1,076 research outputs found
A neural model for the visual tuning properties of action-selective neurons
SUMMARY: The recognition of actions of conspecifics is crucial for survival and social interaction. Most current models on the recognition of transitive (goal-directed) actions rely on the hypothesized role of internal motor simulations for action recognition. However, these models do not specify how visual information can be processed by cortical mechanisms in order to be compared with such motor representations. This raises the question how such visual processing might be accomplished, and in how far motor processing is critical in order to account for the visual properties of action-selective neurons.
We present a neural model for the visual processing of transient actions that is consistent with physiological data and that accomplishes recognition of grasping actions from real video stimuli. Shape recognition is accomplished by a view-dependent hierarchical neural architecture that retains some coarse position information on the highest level that can be exploited by subsequent stages. Additionally, simple recurrent neural circuits integrate effector information over time and realize selectivity for temporal sequences. A novel mechanism combines information about the shape and position of object and effector in an object-centered frame of reference. Action-selective model neurons defined in such a relative reference frame are tuned to learned associations between object and effector shapes, as well as their relative position and motion. 
We demonstrate that this model reproduces a variety of electrophysiological findings on the visual properties of action-selective neurons in the superior temporal sulcus, and of mirror neurons in area F5. Specifically, the model accounts for the fact that a majority of mirror neurons in area F5 show view dependence. The model predicts a number of electrophysiological results, which partially could be confirmed in recent experiments.
We conclude that the tuning of action-selective neurons given visual stimuli can be accounted for by well-established, predominantly visual neural processes rather than internal motor simulations.

METHODS: The shape recognition relies on a hierarchy of feature detectors of increasing complexity and invariance [1]. The mid-level features are learned from sequences of gray-level images depicting segmented views of hand and object shapes. The highest hierarchy level consists of detector populations for complete shapes with a coarse spatial resolution of approximately 3.7°. Additionally, effector shapes are integrated over time by asymmetric lateral connections between shape detectors using a neural field approach [2]. These model neurons thus encode actions such as hand opening or closing for particular grip types. 
We exploit gain field mechanism in order to implement the central coordinate transformation of the shape representations to an object-centered reference frame [3]. Typical effector-object-interactions correspond to activity regions in such a relative reference frame and are learned from training examples. Similarly, simple motion-energy detectors are applied in the object-centered reference frame and encode relative motion. The properties of transitive action neurons are modeled as a multiplicative combination of relative shape and motion detectors.

RESULTS: The model performance was tested on a set of 160 unsegmented sequences of hand grasping or placing actions performed on objects of different sizes, using different grip types and views. Hand actions and objects could be reliably recognized despite their mutual occlusions. Detectors on the highest level showed correct action tuning in more than 95% of the examples and generalized to untrained views. 
Furthermore, the model replicates a number of electrophysiological as well as imaging experiments on action-selective neurons, such as their particular selectivity for transitive actions compared to mimicked actions, the invariance to stimulus position, and their view-dependence. In particular, using the same stimulus set the model nicely fits neural data from a recent electrophysiological experiment that confirmed sequence selectivity in mirror neurons in area F5, as was predicted before by the model.

References
[1] Serre, T. et al. (2007): IEEE Pattern Anal. Mach. Int. 29, 411-426.
[2] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192.
[3] Deneve, S. and Pouget, A. (2003). Neuron 37: 347-359.

Fronteras múltiples: reconfiguración de ejes identitarios en migraciones contemporáneas a la Argentina
Estudiar las identidades desde una perspectiva constructivista y relacional significa problematizar muchas de las respuestas preconcebidas en torno de la estructuración social y de los modos de conformación de colectivos sociales. Se trata de poner en suspenso una concepción de la sociedad como todo estructurado cuya lógica interna es conocida de antemano, y de la que se conocen de antemano también los grupos o sectores que la integran, los modos en que unos marcan sus diferencias respecto de los otros, y los intereses y propósitos que los reúnen y movilizan (o los que se presume deberían hacerlo). La emergencia del interrogante acerca de la constitución de identidades sociales, pues, puso de relieve las dinámicas contingentes, localmente condicionadas y conflictivas de la conformación de grupos y colectivos, y enfatizó el carácter abierto de lo social
Knowledge, attitudes, and behavior concerning dental trauma among parents of children attending primary school
BACKGROUND: Traumatic dental injuries occur frequently in children and adolescents. The purpose of the present study is to examine the levels of knowledge and behaviors regarding dental trauma among parents of children attending primary schools in the Apulia region of Italy.
METHODS: The study was carried out using an anonymous questionnaire with closed answers distributed to 2,775 parents who were enrolled based on the entire regional school population. Analyses were conducted using the PROC CORRESP (procedure to perform multiple correspondence analysis) and PROC FASTCLUS (procedure to perform cluster analysis). Statistical significance was set at p-value <0.05.
RESULTS: A total 15.5% of the sample reported that their children had experienced dental trauma. Overall, 53.8% of respondents stated that they knew what to do in cases of dental injury. Regarding the time limit within which it is possible to usefully intervene for dental trauma, 56.8% of respondents indicated "within 30 minutes". Of the total sample, 56.5% knew how to preserve a displaced tooth. A total 62.9% of parents felt it was appropriate for their children to use dental guards during sports activities. The multivariate analysis showed that wrong knowledge are distributed among all kinds of subject. Parents with previous experience of dental trauma referred right behaviours, instead weak knowledge and wrong behaviours are associated with parents that easily worried for dental events.
CONCLUSIONS: This study showed that most parents reported no experience of dental trauma in their children, and half of them did not know what to do in case of traumatic dental injury and they would intervene within 30 minutes, suggesting that dental trauma may trigger panic. However, they did not have the information needed to best assist the affected child. Motivating parents to assume a preventive approach towards dental trauma may produce positive changes that would result an increase of long-term health benefits among both parents and children
Multiobjective Optimization of Cement-Based Panels Enhanced with Microencapsulated Phase Change Materials for Building Energy Applications
Thermal energy storage using phase change materials (PCMs) is a promising technology for improving the thermal performance of buildings and reducing their energy consumption. However, the effectiveness of passive PCMs in buildings depends on their optimal design regarding the building typology and typical climate conditions. Within this context, the present contribution introduces a novel multiobjective computational method to optimize the thermophysical properties of cementitious building panels enhanced with a microencapsulated PCM (MPCM). To achieve this, a parametric model for PCM-based cementitious composites is developed in EnergyPlus, considering as design variables the melting temperature of PCMs and the thickness and thermal conductivity of the panel. A multiobjective genetic algorithm is dynamically coupled with the building energy model to find the best trade-off between annual heating and cooling loads. The optimization results obtained for a case study building in Sofia (Bulgaria-EU) reveal that the annual heating and cooling loads have contradictory performances regarding the thermophysical properties studied. A thick MPCM-enhanced panel with a melting temperature of 22 (Formula presented.) C is needed to reduce the heating loads, while a thin panel with a melting temperature of 27 (Formula presented.) C is required to mitigate the cooling loads. Using these designs, the annual heating and cooling loads decrease by 23% and 3%, respectively. Moreover, up to 12.4% cooling load reduction is reached if the thermal conductivity of the panels is increased. Therefore, it is also concluded that the thermal conductivity of the cement-based panels can significantly influence the effectiveness of MPCMs in buildings
Intelligent cloud manufacturing platform for efficient resource sharing in smart manufacturing networks
Abstract Modern manufacturing demands are characterized by high fluctuations with negative impact on resource efficiency. In this framework, Industry 4.0 key enabling technologies such as cloud manufacturing enable the sharing of distributed resources for effective use at industrial network level. In this work, an intelligent cloud manufacturing platform is proposed to increase resource efficiency in a manufacturing network through dynamic sharing of manufacturing services, including computational, software as well as physical manufacturing resources, that can be offered on demand according to a service-oriented paradigm. The cloud-based platform includes a database module where user input data are collected, an intelligent module for data processing, optimization and feasible solutions generation, and a decision support module for solutions evaluation and comparison. A case study demonstrates technical and economic advantages for industrial resource efficiency improvement
Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts
This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios
Mirror neurons in monkey area F5 do not adapt to the observation of repeated actions
Repetitive presentation of the same visual stimulus entails a response decrease in the action potential discharge of neurons in various areas of the monkey visual cortex. It is still unclear whether this repetition suppression effect is also present in single neurons in cortical premotor areas responding to visual stimuli, as suggested by the human functional magnetic resonance imaging literature. Here we report the responses of 'mirror neurons' in monkey area F5 to the repeated presentation of action movies. We find that most single neurons and the population at large do not show a significant decrease of the firing rate. On the other hand, simultaneously recorded local field potentials exhibit repetition suppression. As local field potentials are believed to be better linked to the blood-oxygen-level-dependent (BOLD) signal exploited by functional magnetic resonance imaging, these findings suggest caution when trying to derive conclusions on the spiking activity of neurons in a given area based on the observation of BOLD repetition suppression
Integration of reverse engineering and ultrasonic non-contact testing procedures for quality assessment of CFRP aeronautical components
Abstract Nowadays, the quality assurance of aeronautical components is a very crucial issue. Diverse defects can be generated during composite material components manufacturing such as voids, delamination, cracks, etc. The identification of these defects requires the use of different types of inspection methods. In this paper, two diverse non-contact inspection techniques, i.e. a laser-based reverse engineering method and an ultrasonic testing procedure, are integrated to provide a complete quality assessment of carbon fibre reinforced polymer components for applications in the aeronautical field. A custom made software code was developed in order to create a user interface allowing for the visualization and analysis of the reverse engineering and ultrasonic information for the detection of geometrical and internal flaws of the component under inspection
Study on thrust force and torque sensor signals in drilling of Al/CFRP stacks for aeronautical applications
Abstract Multi-material stack products made of carbon fibre reinforce polymers (CFRP) and light-weight metal alloys, such as aluminium alloys, are becoming increasingly employed for aerospace applications. When composite laminates are stacked with metal alloy sheets, the drilling process becomes more complex due to the diverse properties of the stacked materials which involve different wear mechanisms and different drilling parameters. In this framework, the aim of this paper is to investigate the drilling process of Al/CFRP stacks for aeronautical applications through an experimental testing campaign under different drilling conditions. In order to study the thrust force and torque generated during the drilling process, a multiple sensor system is employed for data acquisition, and an advanced methodology for sensor signal processing in the time and frequency domain is developed
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