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Register a new userText-based Technological Signatures and Similarities: How to create them and what to do with them
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Added by
Daniel Hain PhD.
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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This paper describes an efficiently scalable approach to measure technological similarity between patents by combining embedding techniques from natural language processing with nearest-neighbor approximation. Using this methodology we are able to compute existing similarities between all patents, which in turn enables us to represent the whole patent universe as a technological network. We validate both technological signature and similarity in various ways, and demonstrate at the case of electric vehicle technologies their usefulness to measure knowledge flows, map technological change, and create patent quality indicators. Thereby the paper contributes to the growing literature on text-based indicators for patent landscaping.
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The habitable zone (HZ) is the region around a star(s) where standing bodies of water could exist on the surface of a rocky planet. The classical HZ definition makes a number of assumptions common to the Earth, including assuming that the most important greenhouse gases for habitable planets are CO2 and H2O, habitable planets orbit main-sequence stars, and that the carbonate-silicate cycle is a universal process on potentially habitable planets. Here, we discuss these and other predictions for the habitable zone and the observations that are needed to test them. We also, for the first time, argue why A-stars may be interesting HZ prospects. Instead of relying on unverified extrapolations from our Earth, we argue that future habitability studies require first principles approaches where temporal, spatial, physical, chemical, and biological systems are dynamically coupled. We also suggest that next-generation missions are only the beginning of a much more data-filled era in the not-too-distant future, when possibly hundreds to thousands of HZ planets will yield the statistical data we need to go beyond just finding habitable zone planets to actually determining which ones are most likely to exhibit life.
Neutron star mergers are the canonical multimessenger events: they have been observed through photons for half a century, gravitational waves since 2017, and are likely to be sources of neutrinos and cosmic rays. Studies of these events enable unique insights into astrophysics, particles in the ultrarelativistic regime, the heavy element enrichment history through cosmic time, cosmology, dense matter, and fundamental physics. Uncovering this science requires vast observational resources, unparalleled coordination, and advancements in theory and simulation, which are constrained by our current understanding of nuclear, atomic, and astroparticle physics. This review begins with a summary of our current knowledge of these events, the expected observational signatures, and estimated detection rates for the next decade. I then present the key observations necessary to advance our understanding of these sources, followed by the broad science this enables. I close with a discussion on the necessary future capabilities to fully utilize these enigmatic sources to understand our universe.
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. Teasers are one of the main vehicles for transmitting news to social media users. We compile a novel dataset of teasers by systematically accumulating tweets and selecting those that conform to the teaser definition. We have compared a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al.(2017)s seq2seq with pointer network.
We discuss how a powerful iron line emission can be produced if ~1-5 iron rich solar masses are concentrated in the close vicinity of the burst. Recombination, thermal and fluorescent reflection are discussed. We find that recombination suffers the high Compton temperature of the plasma while the other two scenarios are not mutually exclusive and could account for the claimed iron line detected in two afterglows.
Open-domain Question Answering models which directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of speed and memory compared to conventional models which retrieve and read from text corpora. QA-pair retrievers also offer interpretable answers, a high degree of control, and are trivial to update at test time with new knowledge. However, these models lack the accuracy of retrieve-and-read systems, as substantially less knowledge is covered by the available QA-pairs relative to text corpora like Wikipedia. To facilitate improved QA-pair models, we introduce Probably Asked Questions (PAQ), a very large resource of 65M automatically-generated QA-pairs. We introduce a new QA-pair retriever, RePAQ, to complement PAQ. We find that PAQ preempts and caches test questions, enabling RePAQ to match the accuracy of recent retrieve-and-read models, whilst being significantly faster. Using PAQ, we train CBQA models which outperform comparable baselines by 5%, but trail RePAQ by over 15%, indicating the effectiveness of explicit retrieval. RePAQ can be configured for size (under 500MB) or speed (over 1K questions per second) whilst retaining high accuracy. Lastly, we demonstrate RePAQs strength at selective QA, abstaining from answering when it is likely to be incorrect. This enables RePAQ to ``back-off to a more expensive state-of-the-art model, leading to a combined system which is both more accurate and 2x faster than the state-of-the-art model alone.
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