Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWhat does the free energy principle tell us about the brain?
261
0
0.0
(
0
)
Authors
Samuel J. Gershman
Ask ChatGPT about the research
No Arabic abstract
The free energy principle has been proposed as a unifying account of brain function. It is closely related, and in some cases subsumes, earlier unifying ideas such as Bayesian inference, predictive coding, and active learning. This article clarifies these connections, teasing apart distinctive and shared predictions.
rate research
Read More
Planck data has not found the smoking gun of non-Gaussianity that would have necessitated consideration of inflationary models beyond the simplest canonical single field scenarios. This raises the important question of what these results do imply for more general models, and in particular, multi-field inflation. In this paper we revisit four ways in which two-field scenarios can behave differently from single field models; two-field slow-roll dynamics, curvaton-type behaviour, inflation ending on an inhomogeneous hypersurface and modulated reheating. We study the constraints that Planck data puts on these classes of behaviour, focusing on the latter two which have been least studied in the recent literature. We show that these latter classes are almost equivalent, and extend their previous analyses by accounting for arbitrary evolution of the isocurvature mode which, in particular, places important limits on the Gaussian curvature of the reheating hypersurface. In general, however, we find that Planck bispectrum results only constrain certain regions of parameter space, leading us to conclude that inflation sourced by more than one scalar field remains an important possibility.
Gaia will provide parallaxes and proper motions with accuracy ranging from 10 to 1000 microarcsecond on up to one billion stars. Most of these will be disk stars: for an unreddened K giant at 6 kpc, it will measure the distance accurate to 15% and the transverse velocity to an accuracy of about 1 km/s. Gaia will observe tracers of Galactic structure across the whole HR diagram, including Cepheids, RR Lyrae, white dwarfs, F dwarfs and HB stars. Onboard low resolution spectrophotometry will permit -- in addition to a Teff estimate -- dwarf/giant discrimination, metallicity measurement and extinction determination. For the first time, then, Gaia will provide us with a 3D spatial/properties map and at least a 2D velocity map of these tracers (RVs will be obtained too for brighter stars.) This will be a goldmine of information from which to learn about the origin and evolution of the Galactic disk. I briefly review the Gaia mission, and then show how the expected astrometric accuracies translate into distance and velocity accuracies and statistics. I examine the impact Gaia should have on a few scientific areas relevant to the Galactic disk. I discuss how a better determination of the spiral arm locations and pattern speed, plus a better reconstruction of the Suns orbit over the past billion years (from integration through the Gaia-measured gravitational potential) will allow us to assess the possible role of spiral arm crossings in ice ages and mass extinctions on the Earth.
Topical phenomena in high-energy physics related to collision experiments of heavy nuclei (Little Bang) and early universe cosmology (Big Bang) involve far-from-equilibrium dynamics described by quantum field theory. One example concerns the role of plasma instabilities for the process of thermalization in heavy-ion collisions. The reheating of the early universe after inflation may exhibit rather similar phenomena following a tachyonic or parametric resonance instability. Certain universal aspects associated to nonthermal fixed points even quantitatively agree, and considering these phenomena from a common perspective can be fruitful.
This review focuses on the current status of lattice calculations of three observables which are both phenomenologically and experimentally relevant and have been scrutinized recently. These three observables are the nucleon electromagnetic form factors, the momentum fraction, <x>, and the nucleon axial coupling, gA.
Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing, image annotations are difficult and expensive to obtain, especially when the test environment is changing. A natural question then arises: given a trained classifier, can we evaluate its accuracy on varying unlabeled test sets? In this work, we train semantic classification and rotation prediction in a multi-task way. On a series of datasets, we report an interesting finding, i.e., the semantic classification accuracy exhibits a strong linear relationship with the accuracy of the rotation prediction task (Pearsons Correlation r > 0.88). This finding allows us to utilize linear regression to estimate classifier performance from the accuracy of rotation prediction which can be obtained on the test set through the freely generated rotation labels.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
