Similarity Learning via Kernel Preserving Embedding

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.Comment: Published in AAAI 201

Coastal flooding in Scituate (MA) : A FVCOM study of the 27 December 2010 nor'easter

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 118 (2013): 6030–6045, doi:10.1002/2013JC008862.A nested Finite-Volume Coastal Ocean Model (FVCOM) inundation forecast model has been developed for Scituate (MA) as part of the Northeast Coastal Ocean Forecast System (NECOFS). Scituate Harbor is a small coastal lagoon oriented north-south with a narrow entrance (with opposing breakwaters) opening eastward onto Massachusetts Bay and the Gulf of Maine. On 27 December 2010, a classic nor'easter produced a ∼0.9 m high surge, which when added to the ∼1.5 m high tide and seasonal higher mean water level, produced significant inundation in Scituate. The Scituate FVCOM inundation model includes flooding/drying, seawall/breakwater, and wave-current interaction capabilities, and was driven by one-way nesting with NECOFS. Hindcasts of the 27 December nor'easter event were made with two different resolution Scituate FVCOM grids with and without inclusion of wave-current interaction to examine the influence of spatial resolution and model dynamics on the predicted flooding. In all simulations, a wind-driven coastal current flowed southward across the harbor entrance, with an attached separation eddy forming downstream of the northern breakwater and rapid decrease in wave energy entering the harbor. With wave-current interaction, the southward coastal current was strongly enhanced and currents within the separation eddy increased to more than 1 m/s, making it highly nonlinear with large lateral shears. Comparisons of the model water elevation time series with harbor tide station measurements showed that inclusion of wave-current interaction increased the peak model surge by ∼8 cm, in closer agreement with the observed peak.This project was supported by NOAA via the U.S. IOOS Office (Award: NA10NOS0120063 and NA11NOS0120141) and was managed by the Southeastern Universities Research Association. The Scituate FVCOM setup was supported by the NOAA-funded IOOS NERACOOS program for NECOFS and the MIT Sea grant College Program through grant 2012-R/RC-127.2014-05-1

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.Comment: This manuscript is the accepted version for CVPR 201

Impact of current-wave interaction on storm surge simulation : a case study for Hurricane Bob

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 118 (2013): 2685–2701, doi:10.1002/jgrc.20207.Hurricane Bob moved up the U.S. east coast and crossed over southern New England and the Gulf of Maine [with peak marine winds up to 54 m/s (100 mph)] on 19–20 August 1991, causing significant damage along the coast and shelf. A 3-D fully wave-current-coupled finite-volume community ocean model system was developed and applied to simulate and examine the coastal ocean responses to Hurricane Bob. Results from process study-oriented experiments showed that the impact of wave-current interaction on surge elevation varied in space and time, more significant over the shelf than inside the inner bays. While sea level change along the coast was mainly driven by the water flux controlled by barotropic dynamics and the vertically integrated highest water transports were essentially the same for cases with and without water stratification, the hurricane-induced wave-current interaction could generate strong vertical current shear in the stratified areas, leading to a strong offshore transport near the bottom and vertical turbulent mixing over the continental shelf. Stratification could also result in a significant difference of water currents around islands where the water is not vertically well mixed.This work was supported by the MIT Sea Grant College Program through grant 2012-R/RC-127 and the NOAA NERACOOS Program funds for NECOFS. The development of the FVCOM system has been supported by the NSF Ocean Sciences Division through grants OCE-0234545, OCE-0227679, OCE-0606928, and OCE- 0712903 and the NSF Office of Polar Programs-Arctic Sciences Division through grants ARC0712903, ARC0732084, ARC0804029, and ARC1203393. C.C.’s contribution was also supported by Shanghai Ocean University International Cooperation Program (A-2302-11-0003), the Program of Science and Technology Commission of Shanghai Municipality (09320503700), and the Leading Academic Discipline Project of Shanghai Municipal Education Commission (J50702).2013-11-3

Reply to comment on “Current separation and upwelling over the southeast shelf of Vietnam in the South China Sea”

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 118 (2013): 1624, doi:10.1002/jgrc.20114.2013-09-3

Studies of the Canadian Arctic Archipelago water transport and its relationship to basin-local forcings : results from AO-FVCOM

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 4392–4415, doi:10.1002/2016JC011634.A high-resolution (up to 2 km), unstructured-grid, fully coupled Arctic sea ice-ocean Finite-Volume Community Ocean Model (AO-FVCOM) was employed to simulate the flow and transport through the Canadian Arctic Archipelago (CAA) over the period 1978–2013. The model-simulated CAA outflow flux was in reasonable agreement with the flux estimated based on measurements across Davis Strait, Nares Strait, Lancaster Sound, and Jones Sounds. The model was capable of reproducing the observed interannual variability in Davis Strait and Lancaster Sound. The simulated CAA outflow transport was highly correlated with the along-strait and cross-strait sea surface height (SSH) difference. Compared with the wind forcing, the sea level pressure (SLP) played a dominant role in establishing the SSH difference and the correlation of the CAA outflow with the cross-strait SSH difference can be explained by a simple geostrophic balance. The change in the simulated CAA outflow transport through Davis Strait showed a negative correlation with the net flux through Fram Strait. This correlation was related to the variation of the spatial distribution and intensity of the slope current over the Beaufort Sea and Greenland shelves. The different basin-scale surface forcings can increase the model uncertainty in the CAA outflow flux up to 15%. The daily adjustment of the model elevation to the satellite-derived SSH in the North Atlantic region outside Fram Strait could produce a larger North Atlantic inflow through west Svalbard and weaken the outflow from the Arctic Ocean through east Greenland.NSF Grant Numbers: OCE-1203393, PLR-1203643; National Natural Science Foundation of China Grant Number: 41276197; Shanghai Pujiang Program Grant Number: 12PJ1404100; Shanghai Shuguang Program2016-12-2