320 research outputs found
Study of time structure pattern in news stories
News articles are a particular type of story that reflects our daily life on various media. The most distinctive characteristics of news story are its discontinuity in the ordering of events and its limited shelf life. These characteristics present difficulties for automated systems which deal with temporal information. Such applications include information extraction, question answering, summarization, machine translation, etc. In recent years, some concrete work has been done in the area of event identification and temporal expression annotation. Some issues still remain regarding implicit temporal information. This major report describes a method to find patterns in news stories that journalists use to organize events. The study will help us better understand the nature of news stories. It may also help to get implicit temporal information by the implication from the event in the same paragraph. The major report also presents an annotation scheme for annotating temporal expressions and paragraphs in new texts. In this study, we manually annotated all temporal expressions and paragraphs in selected news texts. We build the time structure for each of the texts according to the story time of the paragraphs. After all the target news texts are analyzed and annotated, we compare time structures against each other to derive time structure patterns that may exist in news stories
Synchro-Transient-Extracting Transform for the Analysis of Signals with Both Harmonic and Impulsive Components
Time-frequency analysis (TFA) techniques play an increasingly important role
in the field of machine fault diagnosis attributing to their superiority in
dealing with nonstationary signals. Synchroextracting transform (SET) and
transient-extracting transform (TET) are two newly emerging techniques that can
produce energy concentrated representation for nonstationary signals. However,
SET and TET are only suitable for processing harmonic signals and impulsive
signals, respectively. This poses a challenge for each of these two techniques
when a signal contains both harmonic and impulsive components. In this paper,
we propose a new TFA technique to solve this problem. The technique aims to
combine the advantages of SET and TET to generate energy concentrated
representations for both harmonic and impulsive components of the signal.
Furthermore, we theoretically demonstrate that the proposed technique retains
the signal reconstruction capability. The effectiveness of the proposed
technique is verified using numerical and real-world signals
Breaking of brightness consistency in optical flow with a lightweight CNN network
Sparse optical flow is widely used in various computer vision tasks, however
assuming brightness consistency limits its performance in High Dynamic Range
(HDR) environments. In this work, a lightweight network is used to extract
illumination robust convolutional features and corners with strong invariance.
Modifying the typical brightness consistency of the optical flow method to the
convolutional feature consistency yields the light-robust hybrid optical flow
method. The proposed network runs at 190 FPS on a commercial CPU because it
uses only four convolutional layers to extract feature maps and score maps
simultaneously. Since the shallow network is difficult to train directly, a
deep network is designed to compute the reliability map that helps it. An
end-to-end unsupervised training mode is used for both networks. To validate
the proposed method, we compare corner repeatability and matching performance
with origin optical flow under dynamic illumination. In addition, a more
accurate visual inertial system is constructed by replacing the optical flow
method in VINS-Mono. In a public HDR dataset, it reduces translation errors by
93\%. The code is publicly available at https://github.com/linyicheng1/LET-NET.Comment: 7 pages,7 figure
Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis
NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names
Addressing students by their names helps a teacher to start building rapport
with students and thus facilitates their classroom participation. However, this
basic yet effective skill has become rather challenging for university
lecturers, who have to handle large-sized (sometimes exceeding 100) groups in
their daily teaching. To enhance lecturers' competence in delivering
interpersonal interaction, we developed NaMemo, a real-time name-indicating
system based on a dedicated face-recognition pipeline. This paper presents the
system design, the pilot feasibility test, and our plan for the following
study, which aims to evaluate NaMemo's impacts on learning and teaching, as
well as to probe design implications including privacy considerations.Comment: DIS '20 Companio
Prioritization of disease microRNAs through a human phenome-microRNAome network
<p>Abstract</p> <p>Background</p> <p>The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.</p> <p>Results</p> <p>Herein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.</p> <p>Conclusions</p> <p>We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.</p
Methylation of NF-ĪŗB and its Role in Gene Regulation
The nuclear factor ĪŗB (NF-ĪŗB) is one of the most pivotal transcription factors in mammalian cells. In many pathologies NF-ĪŗB is activated abnormally. This contributes to the development of various disorders such as cancer, acute kidney injury, lung disease, chronic inflammatory diseases, cardiovascular disease, and diabetes. This book chapter focuses on how methylation of NF-ĪŗB regulates its target genes differentially. The knowledge from this chapter will provide scientific strategies for innovative therapeutic intervention of NF-ĪŗB in a wide range of diseases
Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer
Background: Proper cell models for breast cancer primary tumors have long been the focal point in the cancerās
research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity
and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this
paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein
expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented.
Results: Using whole genome expression arrays, strong correlations were observed between cells and tumors.
PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp,
and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells
across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors,
while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data
can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR
and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor,
low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse
protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2,
EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1
are two promising drug targets for breast cancer. A total score developed from the four correlations among four
molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors.
Conclusions: The integrated data from across these multiple platforms demonstrates the existence of the similarity
and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but
not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models
for breast cancer research
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