First record of \u3ci\u3eOrsilochides scurrilis\u3c/i\u3e (Stål) (Hemiptera: Heteroptera: Scutelleridae: Pachycorinae) in the United States, with notes on the biology and distribution of U.S. species of \u3ci\u3eOrsilochides\u3c/i\u3e Kirkaldy

Orsilochides scurrilis (Stål) (Hemiptera: Heteroptera: Scutelleridae) is reported from the United States for the first time based on a specimen collected in Santa Cruz County, Arizona. A key to separate the U.S. species of Orsilochides Kirkaldy is provided. In addition, host plant records and distribution of the other two species of Orsilochides that occur in the U.S., Orsilochides guttata (Herrich-Schäffer) and Orsilochides stictica (Dallas), are analyzed through a combination of digital photo records and museum specimens

Deterministic variational inference for robust Bayesian neural networks

Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches

Pnpla3 single nucleotide polymorphism prevalence and association with liver disease in a diverse cohort of persons living with hiv

In persons living with HIV (PLWH), there are multiple sources of liver injury. Gene polymorphisms of PNPLA3 (patatin-like phospholipase domain-containing protein 3) have been identified as an important cofactor for increased disease severity in both alcoholic and non-alcoholic steatohepatitis (NASH). We utilized a well-characterized cohort of ethnically and racially diverse patients with HIV to define the prevalence of PNPLA3 SNPs (single nucleotide polymorphism) (rs738409), and to determine the relationship to hepatic steatosis and liver fibrosis. Steatosis was determined using MRI-PDFF (magnetic resonance imaging-determined proton density fat fraction) and fibrosis was estimated using MR Elastography (MRE). From the Miami Area HIV Study (MASH) cohort, 100 HIV positive participants and 40 controls (HCV/HIV = 20; HCV and HIV negative = 20) were evaluated. Nearly 40% of all participants carried the variant G allele associated with increased liver disease severity and 5% were homozygotic GG. The variant SNP occurred most frequently in those self-identified as Hispanic compared to white or Black participants. Hepatic steatosis (\u3e5% fat) was present significantly more often in those without HIV vs. those with (p \u3c 0.001). Putative NAFLD/NASH was found to be present in 6% of tested subjects, who were HIV monoinfected. BMI was lower in those that carried the G allele for PNPLA3. This finding suggests that PNPLA3 may be an independent component to NAFLD (non-alcoholic fatty liver disease)/NASH development and longitudinal follow-up of the cohort is warranted

An Ethnography of Brand Piracy in Guatemala

An important dimension of contemporary capitalism is the global spread of intellectual property rights law, drawing new attention by governments and media to the unauthorized copying of fashion brands. In this dissertation, I draw on sixteen months of ethnographic research with small-scale, indigenous Maya garment manufacturers to examine the cultural and moral context of brand piracy in Guatemala. I analyze what practices of copying and imitation, some of which qualify as piracy under national and international law, among Maya manufacturers reveal about two aspects of the social field: first, changing economic and cultural conditions following waves of neoliberal economic and legal reform, and, second, the nonlinear reproduction of forms of moral and legal reckoning at the margins of the global economy and amidst mounting insecurities that include rising violent crime rates and legal impunity for violent crime. I examine how practices of copying and imitation among manufacturers and competitive behavior more generally are evaluated locally in light of kin relations that promote the sharing of knowledge and resources within a somewhat loose property regime and given ideologies of race and nation that encourage class-based solidarity among Maya people. I find that the normative models and business practices evident among these manufacturers parochialize official portraits of progress, business ethics, and development promoted in neoliberal policy agendas and international law. In addition, I analyze significant gaps between what fashion and branding mean in Guatemalan Maya communities and how they are understood in international projects of legal harmonization that are also about re-branding and re-imagining the Guatemalan nation. Neoliberal statecraft following a long internal armed conflict in Guatemala involves policy approaches that amplify the presence of global brands while compounding conditions of social and economic inequality that limit Maya men and women’s access to authorized goods. Meanwhile, Maya people are invited to participate in a modernist vision of citizenship and social progress that encourages a privatized model of indigenous identity mediated by branded commodities and formal market transactions. The brand emerges as a powerful medium through which claims to legitimacy and authority and senses of belonging are negotiated at national and local levels.Anthropolog

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area