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Register a new userHow We Learn About our Networked World
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Sophia U. David
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When presented with information of any type, from music to language to mathematics, the human mind subconsciously arranges it into a network. A network puts pieces of information like musical notes, syllables or mathematical concepts into context by linking them together. These networks help our minds organize information and anticipate what is coming. Here we present two questions about network building. 1) Can humans more easily learn some types of networks than others? 2) Do humans find some links between ideas more surprising than others? The answer to both questions is Yes, and we explain why. The findings provide much-needed insight into the ways that humans learn about the networked world around them. Moreover, the study paves the way for future efforts seeking to optimize how information is presented to accelerate human learning.
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Self-localization during navigation with noisy sensors in an ambiguous world is computationally challenging, yet animals and humans excel at it. In robotics, Simultaneous Location and Mapping (SLAM) algorithms solve this problem though joint sequential probabilistic inference of their own coordinates and those of external spatial landmarks. We generate the first neural solution to the SLAM problem by training recurrent LSTM networks to perform a set of hard 2D navigation tasks that include generalization to completely novel trajectories and environments. The hidden unit representations exhibit several key properties of hippocampal place cells, including stable tuning curves that remap between environments. Our result is also a proof of concept for end-to-end-learning of a SLAM algorithm using recurrent networks, and a demonstration of why this approach may have some advantages for robotic SLAM.
Magnetic fields and stellar spots can alter the equivalent widths of absorption lines in stellar spectra, varying during the activity cycle. This also influences the information that we derive through spectroscopic analysis. In this study we analyse high-resolution spectra of 211 Sun-like stars observed at different phases of their activity cycles, in order to investigate how stellar activity affects the spectroscopic determination of stellar parameters and chemical abundances. We observe that equivalent widths of lines can increase as a function of the activity index log R$^prime_{rm HK}$ during the stellar cycle, which also produces an artificial growth of the stellar microturbulence and a decrease in effective temperature and metallicity. This effect is visible for stars with activity indexes log R$^prime_{rm HK}$$geq$$-$5.0 (i.e., younger than 4-5 Gyr) and it is more significant at higher activity levels. These results have fundamental implications on several topics in astrophysics that are discussed in the paper, including stellar nucleosynthesis, chemical tagging, the study of Galactic chemical evolution, chemically anomalous stars, the structure of the Milky Way disk, stellar formation rates, photoevaporation of circumstellar disks, and planet hunting.
A key step towards understanding human behavior is the prediction of 3D human motion. Successful solutions have many applications in human tracking, HCI, and graphics. Most previous work focuses on predicting a time series of future 3D joint locations given a sequence 3D joints from the past. This Euclidean formulation generally works better than predicting pose in terms of joint rotations. Body joint locations, however, do not fully constrain 3D human pose, leaving degrees of freedom undefined, making it hard to animate a realistic human from only the joints. Note that the 3D joints can be viewed as a sparse point cloud. Thus the problem of human motion prediction can be seen as point cloud prediction. With this observation, we instead predict a sparse set of locations on the body surface that correspond to motion capture markers. Given such markers, we fit a parametric body model to recover the 3D shape and pose of the person. These sparse surface markers also carry detailed information about human movement that is not present in the joints, increasing the naturalness of the predicted motions. Using the AMASS dataset, we train MOJO, which is a novel variational autoencoder that generates motions from latent frequencies. MOJO preserves the full temporal resolution of the input motion, and sampling from the latent frequencies explicitly introduces high-frequency components into the generated motion. We note that motion prediction methods accumulate errors over time, resulting in joints or markers that diverge from true human bodies. To address this, we fit SMPL-X to the predictions at each time step, projecting the solution back onto the space of valid bodies. These valid markers are then propagated in time. Experiments show that our method produces state-of-the-art results and realistic 3D body animations. The code for research purposes is at https://yz-cnsdqz.github.io/MOJO/MOJO.html
It is reported on an analysis of electroproduction of light mesons at small Bjorken-x within the handbag approach. The partonic subprocesses, meson electroproduction off quarks or gluons, are calculated within the modified perturbative approach in which quark transverse momenta are retained. The soft hadronic matrix elements, generalized parton distributions, are constructed by means of double distributions. The constraints from parton distributions and sum rules are taken into account. Various moments of these generalized parton distributions are compared to recent results from lattice gauge theories.
Ultra-relativistic electromagnetic plasmas can be used for improving our understanding of the quark-gluon plasma. In the weakly coupled regime both plasmas can be described by transport theoretical and quantum field theoretical methods leading to similar results for the plasma properties (dielectric tensor, dispersion relations, plasma frequency, Debye screening, transport coefficients, damping and particle production rates). In particular, future experiments with ultra-relativistic electron-positron plasmas in ultra-strong laser fields might open the possibility to test these predictions, e.g. the existence of a new fermionic plasma wave (plasmino). In the strongly coupled regime electromagnetic plasmas such as complex plasmas can be used as models or at least analogies for the quark-gluon plasma possibly produced in relativistic heavy-ion experiments. For example, pair correlation functions can be used to investigate the equation of state and cross section enhancement for parton scattering can be explained.
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