A Talk on the Wild Side: The Direct and Indirect Impact of Speech Recognition on Learning Gains

Research in the learning sciences and mathematics education has suggested that ‘thinking aloud’ (verbalization) can be important for learning. In a technology-mediated learning environment, speech might also help to promote learning by enabling the system to infer the students’ cognitive and affective state so that they can be provided a sequence of tasks and formative feedback, both of which are adapted to their needs. For these and associated reasons, we developed the iTalk2Learn platform that includes speech production and speech recognition for children learning about fractions. We investigated the impact of iTalk2Learn’s speech functionality in classrooms in the UK and Germany, with our results indicating that a speech-enabled learning environment has the potential to enhance student learning gains and engagement, both directly and indirectly

A 3.55 keV Photon Line and its Morphology from a 3.55 keV ALP Line

Galaxy clusters can efficiently convert axion-like particles (ALPs) to photons. We propose that the recently claimed detection of a 3.55--3.57 keV line in the stacked spectra of a large number of galaxy clusters and the Andromeda galaxy may originate from the decay of either a scalar or fermionic $7.1$ keV dark matter species into an axion-like particle (ALP) of mass $m_{a} \lesssim 6\cdot 10^{-11}~{\rm eV}$, which subsequently converts to a photon in the cluster magnetic field. In contrast to models in which the photon line arises directly from dark matter decay or annihilation, this can explain the anomalous line strength in the Perseus cluster. As axion-photon conversion scales as $B^2$ and cool core clusters have high central magnetic fields, this model can also explains the observed peaking of the line emission in the cool cores of the Perseus, Ophiuchus and Centaurus clusters, as opposed to the much larger dark matter halos. We describe distinctive predictions of this scenario for future observations.Comment: 6 page

Automated detection of time-dependent cross-correlation clusters in nonstationary time series

A novel method for the detection of cross-correlation clusters in multivariate time series is suggested. It is based on linear combinations of the eigenvectors corresponding to the largest eigenvalues of the equal-time cross-correlation matrix. The linear combinations are found in a systematic way by maximizing an appropriate distance measure. The performance of the algorithm is evaluated with a flexible time-series$\hbox{--}$based test framework for cluster algorithms. Attribution errors are investigated quantitatively in model data and a comparison with three alternative approaches is made. As the algorithm is suitable for unsupervised online application we demonstrate its time-resolved use in the example of cluster detection in time series from human electroencephalogram

Results of Skylab medical experiment M171: Metabolic activity

The experiment was conducted to establish whether man's ability to perform mechanical work would be progressively altered as a result of exposure to the weightless environment of space flight. The Skylab crewmen exercised on a bicycle ergometer at workloads approximating 25, 50, and 75 percent of their maximum aerobic capacity. The physiological parameters monitored were respiratory gas exchange, blood pressure, and vectorcardiogram/heart rate. The results of these tests indicate that the crewmen had no significant decrement in their responses to exercise during their exposure to zero gravity. The results of the third manned Skylab mission (Skylab 4) are presented and a comparison is made of the overall results obtained from the three successively longer Skylab manned missions. The Skylab 4 crewmembers' 84-day in-flight responses to exercise were no worse and were probably better than the responses of the crewmen on the first two Skylab missions. Indications that exercise was an important contributing factor in maintaining this response are discussed

Exploring the potential energy landscape over a large parameter-space

Solving large polynomial systems with coefficient parameters are ubiquitous and constitute an important class of problems. We demonstrate the computational power of two methods - a symbolic one called the Comprehensive Grobner basis and a numerical one called the cheater's homotopy - applied to studying both potential energy landscapes and a variety of questions arising from geometry and phenomenology. Particular attention is paid to an example in flux compactification where important physical quantities such as the gravitino and moduli masses and the string coupling can be efficiently extracted