From heat maps to Signal chats to war rooms, volunteers across the US are ready to act if federal immigration agents show up on Election Day. The Franklin County board of elections building at 1700 Morse Street in central Columbus, Ohio, is an early voting site for next week's midterms . It is also located in the middle of the large Somali American community that was targeted by the Immigration and Customs Enforcement (ICE) agency's Operation Buckeye last December. The campaign was designed, the agency said, to find "the worst of the worst criminal illegal aliens." The result was a paltry number of arrests, months of sustained harassment, and a lingering feeling that ICE agents could return at any minute, especially around election time.

This easy-to-use, Wi-Fi-enabled bean-to-cup brewer is good, but not quite great. App is simple and works well. "Smart" features only work with Amazon Alexa and Google Assistant. Integrating with HomeKit via third-party apps is not worth the effort. Pricey for what's essentially an auto-drip machine that works with an app, which is no longer novel or futuristic.

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The FAUST test set contains 200 scans of undressed people in challenging poses andthescans themselvesarenoisy. Nonetheless we report the results as per the protocol in Table 2. For competing approaches we take the numbers from the corresponding papers. It can be clearly seen that our model trained primarily with selfsupervision performs better than the competing approaches. Our formulation allows us to jointly differentiate through the correspondences and the instance specific human model parameters. This allows us to create a self-supervised loop for registration.

We study adversarially robust transfer learning, wherein, given labeled data on multiple (source) tasks, the goal is to train a model with small robust error on a previously unseen (target) task. In particular, we consider a multi-task representation learning (MTRL) setting, i.e., we assume that the source and target tasks admit a simple (linear) predictor on top of a shared representation (e.g., the final hidden layer of a deep neural network). In this general setting, we provide rates on the excess adversarial (transfer) risk for Lipschitz losses and smooth nonnegative losses. These rates show that learning a representation using adversarial training on diverse tasks helps protect against inference-time attacks in data-scarce environments. Additionally, we provide novel rates for the single-task setting.

These techniques calibrate an initial non-metric distance matrix estimated on incomplete data to a distance metric.