Legislator-Led Legislative Prayer and the Search for Religious Neutrality

Leading a group in prayer in a public setting blurs the line between public and private. Such blurring implicates a constitutional tension between the Establishment Clause and the Free Exercise Clause. This tension is magnified when the constitutionality of prayer is questioned in the context of democratic participation. Current Supreme Court precedent holds legislative prayer to be constitutional, but the relevant cases, Marsh v. Chambers and Town of Greece, NY v. Galloway, do not address the specific constitutionality of legislator-led prayer. There is currently a circuit split on the subject: in Bormuth v. County of Jackson, the United States Court of Appeals for the Sixth Circuit held legislator-led legislative prayer to be constitutional, but in Lund v. Rowan County, N.C., the United States Court of Appeals for the Fourth Circuit came to the opposite conclusion, despite the case having strikingly similar facts. I seek to confront this tension. First, I challenge the validity of the precedent on legislative prayer. Then, accepting the current precedents as valid, I argue legislator-led prayer in public legislative sessions is unconstitutional. This analysis evaluates the interplay of the original intent of the Establishment Clause, the changes in the social structure of the United States since the eighteenth century, and the unique role of the legislator, separate from that of a guest minister or ordinary citizen. Ultimately, I attempt to inject empathy into legal analysis by pointing to the tangible effects of legislator-led prayer: alienation from the community and increased violence against religious minorities. I hope to highlight these harms as sufficient in themselves to implicate the Establishment Clause and to bolster the argument for holding this practice to be unconstitutional

Conditional R\'enyi entropy and the relationships between R\'enyi capacities

The analogues of Arimoto's definition of conditional R\'enyi entropy and R\'enyi mutual information are explored for abstract alphabets. These quantities, although dependent on the reference measure, have some useful properties similar to those known in the discrete setting. In addition to laying out some such basic properties and the relations to R\'enyi divergences, the relationships between the families of mutual informations defined by Sibson, Augustin-Csisz\'ar, and Lapidoth-Pfister, as well as the corresponding capacities, are explored.Comment: 17 pages, 1 figur

Analyzing the Behavior of Visual Question Answering Models

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. Our behavior analysis reveals that despite recent progress, today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images).Comment: 13 pages, 20 figures; To appear in EMNLP 201

Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

SGTA regulates the cytosolic quality control of hydrophobic substrates

Hydrophobic amino acids are normally shielded from the cytosol and their exposure is often used as an indicator of protein misfolding to enable the chaperone-mediated recognition and quality control of aberrant polypeptides. Mislocalised membrane proteins (MLPs) represent a particular challenge to cellular quality control, and, in this study, membrane protein fragments have been exploited to study a specialised pathway that underlies the efficient detection and proteasomal degradation of MLPs. Our data show that the BAG6 complex and SGTA compete for cytosolic MLPs by recognition of their exposed hydrophobicity, and the data suggest that SGTA acts to maintain these substrates in a non-ubiquitylated state. Hence, SGTA might counter the actions of BAG6 to delay the ubiquitylation of specific precursors and thereby increase their opportunity for successful post-translational delivery to the endoplasmic reticulum. However, when SGTA is overexpressed, the normally efficient removal of aberrant MLPs is delayed, increasing their steady-state level and promoting aggregation. Our data suggest that SGTA regulates the cellular fate of a range of hydrophobic polypeptides should they become exposed to the cytosol

Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models.Comment: 15 pages, 10 figures. To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201