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Coyotes are ubiquitous on the North American landscape as a result of their recent expansion across the continent. They have been documented in the heart of some of the most urbanized cities, such as Chicago, Los Angeles, and New York City. Here, we explored the genomic composition of 16 coyotes in the New York metropolitan area to investigate genomic demography and admixture for urban-dwelling canids in Queens County, New York. We identified moderate-to-high estimates of relatedness among coyotes living in Queens (r = 0.0–0.5) and adjacent neighborhoods, suggestive of a relatively small population. Although we found low background levels of domestic-dog ancestry across most coyotes in our sample (5%), we identified a male suspected to be a first-generation coyote–dog hybrid with 46% dog ancestry, as well as his two putative backcrossed offspring that carried approximately 25% dog ancestry. The male coyote–dog hybrid and one backcrossed offspring each carried two transposable element insertions that are associated with human-directed hypersociability in dogs and gray wolves. An additional, unrelated coyote with little dog ancestry also carried two of these insertions. These genetic patterns suggest that gene flow from domestic dogs may become an increasingly important consideration as coyotes continue to inhabit metropolitan regions. 

While deep generative models have succeeded in image processing, natural language processing, and reinforcement learning, training that involves discrete random variables remains challenging due to the high variance of its gradient estimation process. Monte Carlo is a common solution used in most variance reduction approaches. However, this involves time-consuming resampling and multiple function evaluations. We propose a Gapped Straight-Through (GST) estimator to reduce the variance without incurring resampling overhead. This estimator is inspired by the essential properties of Straight-Through Gumbel-Softmax. We determine these properties and show via an ablation study that they are essential. Experiments demonstrate that the proposed GST estimator enjoys better performance compared to strong baselines on two discrete deep generative modeling tasks, MNIST-VAE and ListOps. 

An attacker can obtain a valid TLS certificate for a domain by hijacking communication between a certificate authority (CA) and a victim domain. Performing domain validation from multiple vantage points can defend against these attacks. We explore the design space of multi-vantage-point domain validation to achieve (1) security via sufficiently diverse vantage points, (2) performance by ensuring low latency and overhead in certificate issuance, (3) manageability by complying with CA/Browser forum requirements, and requiring minimal changes to CA operations, and (4) a low benign failure rate for legitimate requests. Our opensource implementation was deployed by the Let's Encrypt CA in February 2020, and has since secured the issuance of more than half a billion certificates during the first year of its deployment. Using real-world operational data from Let's Encrypt, we show that our approach has negligible latency and communication overhead, and a benign failure rate comparable to conventional designs with one vantage point. Finally, we evaluate the security improvements using a combination of ethically conducted real-world BGP hijacks, Internet-scale traceroute experiments, and a novel BGP simulation framework. We show that multi-vantage-point domain validation can thwart the vast majority of BGP attacks. Our work motivates the deployment of multi-vantage-point domain validation across the CA ecosystem to strengthen TLS certificate issuance and user privacy. 

An attacker can obtain a valid TLS certificate for a domain by hijacking communication between a certificate authority (CA) and a victim domain. Performing domain validation from multiple vantage points can defend against these attacks. We explore the design space of multi-vantage-point domain validation to achieve (1) security via sufficiently diverse vantage points, (2) performance by ensuring low latency and overhead in certificate issuance, (3) manageability by complying with CA/Browser forum requirements, and requiring minimal changes to CA operations, and (4) a low benign failure rate for legitimate requests. Our open- source implementation was deployed by the Let’s Encrypt CA in February 2020, and has since secured the issuance of more than half a billion certificates during the first year of its deployment. Using real-world operational data from Let’s Encrypt, we show that our approach has negligible latency and communication overhead, and a benign failure rate comparable to conventional designs with one vantage point. Finally, we evaluate the security improvements using a combination of ethically conducted real-world BGP hijacks, Internet-scale traceroute experiments, and a novel BGP simulation framework. We show that multi-vantage-point domain validation can thwart the vast majority of BGP attacks. Our work motivates the deployment of multi-vantage-point domain validation across the CA ecosystem to strengthen TLS certificate issuance and user privacy. 

We experimentally examine how gradient descent navigates the landscape of matrix factorization to obtain a global minimum. First, we review the critical points of matrix factorization and introduce a balanced factorization. By focusing on the balanced critical point at the origin and a subspace of unbalanced critical points, we study the effect of balance on gradient descent, including an initially unbalanced factorization and adding a balance-regularizer to the objective in the MF problem. Simulations demonstrate that maintaining a balanced factorization enables faster escape from saddle points and overall faster convergence to a global minimum. 

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.