Search results for "Cognition"

Deep learning and process understanding for data-driven Earth system science

2017

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybri…

Choosing Optimal Seed Nodes in Competitive Contagion.

2019

International audience; In recent years there has been a growing interest in simulating competitive markets to find out the efficient ways to advertise a product or spread an ideology. Along this line, we consider a binary competitive contagion process where two infections, A and B, interact with each other and diffuse simultaneously in a network. We investigate which is the best centrality measure to find out the seed nodes a company should adopt in the presence of rivals so that it can maximize its influence. These nodes can be used as the initial spreaders or advertisers by firms when two firms compete with each other. Each node is assigned a price tag to become an initial advertiser whi…

Cluster-based active learning for compact image classification

2010

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer…

Binge-like alcohol exposure in adolescence: behavioural, neuroendocrine and molecular evidence of abnormal neuroplasticity … and return

2021

Binge alcohol consumption among adolescents affects the developing neural networks underpinning reward and stress processing in the nucleus accumbens (NAc). This study explores in rats the long-lasting effects of early intermittent exposure to intoxicating alcohol levels at adolescence, on: (1) the response to natural positive stimuli and inescapable stress

2017

In continuous flash suppression (CFS), a dynamic noise masker, presented to one eye, suppresses conscious perception of a test stimulus, presented to the other eye, until the suppressed stimulus comes to awareness after few seconds. But what do we see breaking the dominance of the masker in the transition period? We addressed this question with a dual-task in which observers indicated (i) whether the test object was left or right of the fixation mark (localization) and (ii) whether it was a face or a house (categorization). As done recently (Stein et al., 2011), we used two experimental varieties to rule out confounds with decisional strategy. In the terminated mode, stimulus and masker wer…

Radiomics and Prostate MRI: Current Role and Future Applications

2021

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine …

Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors.

2013

Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single…

Mammary pheromone-induced odour learning influences sucking behaviour and milk intake in the newborn rabbit

2016

Newborn rabbits, Oryctolagus cuniculus, locate their mother's nipples through typical orocephalic movements elicited by odour stimuli, in particular by the mammary pheromone (MP). The MP also promotes neonatal odour learning: after single pairing with the MP, an initially neutral odorant becomes able to elicit sucking-related head-searching/oral-grasping movements. However, the behavioural significance of the MP-induced odour learning remains poorly understood. We carried out three experiments to explore its influence on milk intake and compare its consequences with those resulting from nursing-induced conditioning. First, pups conditioned to an odorant by pairing with the MP on postnatal d…

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

2017

Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes. In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders. The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current st…

BIOfid dataset: publishing a German gold standard for named entity recognition in historical biodiversity literature

2019

The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and c…