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Register a new user$Lambda$CDM: Much more than we expected, but now less than what we want
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Michael S. Turner
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The $rmLambda$CDM cosmological model is remarkable: with just 6 parameters it describes the evolution of the Universe from a very early time when all structures were quantum fluctuations on subatomic scales to the present, and it is consistent with a wealth of high-precision data, both laboratory measurements and astronomical observations. However, the foundation of $rmLambda$CDM involves physics beyond the standard model of particle physics: particle dark matter, dark energy and cosmic inflation. Until this `new physics is clarified, $rmLambda$CDM is at best incomplete and at worst a phenomenological construct that accommodates the data. I discuss the path forward, which involves both discovery and disruption, some grand challenges and finally the limits of scientific cosmology.
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We analyze the additional effect on planetary atmospheres of recently detected gamma-ray burst afterglow photons in the range up to 1 TeV. For an Earth-like atmosphere we find that there is a small additional depletion in ozone versus that modeled for only prompt emission. We also find a small enhancement of muon flux at the planet surface. Overall, we conclude that the additional afterglow emission, even with TeV photons, does not result in a significantly larger impact over that found in past studies.
We report the discovery of two new giant radio galaxies (GRGs) using the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. Both GRGs were found within a 1 deg^2 region inside the COSMOS field. They have redshifts of z=0.1656 and z=0.3363 and physical sizes of 2.4Mpc and 2.0Mpc, respectively. Only the cores of these GRGs were clearly visible in previous high resolution VLA observations, since the diffuse emission of the lobes was resolved out. However, the excellent sensitivity and uv coverage of the new MeerKAT telescope allowed this diffuse emission to be detected. The GRGs occupy a unpopulated region of radio power - size parameter space. Based on a recent estimate of the GRG number density, the probability of finding two or more GRGs with such large sizes at z<0.4 in a ~1deg^2 field is only 2.7x10^-6, assuming Poisson statistics. This supports the hypothesis that the prevalence of GRGs has been significantly underestimated in the past due to limited sensitivity to low surface brightness emission. The two GRGs presented here may be the first of a new population to be revealed through surveys like MIGHTEE which provide exquisite sensitivity to diffuse, extended emission.
What we expect from radiology AI algorithms will shape the selection and implementation of AI in the radiologic practice. In this paper I consider prevailing expectations of AI and compare them to expectations that we have of human readers. I observe that the expectations from AI and radiologists are fundamentally different. The expectations of AI are based on a strong and justified mistrust about the way that AI makes decisions. Because AI decisions are not well understood, it is difficult to know how the algorithms will behave in new, unexpected situations. However, this mistrust is not mirrored in our expectations of human readers. Despite well-proven idiosyncrasies and biases in human decision making, we take comfort from the assumption that others make decisions in a way as we do, and we trust our own decision making. Despite poor ability to explain decision making processes in humans, we accept explanations of decisions given by other humans. Because the goal of radiology is the most accurate radiologic interpretation, our expectations of radiologists and AI should be similar, and both should reflect a healthy mistrust of complicated and partially opaque decision processes undergoing in computer algorithms and human brains. This is generally not the case now.
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
Supergranules are believed to be an evidence for large-scale subsurface convection. The vertical component of the supergranular flow field is very hard to measure, but it is considered only a few m/s in and below the photosphere. Here I present the results of the analysis using three-dimensional inversion for time-distance helioseismology that indicate existence of the large-magnitude vertical upflow in the near sub-surface layers. Possible issues and consequences of this inference are also discussed.
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