7 research outputs found
Patch-Based Holographic Image Sensing
Holographic representations of data enable distributed storage with
progressive refinement when the stored packets of data are made available in
any arbitrary order. In this paper, we propose and test patch-based transform
coding holographic sensing of image data. Our proposal is optimized for
progressive recovery under random order of retrieval of the stored data. The
coding of the image patches relies on the design of distributed projections
ensuring best image recovery, in terms of the norm, at each retrieval
stage. The performance depends only on the number of data packets that has been
retrieved thus far. Several possible options to enhance the quality of the
recovery while changing the size and number of data packets are discussed and
tested. This leads us to examine several interesting bit-allocation and
rate-distortion trade offs, highlighted for a set of natural images with
ensemble estimated statistical properties
Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
Effortful control (EC) is a dimension of temperament that encompass individual differences
in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation
on the development of executive attention, but increasing evidence also shows a significant
contribution of the rearing environment to individual differences in EC. The aim of the current study
was to predict the development of EC at 36 months of age from early attentional and environmental
measures taken in infancy using a machine learning approach. A sample of 78 infants participated in
a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional
tasks were administered at 6 months of age, with two additional measures (i.e., one attentional
measure and another self-restraint measure) being collected at 9 months of age. Parents reported
household environment variables during wave 1, and their childâs EC at 36 months. A machinelearning
algorithm was implemented to identify children with low EC scores at 36 months of age. An
âattention onlyâ model showed greater predictive sensitivity than the âenvironmental onlyâ model.
However, a model including both attentional and environmental variables was able to classify the
groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socioeconomic
variables together with attention control processes at 6 months, and self-restraint capacity
at 9 months, are the most important predictors of EC. Results suggest a foundational role of executive
attention processes in the development of EC in complex interactions with household environments
and provide a new tool to identify early markers of socio-emotional regulation development.Spanish State Research Agency (Ref: PSI2017-82670-PPID2020-113996GB-I00)PRE2018-083592Maria ZambranoThe Spanish Government through the European Union NextGeneration EU fund