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

Impact of Political Freedom, Economic Freedom and Political Stability on Economic Growth

The focus of most previous studies on institutions ignores the full range of political and economic institutions that operate in most countries and their importance for determining economic growth. This study fulfills this research gap and endeavored to identify the direct effect of political freedom, economic freedom and political stability on economic growth by using a Panel Data set of 117 countries covering time period from 1980 to 2012. The data was analyzed using the alternative econometric methodologies including panel ordinary least square (OLS), Panel fix effect (FE) and dynamic system generalized method of movements (SGMM).The results revealed that economic freedom and political stability have positive and statistically robust impact on economic growth while we observed a fragile mixed positive and negative effect of political freedom on economic growth.. Keywords: Political Freedom; Economic Freedom; Political Stability; Generalized Method of Moments; panel data

Does Foreign Political Instability Hinder China’s Export?

China is able to export substantially despite of adverse political milieu in host nations. Such phenomenon is quite novel in existing literature, therefore current study explores the impact of foreign political instability on Chinese exports by using Panel Data of 134 countries & territories from 1992 to 2011. The results revealed that foreign political instability have positive and statistically robust impact on Chinese exports. The political instability in major and minor trading partners of China affects its trade positively but impact is higher with major trading partners. Elasticity of political instability in middle and upper middle income groups is positive and statistically significant, similar situation prevails in Middle East and North African region while it is positive and insignificant in all other regions. Keywords: Political Instability, Exports, Generalized Method of Moments, Chin

Influence of Initial State Errors on Perturbation Guidance Accuracy

The inertial navigation system is aligned and leveled before the launch of a long-range vehicle. However, the initial state errors caused by the non-uniformity of the Earth can influence the parameters in flight dynamics, which will bring about serious uncertainty for the impact point of a long-range vehicle. Firstly, this paper analyses the influence mechanism of initial state errors on nominal trajectory, navigation trajectory and guidance trajectory. Then, a propagation model of engine-cutoff state deviation caused by initial state errors is derived under the condition of without-guidance. On this basis, an accuracy analytical solution of initial state errors on perturbation guidance is finally proposed to obtain the real impact-point of the long-range vehicle. In the simulations, the influence properties of initial state errors on perturbation guidance is analysed, give influence regularities of single initial state error, and obtain the statistical properties of engine-cutoff state deviations and impact-point deviation by Monte Carlo technique. From the simulation results, it seems that the navigation state tracks the nominal state. However, the real impact- point deviation has not been truly eliminated, instead of the almost target-hit deviation calculated by navigation output. The proposed analytical guidance accuracy model can be rapidly computed to provide a compensation for guidance and control system to improve hit accuracy

TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model

Prompt tuning represents a valuable technique for adapting pre-trained visual-language models (VLM) to various downstream tasks. Recent advancements in CoOp-based methods propose a set of learnable domain-shared or image-conditional textual tokens to facilitate the generation of task-specific textual classifiers. However, those textual tokens have a limited generalization ability regarding unseen domains, as they cannot dynamically adjust to the distribution of testing classes. To tackle this issue, we present a novel Textual-based Class-aware Prompt tuning(TCP) that explicitly incorporates prior knowledge about classes to enhance their discriminability. The critical concept of TCP involves leveraging Textual Knowledge Embedding (TKE) to map the high generalizability of class-level textual knowledge into class-aware textual tokens. By seamlessly integrating these class-aware prompts into the Text Encoder, a dynamic class-aware classifier is generated to enhance discriminability for unseen domains. During inference, TKE dynamically generates class-aware prompts related to the unseen classes. Comprehensive evaluations demonstrate that TKE serves as a plug-and-play module effortlessly combinable with existing methods. Furthermore, TCP consistently achieves superior performance while demanding less training time. Code:https://github.com/htyao89/Textual-based_Class-aware_prompt_tuning/Comment: accepted by CVPR2

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