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  5. We present Deep Region Competition (DRC), an algorithm designed to extract foreground objects from images in a fully unsupervised manner. Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background. In this work, we rethink the foreground extraction by reconciling energy-based prior with generative image modeling in the form of Mixture of Experts (MoE), where we further introduce the learned pixel re-assignment as the essential inductive bias to capture the regularities of background regions. With this modeling, the foreground/background partition can be naturally found through Expectation-Maximization (EM).more »We show that the proposed method effectively exploits the interaction between the mixture components during the partitioning process, which closely connects to region competition, a seminal approach for generic image segmentation. Experiments demonstrate that DRC exhibits more competitive performances on complex real-world data and challenging multi-object scenes compared with prior methods. Moreover, we show empirically that DRC can potentially generalize to novel foreground objects even from categories unseen during training.« less
  6. Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers' investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Thus, our findings have practical value for developers seeking to increase the adoption rate of their products. Approach: We leverage social contagion theory and statistical modeling to identify, define, and testmore »empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source repositories to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, where developers' software library choices are modeled, to investigate the combination of technological attributes that drive adoption among competing data frame (a core concept for a data science languages) implementations in the R language: tidy and data.table. To describe each technology, we quantify key project attributes that might affect adoption (e.g., response times to raised issues, overall deployments, number of open defects, knowledge base) and also characteristics of developers making the selection (performance needs, scale, and their social network). Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-score StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. To gauge the generalizability of the proposed methodology, we investigate the spread of two popular web JavaScript frameworks Angular and React, and discuss the results. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software.« less
  7. Boeri, L. ; Hennig, R. ; Hirschfeld, P. ; Profeta, G. ; Sanna, A. ; Zurek, E. (Ed.)
    Last year, the report of Room-Temperature Superconductivity in high-pressure carbonaceous sulfur hydride marked a major milestone in the history of physics: one of the holy grails of condensed matter research was reached after more than one century of continuing efforts. This long path started with Neil Ashcroft’s and Vitaly Ginzburg’s visionary insights on high-temperature superconductivity in metallic hydrogen in the 60’s and 70’s, and has led to the current hydride fever, following the report of high-Tc high-pressure superconductivity in H3S in 2014. This Roadmap collects selected contributions from many of the main actors in this exciting chapter of condensed mattermore »history. Key for the rapid progress of this field has been a new course for materials discovery, where experimental and theoretical discoveries proceed hand in hand. The aim of this Roadmap is not only to offer a snapshot of the current status of superconductor materials research, but also to define the theoretical and experimental obstacles that must be overcome for us to realize fully exploitable room temperature superconductors, and foresee future strategies and research directions. This means improving synthesis techniques, extending first-principles methods for superconductors and structural search algorithms for crystal structure predictions, but also identifying new approaches to material discovery based on artificial intelligence.« less
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