Search results for "Temporal"
showing 10 items of 1095 documents
Evaluation of Structural and Temporal Properties of Ego Networks for Data Availability in DOSNs
2017
The large diffusion of Online Social Networks (OSNs) has influenced the way people interact with each other. OSNs present several drawbacks, one of the most important is the problem of privacy disclosures. Distributed Online Social Networks (DOSNs) have been proposed as a valid alternative solution to solve this problem. DOSNs are Online Social Networks implemented on a distributed platform, such as a P2P system or a mobile network. However, the decentralization of the control presents several challenges, one of the main ones is guaranteeing data availability without relying on a central server. To this aim, users’ data allocation strategies have to be defined and this requires the knowledg…
A Visual Simulation Framework For Simultaneous Multithreading Architectures
2011
The computing systems, and particularly microarchitectures, are in a continuous expansion reaching an unmanageable complexity by the human mind. In order to understand and control this expansion, researchers need to design and implement larger and more complex systems’ simulators. In the current paradigm the simulators play the key role in going further, by translating all complex processing mechanisms in relevant and easy to understand information. This paper aims to make a suggestive description of the concepts and principles implemented into a Simultaneous Multithreading Architecture. We introduce the SMTAHSim framework, an educational tool that simulates in an interactive manner the imp…
Single neuron binding properties and the magical number 7
2008
When we observe a scene, we can almost instantly recognize a familiar object or can quickly distinguish among objects differing by apparently minor details. Individual neurons in the medial temporal lobe of humans have been shown to be crucial for the recognition process, and they are selectively activated by different views of known individuals or objects. However, how single neurons could implement such a sparse and explicit code is unknown and almost impossible to investigate experimentally. Hippocampal CA1 pyramidal neurons could be instrumental in this process. Here, in an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (ob…
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…
Temporal Binding in Multisensory and Motor-Sensory Contexts: Toward a Unified Model
2021
Our senses receive a manifold of sensory signals at any given moment in our daily lives. For a coherent and unified representation of information and precise motor control, our brain needs to temporally bind the signals emanating from a common causal event and segregate others. Traditionally, different mechanisms were proposed for the temporal binding phenomenon in multisensory and motor-sensory contexts. This paper reviews the literature on the temporal binding phenomenon in both multisensory and motor-sensory contexts and suggests future research directions for advancing the field. Moreover, by critically evaluating the recent literature, this paper suggests that common computational prin…
Time in Associative Learning: A Review on Temporal Maps
2021
Ability to recall the timing of events is a crucial aspect of associative learning. Yet, traditional theories of associative learning have often overlooked the role of time in learning association and shaping the behavioral outcome. They address temporal learning as an independent and parallel process. Temporal Coding Hypothesis is an attempt to bringing together the associative and non-associative aspects of learning. This account proposes temporal maps, a representation that encodes several aspects of a learned association, but attach considerable importance to the temporal aspect. A temporal map helps an agent to make inferences about missing information by applying an integration mechan…
Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval
2013
Abstract ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we f…
''Investigating reduction of dimensionality during single-joint elbow movements: a case study on muscle synergies''
2013
Chiovetto, Enrico | Berret, Bastien | Delis, Ioannis | Panzeri, Stefano | Pozzo, Thierry; International audience; ''A long standing hypothesis in the neuroscience community is that the central nervous system (CNS) generates the muscle activities to accomplish movements by combining a relatively small number of stereotyped patterns of muscle activations, often referred to as" muscle synergies." Different definitions of synergies have been given in the literature. The most well-known are those of synchronous, time-varying and temporal muscle synergies. Each one of them is based on a different mathematical model used to factor some EMG array recordings collected during the execution of variety…
Spatial-temporal interactions in the human brain
2009
The review summarises current evidence on the cognitive mechanisms for the integration of spatial and temporal representations and of common brain structures to process the where and when of stimuli. Psychophysical experiments document the presence of spatially localised distortions of sub-second time intervals and suggest that visual events are timed by neural mechanisms that are spatially selective. On the other hand, experiments with supra-second intervals suggest that time could be represented on a mental time-line ordered from left-to-right, similar to what is reported for other ordered quantities, such as numbers. Neuroimaging and neuropsychological findings point towards the posterio…