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Systematized subject classification is essential for funding and assessing scientific projects. Conventionally, classification schemes are founded on the empirical knowledge of the group of experts; thus, the experts perspectives have influenced the current systems of scientific classification. Those systems archived the current state-of-art in practice, yet the global effect of the accelerating scientific change over time has made the updating of the classifications system on a timely basis vertually impossible. To overcome the aforementioned limitations, we propose an unbiased classification scheme that takes advantage of collective knowledge; Wikipedia, an Internet encyclopedia edited by millions of users, sets a prompt classification in a collective fashion. We construct a Wikipedia network for scientific disciplines and extract the backbone of the network. This structure displays a landscape of science and technology that is based on a collective intelligence and that is more unbiased and adaptable than conventional classifications.
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The transition to a future electricity system based primarily on wind and solar PV is examined for all regions in the contiguous US. We present optimized pathways for the build-up of wind and solar power for least backup energy needs as well as for least cost obtained with a simplified, lightweight model based on long-term high resolution weather-determined generation data. In the absence of storage, the pathway which achieves the best match of generation and load, thus resulting in the least backup energy requirements, generally favors a combination of both technologies, with a wind/solar PV energy mix of about 80/20 in a fully renewable scenario. The least cost development is seen to start with 100% of the technology with the lowest average generation costs first, but with increasing renewable installations, economically unfavorable excess generation pushes it toward the minimal backup pathway. Surplus generation and the entailed costs can be reduced significantly by combining wind and solar power, and/or absorbing excess generation, for example with storage or transmission, or by coupling the electricity system to other energy sectors.
Hierarchical domain-specific classification schemas (or subject heading vocabularies) are often used to identify, classify, and disambiguate concepts that occur in scholarly articles. In this work, we develop, apply, and evaluate a human-in-the-loop workflow that first extracts an initial category tree from crowd-sourced Wikipedia data, and then combines community detection, machine learning, and hand-crafted heuristics or rules to prune the initial tree. This work resulted in WikiCSSH; a large-scale, hierarchically organized vocabulary for the domain of computer science (CS). Our evaluation suggests that WikiCSSH outperforms alternative CS vocabularies in terms of vocabulary size as well as the performance of lexicon-based key-phrase extraction from scholarly data. WikiCSSH can further distinguish between coarse-grained versus fine-grained CS concepts. The outlined workflow can serve as a template for building hierarchically-organized subject heading vocabularies for other domains that are covered in Wikipedia.
PyECLOUD is a newly developed code for the simulation of the electron cloud (EC) build-up in particle accelerators. Almost entirely written in Python, it is mostly based on the physical models already used in the ECLOUD code but, thanks to the implementation of new optimized algorithms, it exhibits a significantly improved performance in accuracy, speed, reliability and flexibility. Such new features of PyECLOUD have been already broadly exploited to study EC observations in the Large Hadron Collider (LHC) and its injector chain as well as for the extrapolation to high luminosity upgrade scenarios.
Archives play a crucial role in the construction and advancement of society. Humans place a great deal of trust in archives and depend on them to craft public policies and to preserve languages, cultures, self-identity, views and values. Yet, there are certain voices and viewpoints that remain elusive in the current processes deployed in the classification and discoverability of records and archives. In this paper, we explore the ramifications and effects of centralized, due process archival systems on marginalized communities. There is strong evidence to prove the need for progressive design and technological innovation while in the pursuit of comprehensiveness, equity and justice. Intentionality and comprehensiveness is our greatest opportunity when it comes to improving archival practices and for the advancement and thrive-ability of societies at large today. Intentionality and comprehensiveness is achievable with the support of technology and the Information Age we live in today. Reopening, questioning and/or purposefully including others voices in archival processes is the intention we present in our paper. We provide examples of marginalized communities who continue to lead community archive movements in efforts to reclaim and protect their cultural identity, knowledge, views and futures. In conclusion, we offer design and AI-dominant technological considerations worth further investigation in efforts to bridge systemic gaps and build robust archival processes.
Containing the spreading of crime is a major challenge for society. Yet, since thousands of years, no effective strategy has been found to overcome crime. To the contrary, empirical evidence shows that crime is recurrent, a fact that is not captured well by rational choice theories of crime. According to these, strong enough punishment should prevent crime from happening. To gain a better understanding of the relationship between crime and punishment, we consider that the latter requires prior discovery of illicit behavior and study a spatial version of the inspection game. Simulations reveal the spontaneous emergence of cyclic dominance between criminals, inspectors, and ordinary people as a consequence of spatial interactions. Such cycles dominate the evolutionary process, in particular when the temptation to commit crime or the cost of inspection are low or moderate. Yet, there are also critical parameter values beyond which cycles cease to exist and the population is dominated either by a stable mixture of criminals and inspectors or one of these two strategies alone. Both continuous and discontinuous phase transitions to different final states are possible, indicating that successful strategies to contain crime can be very much counter-intuitive and complex. Our results demonstrate that spatial interactions are crucial for the evolutionary outcome of the inspection game, and they also reveal why criminal behavior is likely to be recurrent rather than evolving towards an equilibrium with monotonous parameter dependencies.
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