Uncovering Digital Platform Generativity: A Systematic Literature Review

Generativity is identified as the driver for digital innovation and platform growth by engaging a large number of actors with diverse skills. Generativity is also the signal of innovation, and it enables innovative process self-reinforcement, which leads the digital platforms to evolve in unanticipated ways. However, with the proliferation of generativity in the Information Systems (IS) literature growing, we find the understanding of generativity is inconsistent. We conduct a systematic literature review to clear the understanding mist and advance the understanding of generativity. Our study shows that generativity is a social-technical system in which social actors interact with each other by employing digital technologies. Generativity is not unequivocally positive to the digital platform due to the inherent tension but requires deliberate actions by the platform owners. Our study contributes to IS research by providing a comprehensive conceptual framework of digital platform generativity

HOW PARTS CONNECT TO WHOLE IN BUILDING DIGITAL GENERATIVITY IN DIGITAL PLATFORM ECOSYSTEMS

Generativity drives digital innovation and platform growth by engaging many other businesses with diverse digital skills and resources in a digital platform. As the proliferation of generativity research grows, the Information Systems (IS) literature demonstrates the basic understanding of this notion in the areas of properties of digital technologies, social events, and/or the interaction between these two without an integrated view of how generativity is raised to enable the digital innovation. Therefore, considering that digital platforms are a kind of ecosystem, we aim to develop a new understanding of this emerging phenomenon by employing a holistic perspective. Through the information ecology theoretical lens, we develop a digital generativity process model that explains how the technological and social resources interact to generate perpetual digital innovation in digital platform ecosystems (DPE). This study contributes to generativity research by providing a dynamic and holistic view of generativity formalization in DPEs

Weighted estimates for commutators associated to singular integral operator satisfying a variant of Hörmander's condition

In this paper, we establish some boundedness for commutators generated by the singular integral operator satisfying a variant of Hörmander's condition and a weighted BMO function on weighted Hardy spaces and weighted Herz spaces. As an application, we obtain some classical results

Hurricanes Substantially Reduce the Nutrients in Tropical Forested Watersheds in Puerto Rico

Because nutrients including nitrogen and phosphorus are generally limited in tropical forest ecosystems in Puerto Rico, a quantitative understanding of the nutrient budget at a watershed scale is required to assess vegetation growth and predict forest carbon dynamics. Hurricanes are the most frequent disturbance in Puerto Rico and play an important role in regulating lateral nitrogen and phosphorus exports from the forested watershed. In this study, we selected seven watersheds in Puerto Rico to examine the immediate and lagged effects of hurricanes on nitrogen and phosphorous exports. Our results suggest that immediate surges of heavy precipitation associated with hurricanes accelerate nitrogen and phosphorus exports as much as 297 ± 113 and 306 ± 70 times than the long-term average, respectively. In addition, we estimated that it requires approximately one year for post-hurricane riverine nitrogen and phosphorus concentrations to recover to pre-hurricane levels. During the recovery period, the riverine nitrogen and phosphorus concentrations are 30 ± 6% and 28 ± 5% higher than the pre-hurricane concentrations on average

SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation

Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain. Due to privacy issues, source-free domain adaptation (SFDA), where source data is unavailable during adaptation, has recently become very demanding yet challenging. Existing SFDA methods focus on either self-supervised learning of target samples or reconstruction of virtual source data. The former overlooks the transferable knowledge in the source model, whilst the latter introduces even more uncertainty. To address the above issues, this paper proposes self-supervised intermediate domain exploration (SIDE) that effectively bridges the domain gap with an intermediate domain, where samples are cyclically filtered out in a self-supervised fashion. First, we propose cycle intermediate domain filtering (CIDF) to cyclically select intermediate samples with similar distributions over source and target domains. Second, with the aid of those intermediate samples, an inter-domain gap transition (IDGT) module is developed to mitigate possible distribution mismatches between the source and target data. Finally, we introduce cross-view consistency learning (CVCL) to maintain the intrinsic class discriminability whilst adapting the model to the target domain. Extensive experiments on three popular benchmarks, i.e. Office-31, Office-Home and VisDA-C, show that our proposed SIDE achieves competitive performance against state-of-the-art methods.Comment: code at https://github.com/se111/SID

Super-resolution imaging and tracking of protein–protein interactions in sub-diffraction cellular space

Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein–protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB–EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB–EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB–EF-Tu interactions