in Latin America

Because of the increasingly organized and lethal nature of criminality in Latin America and the Caribbean, organized crime policy may be the single most important safeguard for regional security. A policy-relevant understanding requires disentangling these crimes’ many overlapping sources, removing embedded layers of methodological obstruction, and attuning responses with organized crime practice. Using embedded mixed methods to incorporate the ways in which available quantitative data and policies reflect the qualitative conditions of the agencies and processes that produce it, this report works to identify these broken, frayed or invisible inter-connections through a methodological framework as broadly flexible as the criminality it aims to measure and stop.https://digitalcommons.fiu.edu/jgi_research/1040/thumbnail.jp

Surface Tension Containment Experiment (STCE) - Increasing Science Throughput on ISS

The Microgravity Science Glovebox (MSG) on the International Space Station (ISS) is used for fluid transfer in many types of experiments. Reagents are handled in the MSG to prevent their accidental release into the cabin. However, the MSG is currently over-subscribed, creating a backlog of users in flight. As a recourse, current experiments are underway to assess the possibility of moving certain science operations from the MSG into the open cabin of the ISS. The experiments are designed to assess the efficacy of exploiting surface tension as a control to prevent the unwanted release of liquids. Dyed water currently serves as an ersatz for potentially more hazardous liquids. Common wet-lab operations such as de/mating wetted Luer-Lok fittings, liquid-bearing container lid removal, and pipetting between well plates are performed illustrating the facility and challenges imposed by the microgravity environment. Concerning the latter, various pipette cannula sizes are deployed at various injection, withdrawal, and translations rates to map the existence, size, velocity, and trajectory of satellite droplets expected to form when breaking contact between the water surface and the pipette tip. Though such drops frequently form in terrestrial operations, they are nearly imperceptible and inconsequentialdue in part to their speed and because gravity quickly returns them to the well plate from which they came. The use of airflow to capture and collect such satellite droplets is demonstrated. The dynamic stability of the liquid-filled well plates is quantified in response to a variety of crew-imparted disturbances. From a safety perspective, the results from the STCE are of immediate practical value. If such routine low-gravity capillary fluidic operations can be established as mundane, their performance may be moved out of the MSG and into the cabin, significantly increasing the efficiency of experiments performed on ISS

Learning the Structure of Auto-Encoding Recommenders

Autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. However, existing autoencoder recommenders use fully-connected neural network layers and do not employ structure learning. This can lead to inefficient training, especially when the data is sparse as commonly found in collaborative filtering. The aforementioned results in lower generalization ability and reduced performance. In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain. Due to the nature of items in general, we know that certain items are more related to each other than to other items. Based on this, we propose a method that first learns groups of related items and then uses this information to determine the connectivity structure of an auto-encoding neural network. This results in a network that is sparsely connected. This sparse structure can be viewed as a prior that guides the network training. Empirically we demonstrate that the proposed structure learning enables the autoencoder to converge to a local optimum with a much smaller spectral norm and generalization error bound than the fully-connected network. The resultant sparse network considerably outperforms the state-of-the-art methods like \textsc{Mult-vae/Mult-dae} on multiple benchmarked datasets even when the same number of parameters and flops are used. It also has a better cold-start performance.Comment: Proceedings of The Web Conference 202

Dental topography and diets of platyrrhine primates

More than half a century ago, Percy Butler touted the importance of analyzing teeth to understand their function in an evolutionary context. There have been many advances in the study of dental functional morphology since that time. Here we review the various approaches to characterizing and comparing occlusal form that have been developed, especially dental topographic analysis. We also report on a new study of dental topography of platyrrhine primates (n = 341 individuals representing 16 species) with known differences in both dietary preferences and other food items eaten. Results indicate frugivores, gummivores, folivores, and seed eaters each have a unique combination of slope, relief, angularity, sharpness, and occlusal orientation patch size and count values. Likewise, among frugivores, those that supplement their diets with hard objects, insects, leaves, and seeds, also each have a distinctive suite of topographic features. We conclude that both primary and secondary diet choices select for occlusal form, and that functional morphology more reflects the types of foods and mechanical challenges they pose rather than the frequencies in which they are eaten.Peer reviewe

Dental Topography and Microwear Texture in Sapajus Apella

Dental microwear texture pattern has been associated with aspects of diet for a broad range of mammalian taxa. The basic idea is that soft, tough foods are sheared with a steeper angle of approach between opposing occlusal surfaces, whereas hard, brittle items are crushed with forces perpendicular to those surfaces; and this difference is manifested in anisotropic, striated microwear textures for tough foods, and complex, pitted ones for hard objects. Other factors may, however, influence microwear texture pattern and confound diet signals. For example, if tooth surface slope influences angle of approach between opposing teeth, then perhaps wear-related changes in tooth shape could affect microwear pattern. This study evaluates the effects of occlusal topography on microwear texture for a series of variably worn upper second molars of one primate species, Sapajus apella. Results indicate no significant covariation between any measured topographic attribute (average slope, angularity, relief) and microwear texture variable (complexity, anisotropy, textural fill volume). This suggests that, for this taxon at least, wear-related changes in tooth form do not affect microwear pattern in a consistent manner. This implies that variably worn teeth can be included in samples for comparisons aimed at distinguishing groups by diet

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Dental Microwear and Diet of the Plio-Pleistocene Hominin Paranthropus boisei

The Plio-Pleistocene hominin Paranthropus boisei had enormous, flat, thickly enameled cheek teeth, a robust cranium and mandible, and inferred massive, powerful chewing muscles. This specialized morphology, which earned P. boisei the nickname “Nutcracker Man”, suggests that this hominin could have consumed very mechanically challenging foods. It has been recently argued, however, that specialized hominin morphology may indicate adaptations for the consumption of occasional fallback foods rather than preferred resources. Dental microwear offers a potential means by which to test this hypothesis in that it reflects actual use rather than genetic adaptation. High microwear surface texture complexity and anisotropy in extant primates can be associated with the consumption of exceptionally hard and tough foods respectively. Here we present the first quantitative analysis of dental microwear for P. boisei. Seven specimens examined preserved unobscured antemortem molar microwear. These all show relatively low complexity and anisotropy values. This suggests that none of the individuals consumed especially hard or tough foods in the days before they died. The apparent discrepancy between microwear and functional anatomy is consistent with the idea that P. boisei presents a hominin example of Liem's Paradox, wherein a highly derived morphology need not reflect a specialized diet