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Semantic maps

Natalya Zavyalova
The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure. While designing a research topic, scientists usually employ their individual background. They rely in most cases on their individual assumptions and hypotheses. The strategy, introduced in the article, highlights the method of identifying components of semantic maps which can lead to a better coverage of any scientific topic under analysis...
April 2017: Data in Brief
Mallory C Stites, Sarah Laszlo
ERPs are a powerful tool for the study of reading, as they are both temporally precise and functionally specific. These are essential characteristics for studying a process that unfolds rapidly and consists of multiple, interactive subprocesses. In work with adults, clear, specific models exist linking components of the ERP with individual subprocesses of reading including orthographic decoding, phonological processing, and semantic access (e.g., Grainger & Holcomb, 2009). The relationships between ERP components and reading subprocesses are less clear in development; here, we address two questions regarding these relationships...
February 23, 2017: Psychophysiology
Sandrine Bisenius, Karsten Mueller, Janine Diehl-Schmid, Klaus Fassbender, Timo Grimmer, Frank Jessen, Jan Kassubek, Johannes Kornhuber, Bernhard Landwehrmeyer, Albert Ludolph, Anja Schneider, Sarah Anderl-Straub, Katharina Stuke, Adrian Danek, Markus Otto, Matthias L Schroeter
Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration...
2017: NeuroImage: Clinical
Zhizhen Chi, Hongyang Li, Huchuan Lu, Minghsuan Yang
Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jan Volin, Lenka Weingartová, Oliver Niebuhr
Words like yeah, okay and (al)right are fairly unspecific in their lexical semantics, and not least for this reason there is a general tendency for them to occur with highly varied and expressive prosodic patterns across languages. Here we examine in depth the prosodic forms that express eight pragmatic functions of the Czech discourse marker jasně, including resignation, reassurance, surprise, indifference or impatience. Using a collection of 172 tokens from a corpus of scripted dialogues by 30 native speakers, we performed acoustic analyses, applied classification algorithms and solicited judgments from native listeners in a perceptual experiment...
2016: Phonetica
Feiyang Cheng, Xuming He, Hong Zhang
Search-based structured prediction methods have shown promising successes in both computer vision and natural language processing recently. However, most existing search-based approaches lead to a complex multi-stage learning process, which is ill-suited for scene labeling problems with a high-dimensional output space. In this paper, a stacked learning to search method is proposed to address scene labeling tasks. We design a simplified search process consisting of a sequence of ranking functions, which are learned based on a stacked learning strategy to prevent over-fitting...
February 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Malihe Javidi, Hamid-Reza Pourreza, Ahad Harati
Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image...
February 2017: Computer Methods and Programs in Biomedicine
David Gomez-Cabrero, Jörg Menche, Claudia Vargas, Isaac Cano, Dieter Maier, Albert-László Barabási, Jesper Tegnér, Josep Roca
BACKGROUND: Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers. RESULTS: Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity...
November 22, 2016: BMC Bioinformatics
Mengyang Yu, Li Liu, Ling Shao
Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies. In this paper, we propose a novel unsupervised binary coding algorithm called binary set embedding (BSE) to obtain meaningful hash codes for local features from the image domain and words from text domain...
September 27, 2016: IEEE Transactions on Neural Networks and Learning Systems
Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Xiaohui Shen, Ming-Ming Cheng, Jiashi Feng, Yao Zhao, Shuicheng Yan
Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. In this paper, we propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically, we first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i...
December 6, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Sheng Guo, Weilin Huang, Limin Wang, Yu Qiao
Convolutional neural networks (CNN) have recently achieved remarkable successes in various image classification and understanding tasks. The deep features obtained at the top fully-connected layer of the CNN (FC-features) exhibit rich global semantic information and are extremely effective in image classification. On the other hand, the convolutional features in the middle layers of the CNN also contain meaningful local information, but are not fully explored for image representation. In this paper, we propose a novel Locally-Supervised Deep Hybrid Model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition...
November 16, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Sihong Chen, Jing Qin, Xing Ji, Baiying Lei, Tianfu Wang, Dong Ni, Jie-Zhi Cheng
The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images...
November 16, 2016: IEEE Transactions on Medical Imaging
Yong Shui, Young-Rae Cho
Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping...
April 21, 2016: IEEE Transactions on Nanobioscience
Satoshi Oshima, Rika Mochizuki, Reiner Lenz, Jinhui Chao
We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color weak observers. The two main applications are the simulation of the perception of a color weak observer for a color normal observer and the compensation of color images in a way that a color weak observer has approximately the same perception as a color normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticable-differences between color which are measured with the help of color-matching experiments...
March 8, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Kaibao Sun, Rong Xue, Peng Zhang, Zhentao Zuo, Zhongwei Chen, Bo Wang, Thomas Martin, Yi Wang, Lin Chen, Sheng He, Danny J J Wang
Balanced steady-state free precession (bSSFP) offers an alternative and potentially important tool to the standard gradient-echo echo-planar imaging (GE-EPI) for functional MRI (fMRI). Both passband and transition band based bSSFP have been proposed for fMRI. The applications of these methods, however, are limited by banding artifacts due to the sensitivity of bSSFP signal to off-resonance effects. In this article, a unique case of the SSFP-FID sequence, termed integrated-SSFP or iSSFP, was proposed to overcome the obstacle by compressing the SSFP profile into the width of a single voxel...
December 30, 2016: Journal of Magnetic Resonance
Kyle Rupp, Matthew Roos, Griffin Milsap, Carlos Caceres, Christopher Ratto, Mark Chevillet, Nathan E Crone, Michael Wolmetz
Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses...
January 11, 2017: NeuroImage
Francesca Carota, Nikolaus Kriegeskorte, Hamed Nili, Friedemann Pulvermüller
Language comprehension engages a distributed network of frontotemporal, parietal, and sensorimotor regions, but it is still unclear how meaning of words and their semantic relationships are represented and processed within these regions and to which degrees lexico-semantic representations differ between regions and semantic types. We used fMRI and representational similarity analysis to relate word-elicited multivoxel patterns to semantic similarity between action and object words. In left inferior frontal (BA 44-45-47), left posterior middle temporal and left precentral cortex, the similarity of brain response patterns reflected semantic similarity among action-related verbs, as well as across lexical classes-between action verbs and tool-related nouns and, to a degree, between action verbs and food nouns, but not between action verbs and animal nouns...
January 10, 2017: Cerebral Cortex
Philipp Bruland, Martin Dugas
BACKGROUND: Data capture for clinical registries or pilot studies is often performed in spreadsheet-based applications like Microsoft Excel or IBM SPSS. Usually, data is transferred into statistic software, such as SAS, R or IBM SPSS Statistics, for analyses afterwards. Spreadsheet-based solutions suffer from several drawbacks: It is generally not possible to ensure a sufficient right and role management; it is not traced who has changed data when and why. Therefore, such systems are not able to comply with regulatory requirements for electronic data capture in clinical trials...
January 7, 2017: BMC Medical Informatics and Decision Making
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1]. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification...
January 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Ying Zhao, Luping Song, Junhua Ding, Nan Lin, Qiang Wang, Xiaoxia Du, Rong Sun, Zaizhu Han
: The organizational principles of semantic memory in the human brain are still controversial. Although studies have shown that the semantic system contains hub regions that bind information from different sensorimotoric modalities to form concepts, it is unknown whether there are hub regions other than the anterior temporal lobe (ATL). Meanwhile, previous studies have rarely used network measurements to explore the hubs or correlated network indexes with semantic performance, although the most direct supportive evidence of hubs should come from the network perspective...
January 4, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
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