Neutrino-driven Explosions

The question why and how core-collapse supernovae (SNe) explode is one of the central and most long-standing riddles of stellar astrophysics. A solution is crucial for deciphering the SN phenomenon, for predicting observable signals such as light curves and spectra, nucleosynthesis, neutrinos, and gravitational waves, for defining the role of SNe in the evolution of galaxies, and for explaining the birth conditions and properties of neutron stars (NSs) and stellar-mass black holes. Since the formation of such compact remnants releases over hundred times more energy in neutrinos than the SN in the explosion, neutrinos can be the decisive agents for powering the SN outburst. According to the standard paradigm of the neutrino-driven mechanism, the energy transfer by the intense neutrino flux to the medium behind the stagnating core-bounce shock, assisted by violent hydrodynamic mass motions (sometimes subsumed by the term "turbulence"), revives the outward shock motion and thus initiates the SN blast. Because of the weak coupling of neutrinos in the region of this energy deposition, detailed, multidimensional hydrodynamic models including neutrino transport and a wide variety of physics are needed to assess the viability of the mechanism. Owing to advanced numerical codes and increasing supercomputer power, considerable progress has been achieved in our understanding of the physical processes that have to act in concert for the success of neutrino-driven explosions. First studies begin to reveal observational implications and avenues to test the theoretical picture by data from individual SNe and SN remnants but also from population-integrated observables. While models will be further refined, a real breakthrough is expected through the next Galactic core-collapse SN, when neutrinos and gravitational waves can be used to probe the conditions deep inside the dying star. (abridged)Comment: Author version of chapter for 'Handbook of Supernovae,' edited by A. Alsabti and P. Murdin, Springer. 54 pages, 13 figure

The Chern-Gauss-Bonnet formula for singular non-compact four-dimensional manifolds

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Real-time WebRTC-based design for a telepresence wheelchair

© 2017 IEEE. This paper presents a novel approach to the telepresence wheelchair system which is capable of real-time video communication and remote interaction. The investigation of this emerging technology aims at providing a low-cost and efficient way for assisted-living of people with disabilities. The proposed system has been designed and developed by deploying the JavaScript with Hyper Text Markup Language 5 (HTML5) and Web Real-time Communication (WebRTC) in which the adaptive rate control algorithm for video transmission is invoked. We conducted experiments in real-world environments, and the wheelchair was controlled from a distance using the Internet browser to compare with existing methods. The results show that the adaptively encoded video streaming rate matches the available bandwidth. The video streaming is high-quality with approximately 30 frames per second (fps) and round trip time less than 20 milliseconds (ms). These performance results confirm that the WebRTC approach is a potential method for developing a telepresence wheelchair system

Local Kesten–McKay Law for Random Regular Graphs

We study the adjacency matrices of random $d$-regular graphs with large but fixed degree $d$. In the bulk of the spectrum $[-2\sqrt{d-1}+\varepsilon, 2\sqrt{d-1}-\varepsilon]$ down to the optimal spectral scale, we prove that the Green's functions can be approximated by those of certain infinite tree-like (few cycles) graphs that depend only on the local structure of the original graphs. This result implies that the Kesten--McKay law holds for the spectral density down to the smallest scale and the complete delocalization of bulk eigenvectors. Our method is based on estimating the Green's function of the adjacency matrices and a resampling of the boundary edges of large balls in the graphs

Explosion Mechanisms of Core-Collapse Supernovae

Supernova theory, numerical and analytic, has made remarkable progress in the past decade. This progress was made possible by more sophisticated simulation tools, especially for neutrino transport, improved microphysics, and deeper insights into the role of hydrodynamic instabilities. Violent, large-scale nonradial mass motions are generic in supernova cores. The neutrino-heating mechanism, aided by nonradial flows, drives explosions, albeit low-energy ones, of ONeMg-core and some Fe-core progenitors. The characteristics of the neutrino emission from new-born neutron stars were revised, new features of the gravitational-wave signals were discovered, our notion of supernova nucleosynthesis was shattered, and our understanding of pulsar kicks and explosion asymmetries was significantly improved. But simulations also suggest that neutrino-powered explosions might not explain the most energetic supernovae and hypernovae, which seem to demand magnetorotational driving. Now that modeling is being advanced from two to three dimensions, more realism, new perspectives, and hopefully answers to long-standing questions are coming into reach.Comment: 35 pages, 11 figures (29 eps files; high-quality versions can be obtained upon request); accepted by Annual Review of Nuclear and Particle Scienc

In‐plane photocurrent spectroscopy in GaAs-AlAs superlattices

The in‐plane photoconductivity of GaAs‐AlAs superlattices on GaAs substrates is experimentally studied as a function of the incident photon energy at different temperatures and light intensities. Superlattice and substrate are electrically isolated by a thick  Al0.3Ga0.7As barrier but connected through penetrating contacts. Depending on the transport properties of the two subsystems pseudo‐negative photoconductivity can be observed, i.e., at the absorption maximum of the superlattice the photocurrent exhibits a minimum

Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm