Snap

‘Snap’ deploys the queer cliché of becoming-sailor as a trope for recognition in the amorous encounter and literalises the ‘copycat’ as its catalyst. As Girard argues (1973), the human is foremost mimetic: this story makes its claim for originality and authenticity of connection through the playful recycling of cliché.‘Snap’ eschews the relative affectlessness of some metafiction by staging an amorous approach under the shadow of mortality. It exploits the liminal moment of modernist short fiction to summon the ‘manifold’ of experience. Here, love opens a space of intertextual esonance (Costello 2007), including motifs of Genet (masquerade), Duras (haunting) and Maurice Blanchot (the infinite approach), by writing the threshold of encounter as the intensive silence of wond

Lingo: linearized grassmannian optimization for nuclear norm minimization

As a popular heuristic to the matrix rank minimization problem, nuclear norm minimization attracts intensive research attentions. Matrix factorization based algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for convergence caused by their non-unique factorizations. This paper proposes an efficient and accurate Linearized Grass-mannian Optimization (Lingo) algorithm, which adopts matrix factorization and Grassmann manifold structure to alternatively minimize the subproblems. More specially, linearization strategy makes the auxiliary variables unnecessary and guarantees the close-form solution for low periteration complexity. Lingo then converts linearized objective function into a nuclear norm minimization over Grass-mannian manifold, which could remedy the non-unique of solution for the low-rank matrix factorization. Extensive comparison experiments demonstrate the accuracy and efficiency of Lingo algorithm. The global convergence of Lingo is guaranteed with theoretical proof, which also verifies the effectiveness of Lingo.