Search results for "Pattern recognition"
showing 10 items of 2301 documents
Minimal learning machine in anomaly detection from hyperspectral images
2020
Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.
Genome-Wide Inhibition of Pro-atherogenic Gene Expression by Multi-STAT Targeting Compounds as a Novel Treatment Strategy of CVDs.
2018
Cardiovascular diseases (CVDs), including atherosclerosis, are globally the leading cause of death. Key factors contributing to onset and progression of atherosclerosis include the pro-inflammatory cytokines Interferon (IFN)a and IFN? and the Pattern Recognition Receptor (PRR) Toll-like receptor 4 (TLR4). Together, they trigger activation of Signal Transducer and Activator of Transcription (STAT)s. Searches for compounds targeting the pTyr-SH2 interaction area of STAT3, yielded many small molecules, including STATTIC and STX-0119. However, many of these inhibitors do not seem STAT3-specific. We hypothesized that multi-STAT-inhibitors that simultaneously block STAT1, STAT2, and STAT3 activit…
TLR7 controls VSV replication in CD169(+) SCS macrophages and associated viral neuroinvasion
2019
Vesicular stomatitis virus (VSV) is an insect-transmitted rhabdovirus that is neurovirulent in mice. Upon peripheral VSV infection, CD169+ subcapsular sinus (SCS) macrophages capture VSV in the lymph, support viral replication, and prevent CNS neuroinvasion. To date, the precise mechanisms controlling VSV infection in SCS macrophages remain incompletely understood. Here, we show that Toll-like receptor-7 (TLR7), the main sensing receptor for VSV, is central in controlling lymph-borne VSV infection. Following VSV skin infection, TLR7−/− mice display significantly less VSV titers in the draining lymph nodes (dLN) and viral replication is attenuated in SCS macrophages. In contrast to effects o…
Special Issue on Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition
2018
This special issue of Algorithms is devoted to the study of Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition. The special issue considered both theoretical contributions able to advance the state-of-the-art in this field and practical applications that describe novel approaches for solving real-world problems.
Multispectral analysis of color vision deficiency tests
2011
Color deficiency tests are usually produced by means of polygraphy technologies and help to diagnose the type and severity of the color deficiencies. Due to different factors, as lighting conditions or age of the test, standard characteristics of these tests fail, thus not allowing diagnosing unambiguously the degree of different color deficiency. Multispectral camera was used to acquire the spectral images of the Ishihara and Rabkin pseudoisochromatic plates in the visible spectrum. Spectral data was converted to cone signals, and successive mathematics applied to provide a simple simulation of the test performance. Colorimetric data of the each pixel of the test image can be calculated an…
A study of the relationship between regulatory systems, assessment e locomotion, and online learning groups
2011
The present paper examines the relationship between assessment, locomotion and attitudes and learning outcomes in a Computer-supported collaborative learning (CSCL) framework. Results showed that regulatory mode predicted exam marks, numbers of tasks completed, messages sent and attitudes towards the course and the ingroup. The theoretical implications and some reflections about CSCL and Regulatory-mode Theory (RMT) are presented.
Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
2018
Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an …
Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images
2022
Abstract Purpose Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. Methods We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorp…
Dynamics of brain activation during learning of syllable-symbol paired associations.
2019
| openaire: EC/H2020/641652/EU//ChildBrain Initial stages of reading acquisition require the learning of letter and speech sound combinations. While the long-term effects of audio-visual learning are rather well studied, relatively little is known about the short-term learning effects at the brain level. Here we examined the cortical dynamics of short-term learning using magnetoencephalography (MEG) and electroencephalography (EEG) in two experiments that respectively addressed active and passive learning of the association between shown symbols and heard syllables. In experiment 1, learning was based on feedback provided after each trial. The learning of the audio-visual associations was c…
Does orthographic processing emerge rapidly after learning a new script?
2021
Epub 2020 Aug 11 Orthographic processing is characterized by location-invariant and location-specific processing (Grainger, 2018): (1) strings of letters are more vulnerable to transposition effects than the strings of symbols in same-different tasks (location-invariant processing); and (2) strings of letters, but not strings of symbols, show an initial position advantage in target-in-string identification tasks (location-specific processing). To examine the emergence of these two markers of orthographic processing, we conducted a same-different task and a target-in-string identification task with two unfamiliar scripts (pre-training experiments). Across six training sessions, participants …