We propose a theoretical framework to capture incremental solutions to cardinality constrained maximization problems. The defining characteristic of our framework is that the cardinality/support of the solution is bounded by a value k ∈ N that grows over time, and we allow the solution to be extended one element at a time. We investigate the best-possible competitive ratio of such an incrementa...

The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve a cardinality constrained problem, the key challenge is to solve the projection onto the cardinality constraint set, which is NP-hard in general when there exist multiple overlapped cardiaiality constraints. In this ...

Cryoelectron microscopy and three-dimensional image reconstruction analysis has been used to determine the structure of native and in vitro assembled cowpea chlorotic mottle virus (CCMV) virions and capsids to 25-A resolution. Purified CCMV coat protein was used in conjunction with in vitro transcribed viral RNAs to assemble RNA 1 only, RNA 2 only, RNA 3/4 only, and empty (RNA lacking) virions....

BACKGROUND Congenital cytomegalovirus (cCMV) infections are the most prevalent intrauterine infections worldwide and are the result of maternal primary or non-primary infections. Early maternal primary infections are thought to carry the highest risk of fetal developmental abnormalities as seen by ultrasound; however, non-primary infections may prove equally detrimental. METHODS/RESULTS This ...

Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs trained with respect to the expected performance, which comes no guarantee about deviations of particular output realizations around optimal expectation. To overcome this issue, we propose a variance-constrained optimization problem for SGNNs, balancin...