Results 221 to 230 of about 9,282,467 (356)
When do firms learn by hiring? How complexity moderates the value of new knowledge
Strategic Management Journal, EarlyView.Abstract Research Summary Organizations often hire employees hoping to acquire new knowledge. While the literature has paid considerable attention to the role of the characteristics of the source of knowledge, the recipient firm, and the knowledge being transferred, it has largely overlooked those of the knowledge being replaced.
Dong Nghi Pham +2 more
wiley +1 more source
Multi-Party Semi-Quantum Simultaneous Ascending Auction Protocol Based on Single-Particle States. [PDF]
Entropy (Basel)Wu X, Yang Y, Wang B, Zhang Y, Han Y.
europepmc +1 more source
Thomson Problem approach to relativistic Dirac fermions ground state energy in the unitary limit
, 2006 Jisheng Chen
openalex +1 more source
Systems Research and Behavioral Science, EarlyView.ABSTRACT The exploration of ways to address the complexity of relationships, power dynamics and multiple perspectives within federated governance systems in sport has been an ongoing theme within sport governance scholarly and practice communities for several decades.
Ian O'Boyle +4 more
wiley +1 more source
Shallow entangled circuits for quantum time series prediction on IBM devices. [PDF]
Sci RepLaskar MR, Goel R.
europepmc +1 more source
Erving Goffman at 100: A Chameleon Seen as a Rorschach Test within a Kaleidoscope
Symbolic Interaction, EarlyView.The 100th anniversary of Erving Goffman's birth was in 2022. Drawing on his work, the Goffman archives, the secondary literature, and personal experiences with him and those in his university of Chicago cohort, I reflect on some implications of his work and life, and the inseparable issues of understanding society.
Gary T. Marx
wiley +1 more source
Quantum speedup for nonreversible Markov chains. [PDF]
Nat CommunClaudon B, Piquemal JP, Monmarché P.
europepmc +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source