Interaction between anti-tick vaccine and a macrocyclic lactone improves acaricidal efficacy against Rhipicephalus (Boophilus) microplus (Canestrini) (Acari: Ixodidae) in experimentally infested cattle.

The southern cattle fever tick (SCFT) Rhipicephalus (Boophilus) microplus, is considered the most important ectoparasite of livestock in the world because of high financial losses associated with direct feeding and transmission of the hemoparasites Babesia bovis, B. bigemina, and Anaplasma marginale. Unfortunately, SCFT in many parts of the world have evolved resistance to all market-available pesticides thus driving development of new control technologies.

Snorkel: rapid training data creation with weak supervision.

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations.

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale.

Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting. Snorkel DryBell builds on the Snorkel framework, extending it in three critical aspects: flexible, template-based ingestion of diverse organizational knowledge, cross-feature production serving, and scalable, sampling-free execution.

Snorkel: Rapid Training Data Creation with Weak Supervision.

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of- the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations.

Learning the Structure of Generative Models without Labeled Data.

Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model's dependency structure directly affects the quality of the estimated labels, but selecting a structure automatically without any labeled data is a distinct challenge.

Interpretable Decision Sets: A Joint Framework for Description and Prediction.

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model's prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable.

Fourier Transform Mass Spectrometry: The Transformation of Modern Environmental Analyses.

Unknown compounds in environmental samples are difficult to identify using standard mass spectrometric methods. Fourier transform mass spectrometry (FTMS) has revolutionized how environmental analyses are performed. With its unsurpassed mass accuracy, high resolution and sensitivity, researchers now have a tool for difficult and complex environmental analyses.