Acqui-hiring or Acqui-quitting: Post-M&A Turnover Prediction via a Dual-fit Model

Gaining highly skilled human capital is one of the primary reasons for corporate mergers and acquisitions (M&A), especially for knowledge-intensive industries. However, the inevitable tensions brought by the divergent cultures and organizational misalignment during the M&A process result in high talent turnover rate and ultimately the integration failure. Hence, it is imperative to understand and prepare for the potential effects of M&A process on employee turnover. To this end, we propose a novel dual-fit model induced heterogeneous Graph Neural Network (GNN) model to predict the talent turnover trend in the post-M&A process, by taking into account the complex relationship among the acquirer firm, the acquiree firm, and the acquired employees. Specifically, we creatively design a dual-fit model comprised of both the firm-level compatibility and employee-firm fit. Extensive evaluations on large-scale real-world data clearly demonstrate the effectiveness of our approach

Relaxin in fibrotic ligament diseases: Its regulatory role and mechanism

Fibrotic ligament diseases (FLDs) are diseases caused by the pathological accumulation of periarticular fibrotic tissue, leading to functional disability around joint and poor life quality. Relaxin (RLX) has been reported to be involved in the development of fibrotic lung and liver diseases. Previous studies have shown that RLX can block pro-fibrotic process by reducing the excess extracellular matrix (ECM) formation and accelerating collagen degradation in vitro and in vivo. Recent studies have shown that RLX can attenuate connective tissue fibrosis by suppressing TGF-β/Smads signaling pathways to inhibit the activation of myofibroblasts. However, the specific roles and mechanisms of RLX in FLDs remain unclear. Therefore, in this review, we confirmed the protective effect of RLX in FLDs and summarized its mechanism including cells, key cytokines and signaling pathways involved. In this article, we outline the potential therapeutic role of RLX and look forward to the application of RLX in the clinical translation of FLDs

Invariant torus and its destruction for an oscillator with dry friction

Acknowledgments This work is supported by the National Natural Science Foundation of China (11732014).Peer reviewedPostprin

Search for spin-dependent gravitational interactions at the Earth range

Among the four fundamental forces, only gravity does not couple to particle spins according to the general theory of relativity. We test this principle by searching for an anomalous scalar coupling between the neutron spin and the Earth gravity on the ground. We develop an atomic gas comagnetometer to measure the ratio of nuclear spin-precession frequencies between $^{129}$Xe and $^{131}$Xe, and search for a change of this ratio to the precision of 10$^{-9}$ as the sensor is flipped in the Earth gravitational field. The null results of this search set an upper limit on the coupling energy between the neutron spin and the gravity on the ground at 5.3$\times$10$^{-22}$~eV (95\% confidence level), resulting in a 17-fold improvement over the previous limit. The results can also be used to constrain several other anomalous interactions. In particular, the limit on the coupling strength of axion-mediated monopole-dipole interactions at the range of the Earth radius is improved by a factor of 17.Comment: Accepted by Physical Review Letter

Strange Nonchaotic Attractors From a Family of Quasiperiodically Forced Piecewise Linear Maps

Acknowledgments This work is supported by the National Natural Science Foundation of China (11732014).Peer reviewedPostprin

Graph Learning of Multifaceted Motivations for Online Engagement Prediction in Counter-party Social Networks

Social media has emerged as an essential venue to invigorate online political engagement. However, political engagement is multifaceted and impacted by both individuals\u27 self-motivation and social influence from peers and remains challenging to model in a counter-party network. Therefore, we propose a counter-party graph representation learning model to study individuals\u27 intrinsic and extrinsic motivations for online political engagement. Firstly, we capture users\u27 intrinsic political interests providing self-motivation from a user-topic network. Then, we encode how users cast influence on others from the inner-/counter-party through a user-user network. With the learned embedding of intrinsic and extrinsic motivations, we model the interactions between these two facets and utilize the dependency by deep sequential model decoding. Finally, extensive experiments using Twitter data related to the 2020 U.S. presidential election and the 2019 HK protests validate the model\u27s predictive power. This study has implications for online political engagement, political participation, and political polarization