Geometric Multi-Model Fitting by Deep Reinforcement Learning

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations

Density Peaks Clustering Approach for Discovering Demand Hot Spots in City-scale Taxi Fleet Dataset

Abstract—In this paper, we introduce a variant of the density peaks clustering (DPC) approach for discovering demand hot spots from a low-frequency, low-quality taxi fleet operational dataset. From the literature, the DPC approach mainly uses density peaks as features to discover potential cluster centers, and this requires distances between all pairs of data points to be calculated. This implies that the DPC approach can only be applied to cases with relatively small numbers of data points. For the domain of urban taxi operations that we are interested in, we could have millions of demand points per day, and calculating all-pair distances between all demand points would be practically impossible, thus making DPC approach not applicable. To address this issue, we project all points to a density image and execute our variant of the DPC algorithm on the processed image. Experiment results show that our proposed DPC variant could get similar results as original DPC, yet with much shorter execution time and lower memory consumption. By running our DPC variant on a real-world dataset collected in Singapore, we show that there are indeed recurrent demand hot spots within the central business district that are not covered by the current taxi stand design. Our approach could be of use to both taxi fleet operator and traffic planners in guiding drivers and setting up taxi stands. I

FGF Receptor-Mediated Gene Delivery Using Ligands Coupled to PEI-β-CyD

A novel vector with high gene delivery efficiency and special cell-targeting ability was developed using a good strategy that utilized low-molecular-weight polyethylenimine (PEI; molecular weight: 600 KDa [PEI600]) crosslinked to β-cyclodextrin (β-CyD) via a facile synthetic route. Fibroblast growth factor receptors (FGFRs) are highly expressed in a variety of human cancer cells and are potential targets for cancer therapy. In this paper, CY11 peptides, which have been proven to combine especially with FGFRs on cell membranes were coupled to PEI-β-CyD using N-succinimidyl-3-(2-pyridyldithio) propionate as a linker. The ratios of PEI600, β-CyD, and peptide were calculated based on proton integral values obtained from the 1H-NMR spectra of the resulting products. Electron microscope observations showed that CY11-PEI-β-CyD can efficiently condense plasmid DNA (pDNA) into nanoparticles of about 200 nm, and MTT assays suggested the decreased toxicity of the polymer. Experiments on gene delivery efficiency in vitro showed that CY11-PEI-β-CyD/pDNA polyplexes had significantly greater transgene activities than PEI-β-CyD/pDNA in the COS-7 and HepG2 cells, which positively expressed FGFR, whereas no such effect was observed in the PC-3 cells, which negatively expressed FGFR. Our current research indicated that the synthesized nonviral vector shows improved gene delivery efficiency and targeting specificity in FGFR-positive cells

Temperature-Dependence Studies of Photorefractive Effect in a Low Glass-Transition-Temperature Polymer Composite

The temperature dependence of the photorefractive effect in a polymer composite containing poly(9-vinycarbazole), tricresyl phosphate, buckminsterfullerene, and 4-(N,N-diethylamino)-β-nitrostyrene is presented. The photoconductive, electro-optic and photorefractive properties of the material have been studied in the temperature range of 22-61°C. An apparent increase of electro-optic modulation with temperature and its eventual saturation is observed. This behavior is attributed to the temperature activated orientational mobility of the second-order nonlinear chromophores. The polarization anisotropy between the p- and s-polarized readouts is consistent with what would be expected on the basis of directly measured effective electro-optic coefficients. By correlating the electro-optic value with the diffraction efficiency, the temperature dependence of the space-charge field is obtained and explained by temperature dependencies of the dark conductivity and the photoconductivity of the material