High-capacity preconscious processing in concurrent groupings of colored dots.

Grouping is a perceptual process in which a subset of stimulus components (a group) is selected for a subsequent-typically implicit-perceptual computation. Grouping is a critical precursor to segmenting objects from the background and ultimately to object recognition. Here, we study grouping by color. We present subjects with 300-ms exposures of 12 dots colored with the same but unknown identical color interspersed among 14 dots of seven different colors. To indicate grouping, subjects point-click the remembered centroid ("center of gravity") of the set of homogeneous dots, of heterogeneous dots, or of all dots. Subjects accurately judge all of these centroids. Furthermore, after a single stimulus exposure, subjects can judge both the heterogeneous and homogeneous centroids, that is, subjects simultaneously group by similarity and by dissimilarity. The centroid paradigm reveals the relative weight of each dot among targets and distractors to the underlying grouping process, offering a more detailed, quantitative description of grouping than was previously possible. A change detection experiment reveals that conscious memory contains less than two dots and their locations, whereas an ideal detector would have to perfectly process at least 15 of 26 dots to match the subjects' centroid judgments-indicating an extraordinary capacity for preconscious grouping. A different color set yielded identical results. Grouping theories that rely on predefined feature maps would fail to explain these results. Rather, the results indicate that preconscious grouping is automatic, flexible, and rapid, and a far more complex process than previously believed

New measurement paradigms

This collection of New Measurement Paradigms papers represents a snapshot of the variety of measurement methods in use at the time of writing across several projects funded by the National Science Foundation (US) through its REESE and DR K–12 programs. All of the projects are developing and testing intelligent learning environments that seek to carefully measure and promote student learning, and the purpose of this collection of papers is to describe and illustrate the use of several measurement methods employed to achieve this. The papers are deliberately short because they are designed to introduce the methods in use and not to be a textbook chapter on each method. The New Measurement Paradigms collection is designed to serve as a reference point for researchers who are working in projects that are creating e-learning environments in which there is a need to make judgments about students’ levels of knowledge and skills, or for those interested in this but who have not yet delved into these methods

COrE (Cosmic Origins Explorer) A White Paper

COrE (Cosmic Origins Explorer) is a fourth-generation full-sky, microwave-band satellite recently proposed to ESA within Cosmic Vision 2015-2025. COrE will provide maps of the microwave sky in polarization and temperature in 15 frequency bands, ranging from 45 GHz to 795 GHz, with an angular resolution ranging from 23 arcmin (45 GHz) and 1.3 arcmin (795 GHz) and sensitivities roughly 10 to 30 times better than PLANCK (depending on the frequency channel). The COrE mission will lead to breakthrough science in a wide range of areas, ranging from primordial cosmology to galactic and extragalactic science. COrE is designed to detect the primordial gravitational waves generated during the epoch of cosmic inflation at more than $3\sigma$ for $r=(T/S)>=10^{-3}$. It will also measure the CMB gravitational lensing deflection power spectrum to the cosmic variance limit on all linear scales, allowing us to probe absolute neutrino masses better than laboratory experiments and down to plausible values suggested by the neutrino oscillation data. COrE will also search for primordial non-Gaussianity with significant improvements over Planck in its ability to constrain the shape (and amplitude) of non-Gaussianity. In the areas of galactic and extragalactic science, in its highest frequency channels COrE will provide maps of the galactic polarized dust emission allowing us to map the galactic magnetic field in areas of diffuse emission not otherwise accessible to probe the initial conditions for star formation. COrE will also map the galactic synchrotron emission thirty times better than PLANCK. This White Paper reviews the COrE science program, our simulations on foreground subtraction, and the proposed instrumental configuration.Comment: 90 pages Latex 15 figures (revised 28 April 2011, references added, minor errors corrected

Gait performance and prefrontal cortex activation during single and dual task walking in older adults with different cognitive levels

BackgroundGrowing evidence shows the cognitive function influences the motor performance. The prefrontal cortex (PFC) as a part of the executive locomotor pathway is also important for cognitive function. This study investigated the differences in motor function and brain activity among older adults with different cognitive levels, and examined the significance of cognition on motor functions.MethodsNormal control (NC), individuals with mild cognitive impairment (MCI) or mild dementia (MD) were enrolled in this study. All participants received a comprehensive assessment including cognitive function, motor function, PFC activity during walking, and fear of fall. The assessment of cognitive function included general cognition, attention, executive function, memory, and visuo-spatial. The assessment of motor function included timed up and go (TUG) test, single walking (SW), and cognitive dual task walking (CDW).ResultsIndividuals with MD had worse SW, CDW and TUG performance as compared to individuals with MCI and NC. These gait and balance performance did not differ significantly between MCI and NC. Motor functions all correlated with general cognition, attention, executive function, memory, and visuo-spatial ability. Attention ability measured by trail making test A (TMT-A) was the best predictor for TUG and gait velocity. There were no significant differences in PFC activity among three groups. Nevertheless, the PFC activated more during CDW as compared with SW in individuals with MCI (p = 0.000), which was not demonstrated in the other two groups.ConclusionMD demonstrated worse motor function as compared to NC and MCI. The greater PFC activity during CDW in MCI may be considered as a compensatory strategy for maintaining the gait performance. Motor function was related to the cognitive function, and the TMT A was the best predictor for the gait related performance in present study among older adults

Real-time detection of auditory : steady-state brainstem potentials evoked by auditory stimuli

The auditory steady-state response (ASSR) is advantageous against other hearing techniques because of its capability in providing objective and frequency specific information. The objectives are to reduce the lengthy test duration, and improve the signal detection rate and the robustness of the detection against the background noise and unwanted artefacts.Two prominent state estimation techniques of Luenberger observer and Kalman filter have been used in the development of the autonomous ASSR detection scheme. Both techniques are real-time implementable, while the challenges faced in the application of the observer and Kalman filter techniques are the very poor SNR (could be as low as −30dB) of ASSRs and unknown statistics of the noise. Dual-channel architecture is proposed, one is for the estimate of sinusoid and the other for the estimate of the background noise. Simulation and experimental studies were also conducted to evaluate the performances of the developed ASSR detection scheme, and to compare the new method with other conventional techniques. In general, both the state estimation techniques within the detection scheme produced comparable results as compared to the conventional techniques, but achieved significant measurement time reduction in some cases. A guide is given for the determination of the observer gains, while an adaptive algorithm has been used for adjustment of the gains in the Kalman filters.In order to enhance the robustness of the ASSR detection scheme with adaptive Kalman filters against possible artefacts (outliers), a multisensory data fusion approach is used to combine both standard mean operation and median operation in the ASSR detection algorithm. In addition, a self-tuned statistical-based thresholding using the regression technique is applied in the autonomous ASSR detection scheme. The scheme with adaptive Kalman filters is capable of estimating the variances of system and background noise to improve the ASSR detection rate

High-precision measurement of the hypertriton lifetime and Λ-separation energy exploiting ML algorithms with ALICE at the LHC.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Diffuse Optical Imaging with Ultrasound Priors and Deep Learning

Diffuse Optical Imaging (DOI) techniques are an ever growing field of research as they are noninvasive, compact, cost-effective and can furnish functional information about human tissues. Among others, they include techniques such as Tomography, which solves an inverse reconstruction problem in a tissue volume, and Mapping which only seeks to find values on a tissue surface. Limitations in reliability and resolution, due to the ill-posedness of the underlying inverse problems, have hindered the clinical uptake of this medical imaging modality. Multimodal imaging and Deep Learning present themselves as two promising solutions to further research in DOI. In relation to the first idea, we implement and assess here a set of methods for SOLUS, a combined Ultrasound (US) and Diffuse Optical Tomography (DOT) probe for breast cancer diagnosis. An ad hoc morphological prior is extracted from US B-mode images and utilised for the regularisation of the inverse problem in DOT. Combination of the latter in reconstruction with a linearised forward model for DOT is assessed on specifically designed dual phantoms. The same reconstruction approach with the incorporation of a spectral model has been assessed on meat phantoms for reconstruction of functional properties. A simulation study with realistic digital phantoms is presented for an assessment of a non-linear model in reconstruction for the quantification of optical properties of breast lesions. A set of machine learning tools is presented for diagnosis breast lesions based on the reconstructed optical properties. A preliminary clinical study with the SOLUS probe is presented. Finally, a specifically designed deep learning architecture for diffusion is applied to mapping on the brain cortex or Diffuse Optical Cortical Mapping (DOCM). An assessment of its performances is presented on simulated and experimental data

Real-time Assessment, Prediction, and Scaffolding of Middle School Students’ Data Collection Skills within Physical Science Simulations

Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students\u27 skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students\u27 developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models\u27 predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students\u27 inquiry skills. The ability to evaluate students\u27 inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students\u27 inquiry