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Free, publicly-accessible full text available August 19, 2022
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Free, publicly-accessible full text available June 17, 2022
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Free, publicly-accessible full text available November 1, 2022
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Recent work on Question Answering (QA) and Conversational QA (ConvQA) emphasizes the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval setting typically assumes that each question is answerable by a single span of text within a particular passage (a span answer). The supervision signal is thus derived from whether or not the system can recover an exact match of this ground-truth answer span from the retrieved passages. This method is referred to as span-match weak supervision. However, information-seeking conversations are challenging for this span-match method since long answers, especially freeformmore »
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Free, publicly-accessible full text available July 1, 2022
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ABSTRACT Of all the factors that influence star formation, magnetic fields are perhaps the least well understood. The goal of this paper is to characterize the 3D magnetic field properties of nearby molecular clouds through various methods of statistically analysing maps of polarized dust emission. Our study focuses on nine clouds, with data taken from the Planck Sky Survey as well as data from the Balloon-borne Large Aperture Submillimeter Telescope for Polarimetry observations of Vela C. We compare the distributions of polarization fraction (p), dispersion in polarization angles ($\mathcal {S}$), and hydrogen column density (NH) for each of our targeted clouds.more »
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Free, publicly-accessible full text available December 1, 2022
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Federated learning allows multiple users to collaboratively train a shared classifica- tion model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to poisoning backdoor attacks: a malicious user can alter the shared model to arbitrarily classify specific inputs from a given class. In this paper, we analyze the effects of backdoor attacks on federated meta-learning, where users train a model that can be adapted to dif- ferent sets of output classes using only a few examples. While the ability to adapt could, in principle, make federated learning frameworksmore »
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url: https://openreview.net/pdf?id=LGgdb4TS4Z