Multi-wavelength variability properties of Fermi blazar S5 0716+714

S5 0716+714 is a typical BL Lacertae object. In this paper we present the analysis and results of long term simultaneous observations in the radio, near-infrared, optical, X-ray and $\gamma$-ray bands, together with our own photometric observations for this source. The light curves show that the variability amplitudes in $\gamma$-ray and optical bands are larger than those in the hard X-ray and radio bands and that the spectral energy distribution (SED) peaks move to shorter wavelengths when the source becomes brighter, which are similar to other blazars, i.e., more variable at wavelengths shorter than the SED peak frequencies. Analysis shows that the characteristic variability timescales in the 14.5 GHz, the optical, the X-ray, and the $\gamma$-ray bands are comparable to each other. The variations of the hard X-ray and 14.5 GHz emissions are correlated with zero-lag, so are the V band and $\gamma$-ray variations, which are consistent with the leptonic models. Coincidences of $\gamma$-ray and optical flares with a dramatic change of the optical polarization are detected. Hadronic models do not have the same nature explanation for these observations as the leptonic models. A strong optical flare correlating a $\gamma$-ray flare whose peak flux is lower than the average flux is detected. Leptonic model can explain this variability phenomenon through simultaneous SED modeling. Different leptonic models are distinguished by average SED modeling. The synchrotron plus synchrotron self-Compton (SSC) model is ruled out due to the extreme input parameters. Scattering of external seed photons, such as the hot dust or broad line region emission, and the SSC process are probably both needed to explain the $\gamma$-ray emission of S5 0716+714.Comment: 43 pages, 13 figures, 3 tables, to be appeared in Ap

Learn to Interpret Atari Agents

Deep Reinforcement Learning (DeepRL) agents surpass human-level performances in a multitude of tasks. However, the direct mapping from states to actions makes it hard to interpret the rationale behind the decision making of agents. In contrast to previous a-posteriori methods of visualizing DeepRL policies, we propose an end-to-end trainable framework based on Rainbow, a representative Deep Q-Network (DQN) agent. Our method automatically learns important regions in the input domain, which enables characterizations of the decision making and interpretations for non-intuitive behaviors. Hence we name it Region Sensitive Rainbow (RS-Rainbow). RS-Rainbow utilizes a simple yet effective mechanism to incorporate visualization ability into the learning model, not only improving model interpretability, but leading to improved performance. Extensive experiments on the challenging platform of Atari 2600 demonstrate the superiority of RS-Rainbow. In particular, our agent achieves state of the art at just 25% of the training frames. Demonstrations and code are available at https://github.com/yz93/Learn-to-Interpret-Atari-Agents

Chemoviscosity modeling for thermosetting resins

A chemoviscosity model, which describes viscosity rise profiles accurately under various cure cycles, and correlates viscosity data to the changes of physical properties associated with structural transformations of the thermosetting resin system during cure, was established. Work completed on chemoviscosity modeling for thermosetting resins is reported

Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.Comment: 2018 International Workshop on Statistical Atlases and Computational Modeling of the Hear

Studies on chemoviscosity modeling for thermosetting resins

A new analytical model for simulating chemoviscosity of thermosetting resins has been formulated. The model is developed by modifying the well-established Williams-Landel-Ferry (WLF) theory in polymer rheology for thermoplastic materials. By introducing a relationship between the glass transition temperature Tg(t) and the degree of cure alpha(t) of the resin system under cure, the WLF theory can be modified to account for the factor of reaction time. Temperature dependent functions of the modified WLF theory constants C sub 1 (t) and C sub 2 (t) were determined from the isothermal cure data. Theoretical predictions of the model for the resin under dynamic heating cure cycles were shown to compare favorably with the experimental data. This work represents progress toward establishing a chemoviscosity model which is capable of not only describing viscosity profiles accurately under various cure cycles, but also correlating viscosity data to the changes of physical properties associated with the structural transformation of the thermosetting resin systems during cure

Magnetic spin moment reduction in photoexcited ferromagnets through exchange interaction quenching: Beyond the rigid band approximation

The exchange interaction among electrons is one of the most fundamental quantum mechanical interactions in nature and underlies any magnetic phenomena from ferromagnetic ordering to magnetic storage. The current technology is built upon a thermal or magnetic field, but a frontier is emerging to directly control magnetism using ultrashort laser pulses. However, little is known about the fate of the exchange interaction. Here we report unambiguously that photoexcitation is capable of quenching the exchange interaction in all three $3d$ ferromagnetic metals. The entire process starts with a small number of photoexcited electrons which build up a new and self-destructive potential that collapses the system into a new state with a reduced exchange splitting. The spin moment reduction follows a Bloch-like law as $M_z(\Delta E)=M_z(0)(1-{\Delta E}/{\Delta E_0})^{\frac{1}{\beta}}$, where $\Delta E$ is the absorbed photon energy and $\beta$ is a scaling exponent. A good agreement is found between the experimental and our theoretical results. Our findings may have a broader implication for dynamic electron correlation effects in laser-excited iron-based superconductors, iron borate, rare-earth orthoferrites, hematites and rare-earth transition metal alloys.Comment: 16 pages, 3 figures, one supplementary material fil

Sequential nature of damage annealing and activation in implanted GaAs

Rapid thermal processing of implanted GaAs reveals a definitive sequence in the damage annealing and the electrical activation of ions. Removal of implantation-induced damage and restoration of GaAs crystallinity occurs first. Irrespective of implanted species, at this stage the GaAs is n-type and highly resistive with almost ideal values of electron mobility. Electrical activation is achieved next when, in a narrow anneal temperature window, the material becomes n- or p-type, or remains semi-insulating, commensurate to the chemical nature of the implanted ion. Such a two-step sequence in the electrical doping of GaAs by ion implantation may be unique of GaAs and other compound semiconductors