Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in part...Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.展开更多
Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal da...Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal dataset of migration-related tweets.To this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts vs.crowds).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper bound.We use this high-quality model to automatically label over 240000 tweets between September 2019 and June 2021.We then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 crisis.Our findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020,with tweet numbers falling slightly below pre-pandemic levels in summer 2021.During the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related tweets.These findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related effects.As one of our main contributions,we outline potential pitfalls of an analysis of social solidarity trends:for example,the ratio of solidarity and anti-solidarity statements depends on the sampling design,i.e.,tweet language,Twitter-user accounts’national identification(country known or unknown)and selection of relevant tweets.In our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymous”Twitter users writing in German compared to English-language tweets of users located in Germany.展开更多
基金partially supported by the Swedish Foundation for International Cooperation in Research and Higher Education(STINT),grant number IG2011-2025ARC DP0878968/DP0987989 for funding support.
文摘Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.
文摘Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal dataset of migration-related tweets.To this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts vs.crowds).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper bound.We use this high-quality model to automatically label over 240000 tweets between September 2019 and June 2021.We then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 crisis.Our findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020,with tweet numbers falling slightly below pre-pandemic levels in summer 2021.During the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related tweets.These findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related effects.As one of our main contributions,we outline potential pitfalls of an analysis of social solidarity trends:for example,the ratio of solidarity and anti-solidarity statements depends on the sampling design,i.e.,tweet language,Twitter-user accounts’national identification(country known or unknown)and selection of relevant tweets.In our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymous”Twitter users writing in German compared to English-language tweets of users located in Germany.
