AbstractWe propose VecGAN, an image-to-image translation framework for facial attribute editing with interpretable latent directions. Facial task faces the challenges of precise controllable strength and preservation other attributes image. For this goal, we design by space factorization each attribute, learn a linear direction that is orthogonal to others. The component change, scalar value. I...

Deep operator networks (DeepONets) are powerful architectures for fast and accurate emulation of complex dynamics. As their remarkable generalization capabilities primarily enabled by projection-based attribute, we investigate connections with low-rank techniques derived from the singular value decomposition (SVD). We demonstrate that some concepts behind proper orthogonal (POD)-neural can impr...

Column-Stores has gained market share due to promising physical storage alternative for analytical queries. However, for multi-attribute queries column-stores pays performance penalties due to on-the-fly tuple reconstruction. This paper presents an adaptive approach for reducing tuple reconstruction time. Proposed approach exploits decision tree algorithm to cluster attributes for each projecti...

The influence of orthographic transparency on the prevalence of dyslexia subtypes was examined in a review of multiple-case studies conducted in languages differing in orthographic depth (English, French and Spanish). Cross-language differences are found in the proportion of dissociated profiles as a function of the dependent variables (speed or accuracy), the classification method (classical v...

Fine-grained sketch-based image retrieval (FG-SBIR) is a newly emerged topic in computer vision. The problem is challenging because in addition to bridging the sketch-photo domain gap, it also asks for instance-level discrimination within object categories. Most prior approaches focused on feature engineering and fine-grained ranking, yet neglected an important and central problem: how to estab...

To find an appropriate utterance for a semantic representation is a problem normally treated in the domain of (tactical) natural language generation. For unification-based approaches, like LFG, PATR, or HPSG (Kaplan and Bresnan 1982; Shieber et al. 1983; Pollard and Sag 1994), this problem turns out to be a formal problem of the underlying grammar formalism, when the mapping between strings and...

Supervised attribute relevance detection using cross-comparisons (SARDUX), a recently proposed method for data-driven metric learning, is extended from dimension-weighted Minkowski distances to metrics induced by a data transformation matrix Ω for modeling mutual attribute dependence. Given class labels, parameters of Ω are adapted in such a manner that the inter-class distances are maximized, ...

Recently, there are increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works usually used spatiotemporal local binary pattern for micro-expression analysis. However, the commonly used spatiotemp...

A number of diierent semantics-based program analysis techniques, such as abstract interpretation, projection-based analyses and type inference, have been developed by various people. How can they be compared? In answering this question, we have found it particularly fruitful to focus on the properties that an analysis can manipulate, and how these properties can be combined. We demonstrate the...