Title: Paper Matching through Iterative Relaxation
Abstract: Peer review is an integral stage of the scientific publication process during which a group of reviewers attempt to identify the highest quality work. For venues that regularly experience thousands of submissions, assigning reviewers to those submissions must be automated. When constructing a matching, the most prevalent paper matching algorithms often assign many papers to a set of reviewers who may not possess the necessary expertise to render a sufficient review. We propose a new integer programming formulation of paper matching that directly addresses this issue. Since optimizing our formulation may not always be tractable, we also present a new algorithm that uses iterative relaxation to approximate the optimal matching. We empirically demonstrate that our algorithm reduces the number of insufficiently reviewed papers while maintaining measurably high quality when compared to previous paper matching algorithms.
Title: Pervasive Variation of Transcription Factor Orthologs Contributes to Regulatory Network Evolution
Abstract: Differences in transcriptional regulatory networks underlie much of the phenotypic variation observed across organisms. Changes to cis-regulatory elements are widely believed to be the predominant means by which regulatory networks evolve, yet examples of regulatory network divergence due to transcription factor (TF) variation have also been observed. To systematically ascertain the extent to which TFs contribute to regulatory divergence, we analyzed the evolution of the largest class of metazoan TFs, Cys2-His2 zinc finger (C2H2-ZF) TFs, across 12 Drosophila species spanning ~45 million years of evolution. Remarkably, we uncovered that a significant fraction of all C2H2-ZF 1-to-1 orthologs in flies exhibit variations that can affect their DNA-binding specificities. In addition to loss and recruitment of C2H2-ZF domains, we found diverging DNA-contacting residues in ~44% of domains shared between D. melanogaster and the other fly species. These diverging DNA-contacting residues, found in ~70% of the D. melanogaster C2H2-ZF genes in our analysis and corresponding to ~26% of all annotated D. melanogaster TFs, show evidence of functional constraint: they tend to be conserved across phylogenetic clades and evolve slower than other diverging residues. These same variations were rarely found as polymorphisms within a population of D. melanogaster flies, indicating their rapid fixation. The predicted specificities of these dynamic domains gradually change across phylogenetic distances, suggesting stepwise evolutionary trajectories for TF divergence. Further, whereas proteins with conserved C2H2-ZF domains are enriched in developmental functions, those with varying domains exhibit no functional enrichments. Our work suggests that a subset of highly dynamic and largely unstudied TFs are a likely source of regulatory variation in Drosophila and other metazoans.
Title: Object Manipulation Based on Memory and Observation
Abstract: Traditional approaches to object manipulation often consider a pipeline of segmentation, object recognition, and pose estimation to be an essential perception stage prior to motor activity. In this work, we propose an object model that is composed of a set of viewpoint-specific observations to capture how actions change observation of the object. This approach allows the robot to skip pose estimation and interact with objects directly based on memory and observation.
Title: Estimating Peer Influence Effects with Generalized Propensity Scores
Abstract: In this work, we explore the problem of estimating and adjusting for peer influence effects in networks. Networks provide additional opportunities and strategies for interventional planning. Identifying effective interventional regimes requires reasoning about a spectrum of causal effects, where an individual's outcome may be regulated by the treatment status of that individual's neighbors. We motivate and evaluate a technique for estimating these peer effects, grounded in the use of generalized propensity score models
Title: Inference in a Partially Observed Queuing Model with Applications in Ecology
Abstract: We consider the problem of inference in a probabilistic model for transient populations where we wish to learn about arrivals, departures, and population size over all time, but the only available data are periodic counts of the population size at specific observation times. The underlying model arises in queueing theory (as an Mt/G/∞ queue) and also in ecological models for short-lived animals such as insects. Our work applies to both systems. Previous work in the ecology literature focused on maximum likelihood estimation and made a simplifying independence assumption that prevents inference over unobserved random variables such as arrivals and departures. The contribution of this paper is to formulate a latent variable model and develop a novel Gibbs sampler based on Markov bases to perform inference using the correct, but intractable, likelihood function. We empirically validate the convergence behavior of our sampler and demonstrate the ability of our model to make much finer-grained inferences than the previous approach