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representational learning

Florian Ferreri, Alexis Bourla, Stephane Mouchabac, Laurent Karila
Background: New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping , a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past...
2018: Frontiers in Psychiatry
Stefan Elmer, Joëlle Albrecht, Seyed Abolfazl Valizadeh, Clément François, Antoni Rodríguez-Fornells
Word learning constitutes a human faculty which is dependent upon two anatomically distinct processing streams projecting from posterior superior temporal (pST) and inferior parietal (IP) brain regions toward the prefrontal cortex (dorsal stream) and the temporal pole (ventral stream). The ventral stream is involved in mapping sensory and phonological information onto lexical-semantic representations, whereas the dorsal stream contributes to sound-to-motor mapping, articulation, complex sequencing in the verbal domain, and to how verbal information is encoded, stored, and rehearsed from memory...
March 15, 2018: Scientific Reports
Shuchao Pang, Mehmet A Orgun, Zhezhou Yu
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images...
May 2018: Computer Methods and Programs in Biomedicine
Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C Barratt, Sébastien Ourselin, M Jorge Cardoso, Tom Vercauteren
BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups...
May 2018: Computer Methods and Programs in Biomedicine
Daniel M Stout, Daniel E Glenn, Dean T Acheson, Andrea D Spadoni, Victoria B Risbrough, Alan N Simmons
Contextual threat learning reflects two often competing processes: configural and elemental learning. Configural threat learning is a hippocampal-dependent process of forming a conjunctive representation of a context through binding of several multi-modal elements. In contrast, elemental threat-learning is governed by the amygdala and involves forming associative relationships between individual features within the context. Contextual learning tasks in humans however, rarely probe if a learned fear response is truly due to configural learning vs...
March 12, 2018: Neurobiology of Learning and Memory
Shiri Lev-Ari
We learn language from our social environment, but the more sources we have, the less informative each source is, and therefore, the less weight we ascribe its input. According to this principle, people with larger social networks should give less weight to new incoming information, and should therefore be less susceptible to the influence of new speakers. This paper tests this prediction, and shows that speakers with smaller social networks indeed have more malleable linguistic representations. In particular, they are more likely to adjust their lexical boundary following exposure to a new speaker...
March 12, 2018: Cognition
Wei Wang, Yan Yan, Feiping Nie, Shuicheng Yan, Nicu Sebe
Graph-based dimensionality reduction techniques have been widely and successfully applied to clustering and classification tasks. The basis of these algorithms is the constructed graph which dictates their performance. In general, the graph is defined by the input affinity matrix. However, the affinity matrix derived from the data is sometimes suboptimal for dimension reduction as the data used are very noisy. To address this issue, we propose the projective unsupervised flexible embedding models with optimal graph (PUFE-OG)...
June 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Folakemi T Odedina, R R Reams, E Kaninjing, J Nguyen, B Mochona, D E Lyon, N Askins, L S Behar-Horenstein
With the growing burden of cancer in minority populations and limited progress in eliminating cancer disparities, it has become important to develop a diverse oncology workforce in basic, clinical, and behavioral research who will address cancer disparities and increase the participation of minority populations in clinical trials. To address the lack of well-trained underrepresented minority cancer scientists in Florida, the University of Florida collaborated with Florida A&M University in 2012 to establish the Florida Prostate Cancer Research Training Opportunities for Outstanding Leaders (ReTOOL) Program...
March 15, 2018: Journal of Cancer Education: the Official Journal of the American Association for Cancer Education
Natalia Mitrofanova, Marit Westergaard
This paper focuses on the acquisition of locative prepositional phrases in L1 Norwegian. We report on two production experiments with children acquiring Norwegian as their first language and compare the results to similar experiments conducted with Russian children. The results of the experiments show that Norwegian children at age 2 regularly produce locative utterances lacking overt prepositions, with the rate of preposition omission decreasing significantly by age 3. Furthermore, our results suggest that phonologically strong and semantically unambiguous locative items appear earlier in Norwegian children's utterances than their phonologically weak and semantically ambiguous counterparts...
March 15, 2018: Journal of Child Language
Zarrar Shehzad, Gregory McCarthy
Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Early functional MRI studies supported the localizationist perspective that category information was represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately...
March 14, 2018: Journal of Neurophysiology
Ewa Siucinska, Wojciech Brutkowski, Tytus Bernas
We found previously that fear conditioning by combined stimulation of a row B facial vibrissae (conditioned stimulus, CS) with a tail shock (unconditioned stimulus, UCS) leads to expansion of the cortical representation of the "trained" row, labeled with 2-deoxyglucose (2DG), in the layer IIIb/IV of the adult mouse the primary somatosensory cortex (S1) 24 h later. We have observed that learning - dependent these plastic changes are manifested by increased expression of somatostatin, cholecystokinin (SST+, CCK+) but not parvalbumin (PV+) immunopositive interneurons...
March 14, 2018: ACS Chemical Neuroscience
Justin N Wood, Samantha M W Wood
How do newborns learn to recognize objects? According to temporal learning models in computational neuroscience, the brain constructs object representations by extracting smoothly changing features from the environment. To date, however, it is unknown whether newborns depend on smoothly changing features to build invariant object representations. Here, we used an automated controlled-rearing method to examine whether visual experience with smoothly changing features facilitates the development of view-invariant object recognition in a newborn animal model-the domestic chick (Gallus gallus)...
March 14, 2018: Cognitive Science
Mengmeng Zhang, Wei Li, Qian Du
Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification...
June 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yawen Huang, Ling Shao, Alejandro F Frangi
Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions...
March 2018: IEEE Transactions on Medical Imaging
Lina Xu, Giles Tetteh, Jana Lipkova, Yu Zhao, Hongwei Li, Patrick Christ, Marie Piraud, Andreas Buck, Kuangyu Shi, Bjoern H Menze
The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM).68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner...
2018: Contrast Media & Molecular Imaging
Chia-Ling Li, M Pilar Aivar, Matthew H Tong, Mary M Hayhoe
Search is a central visual function. Most of what is known about search derives from experiments where subjects view 2D displays on computer monitors. In the natural world, however, search involves movement of the body in large-scale spatial contexts, and it is unclear how this might affect search strategies. In this experiment, we explore the nature of memory representations developed when searching in an immersive virtual environment. By manipulating target location, we demonstrate that search depends on episodic spatial memory as well as learnt spatial priors...
March 12, 2018: Scientific Reports
Christophe Gardella, Olivier Marre, Thierry Mora
The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
Marta Gómez-Sancho, Jussi Tohka, Vanessa Gómez-Verdejo
Alzheimer's Disease (AD) is a progressive neurological disorder in which the death of brain cells causes memory loss and cognitive decline. The identification of at-risk subjects yet showing no dementia symptoms but who will later convert to AD can be crucial for the effective treatment of AD. For this, Magnetic Resonance Imaging (MRI) is expected to play a crucial role. During recent years, several Machine Learning (ML) approaches to AD-conversion prediction have been proposed using different types of MRI features...
March 9, 2018: Magnetic Resonance Imaging
Mohammed Alsuhaibani, Danushka Bollegala, Takanori Maehara, Ken-Ichi Kawarabayashi
Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words...
2018: PloS One
Zhen Chen, Pei Zhao, Fuyi Li, André Leier, Tatiana T Marquez-Lago, Yanan Wang, Geoffrey I Webb, A Ian Smith, Roger J Daly, Kuo-Chen Chou, Jiangning Song
Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors...
March 8, 2018: Bioinformatics
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