Dependency Grammar Induction with Neural Lexicalization and Big Training Data

We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence. We find that L-DMV only benefits from very small degrees of lexicalization and moderate sizes of training corpora. L-NDMV can benefit from big training data and lexicalization of greater degrees, especially when enhanced with good model initialization, and it achieves a result that is competitive with the current state-of-the-art.Comment: EMNLP 201

Public Policies and Public Resale Housing Prices in Singapore

In Singapore, the public resale housing market is an active second-hand housing market, whereby previously subsidised new public housing units were being transacted at market prices. In contrast to the private housing price determinants that have been identified in the international literature, the prices of public resale housing in Singapore are largely determined by public policies rather than by economic variables. This paper provides some empirical evidence on how and to what degree public housing policies affected the price dynamics of public resale housing in Singapore during the 1990s. The findings have additional implications of the wider consequences of public policies on the prices of private housing units.Public, resale, housing, policies, prices, Singapore

Dispersive Coupling Between the Superconducting Transmission Line Resonator and the Double Quantum Dots

Realization of controllable interaction between distant qubits is one of the major problems in scalable solid state quantum computing. We study a superconducting transmission line resonator (TLR) as a tunable dispersive coupler for the double-dot molecules. A general interaction Hamiltonian of $n$ two-electron spin-based qubits and the TLR is presented, where the double-dot qubits are biased at the large detuning region and the TLR is always empty and virtually excited. Our analysis o the main decoherence sources indicates that various major quantum operations can be reliably implemented with current technology.Comment: 10 pages, 5 figure

Genetically engineered pre-microRNA-34a prodrug suppresses orthotopic osteosarcoma xenograft tumor growth via the induction of apoptosis and cell cycle arrest.

Osteosarcoma (OS) is the most common primary malignant bone tumor in children, and microRNA-34a (miR-34a) replacement therapy represents a new treatment strategy. This study was to define the effectiveness and safety profiles of a novel bioengineered miR-34a prodrug in orthotopic OS xenograft tumor mouse model. Highly purified pre-miR-34a prodrug significantly inhibited the proliferation of human 143B and MG-63 cells in a dose dependent manner and to much greater degrees than controls, which was attributed to induction of apoptosis and G2 cell cycle arrest. Inhibition of OS cell growth and invasion were associated with release of high levels of mature miR-34a from pre-miR-34a prodrug and consequently reduction of protein levels of many miR-34a target genes including SIRT1, BCL2, c-MET, and CDK6. Furthermore, intravenous administration of in vivo-jetPEI formulated miR-34a prodrug significantly reduced OS tumor growth in orthotopic xenograft mouse models. In addition, mouse blood chemistry profiles indicated that therapeutic doses of bioengineered miR-34a prodrug were well tolerated in these animals. The results demonstrated that bioengineered miR-34a prodrug was effective to control OS tumor growth which involved the induction of apoptosis and cell cycle arrest, supporting the development of bioengineered RNAs as a novel class of large molecule therapeutic agents