Search results for "Neural"
showing 10 items of 2783 documents
Polar bosons in one-dimensional disordered optical lattices
2013
We analyze the effects of disorder and quasi-disorder on the ground-state properties of ultra-cold polar bosons in optical lattices. We show that the interplay between disorder and inter-site interactions leads to rich phase diagrams. A uniform disorder leads to a Haldane-insulator phase with finite parity order, whereas the density-wave phase becomes a Bose-glass at very weak disorder. For quasi-disorder, the Haldane insulator connects with a gapped generalized incommesurate density wave without an intermediate critical region.
Machine Learning-Based Classification of Vector Vortex Beams.
2020
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. O…
Amnesia and the hippocampus
2006
PURPOSE OF REVIEW: Long-term memory impairments have great medical significance and a considerable health and economic burden. Understanding their cognitive and neuroanatomical underpinnings is of crucial importance. Severe amnesia is usually observed following bilateral hippocampal pathology. This review addresses the precise role of the hippocampus and related medial temporal lobe structures in amnesia. RECENT FINDINGS: Disagreements exist over whether, following selective hippocampal damage: retrograde amnesia for episodic memories is temporally limited or extensive and ungraded; anterograde amnesia involves both recollective and familiarity processes. It is accepted that material specif…
Embryonic neural cell adhesion molecules on human natural killer cells
1989
The neural cell adhesion molecules (NCAM) are surface glycoproteins that were first described in brain tissue. NCAM mediate adhesion in a variety of cell-cell interactions. In the present study we show that the so-called "embryonic" NCAM, i.e., the highly polysialylated forms of these proteins, are expressed on natural killer cells and some CD3+ cells in man. Homotypic binding of NCAM, believed to be of importance for cell-cell adhesion in neural tissues, appears not to be essential for NK cell-mediated killing. Yet, NCAM might be involved in NK cell migration, homing or related functions.
Solvent-free microwave-assisted extraction of polyphenols from olive tree leaves: Antioxidant and antimicrobial properties
2017
International audience; Response surface methodology (RSM) and artificial neural networks (ANN) were evaluated and compared in order to decide which method was the most appropriate to predict and optimize total phenolic content (TPC) and oleuropein yields in olive tree leaf (Olea europaea) extracts, obtained after solvent-free microwave- assisted extraction (SFMAE). The SFMAE processing conditions were: microwave irradiation power 250-350 W, extraction time 2-3 min, and the amount of sample 5-10 g. Furthermore, the antioxidant and antimicrobial activities of the olive leaf extracts, obtained under optimal extraction conditions, were assessed by several in vitro assays. ANN had better predic…
Comparative study to predict toxic modes of action of phenols from molecular structures.
2013
Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. M…
Development of handcrafted and deep based methods for face and facial expression recognition
2021
The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effectiv…
Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems
2020
International audience; Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN.
Forecasting Aquaponic Systems Behaviour With Recurrent Neural Networks Models
2022
Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the m…
Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongoli…
2020
11 pages; International audience; The present study proposes a workflow to extract from orthomosaics the enormous amount of dry stones used by past societies to construct funeral complexes in the Mongolian steppes. Several different machine learning algorithms for binary pixel classification (i.e. stone vs non-stone) were evaluated. Input features were extracted from high-resolution orthomosaics and digital elevation models (both derived from aerial imaging). Comparative analysis used two colour spaces (RGB and HSV), texture features (contrast, homogeneity and entropy raster maps), and the topographic position index, combined with nine supervised learning algorithms (nearest centroid, naive…