A new kernel method for hyperspectral image feature extraction

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required

Optimized kernel minimum noise fraction transformation for hyperspectral image classification

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy

Epidemic modelling by ripple-spreading network and genetic algorithm

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results

Abstractive Multi-Document Summarization via Phrase Selection and Merging

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201

Mapping the mammalian ribosome quality control complex interactome using proximity labeling approaches.

Previous genetic and biochemical studies from Saccharomyces cerevisiae have identified a critical ribosome-associated quality control complex (RQC) that facilitates resolution of stalled ribosomal complexes. While components of the mammalian RQC have been examined in vitro, a systematic characterization of RQC protein interactions in mammalian cells has yet to be described. Here we utilize both proximity-labeling proteomic approaches, BioID and APEX, and traditional affinity-based strategies to both identify interacting proteins of mammalian RQC members and putative substrates for the RQC resident E3 ligase, Ltn1. Surprisingly, validation studies revealed that a subset of substrates are ubiquitylated by Ltn1 in a regulatory manner that does not result in subsequent substrate degradation. We demonstrate that Ltn1 catalyzes the regulatory ubiquitylation of ribosomal protein S6 kinase 1 and 2 (RPS6KA1, RPS6KA3). Further, loss of Ltn1 function results in hyperactivation of RSK1/2 signaling without impacting RSK1/2 protein turnover. These results suggest that Ltn1-mediated RSK1/2 ubiquitylation is inhibitory and establishes a new role for Ltn1 in regulating mitogen-activated kinase signaling via regulatory RSK1/2 ubiquitylation. Taken together, our results suggest that mammalian RQC interactions are difficult to observe and may be more transient than the homologous complex in S. cerevisiae and that Ltn1 has RQC-independent functions

Imaging diagnosis of chronic encapsulated intracerebral hematoma : a comparison of computed tomography (CT) and magnetic resonance imaging (MRI) characteristics

Background: Chronic encapsulated intracerebral hematoma (CEICH) is a rare type of intracerebral hematoma that is often misdiagnosed. To explore the characteristics of CEICH on computerized tomography (CT) and magnetic resonance imaging (MRI). Material/Methods: Clinical, CT, MRI, and susceptibility weighted imaging (SWI) data of 5 patients who were diagnosed with CEICH on surgery and pathology were retrospectively analyzed. Results: CT showed quasi-circular or elliptical lesions with clear borders in all 5 cases and iso-density or low-density in the center of lesions that were surrounded by peripheral edema in 2 cases. CT showed mass effect in 5 patients. On contrast-enhanced CT, 2 cases exhibited mild ring enhancement, and 3 cases exhibited moderate ring enhancement. MRI showed cystic lesions with high uniform signal on T1-weighted images (T1WI) and T2-weighted images (T2WI), a lowsignal ring sign on the coated cystic lesions on T2WI, a lower signal ring sign on SWI, and ring enhancement after administration of contrast. Conclusions: CT imaging of CEICH did not reveal any typical characteristics in the studied patients. MRI showed an envelope with a "ring" an