Local Relaxation and Collective Stochastic Dynamics

Damping and thermal fluctuations have been introduced to collective normal modes of a magnetic system in recent modeling of dynamic thermal magnetization processes. The connection between this collective stochastic dynamics and physical local relaxation processes is investigated here. A system of two coupled magnetic grains embedded in two separate oscillating thermal baths is analyzed with no \QTR{it}{a priori} assumptions except that of a Markovian process. It is shown explicitly that by eliminating the oscillating thermal bath variables, collective stochastic dynamics occurs in the normal modes of the magnetic system. The grain interactions cause local relaxation to be felt by the collective system and the dynamic damping to reflect the system symmetry. This form of stochastic dynamics is in contrast to a common phenomenological approach where a thermal field is added independently to the dynamic equations of each discretized cell or interacting grain. The dependence of this collective stochastic dynamics on the coupling strength of the magnetic grains and the relative local damping is discussed

Plasma Generation and Application in a Laser Ablation Pulsed Plasma Thruster

The laser ablation plasma thruster is a novel electric propulsion thruster, which combined the laser ablation and electromagnetic acceleration. In order to investigate the plasma expansion and ionization in the laser ablation plasma thruster, which was difficult to obtain from experiments, the heat conduction model and fluid dynamics model were established. The heat conduction model was established to calculate the target ablation, taking into account temperature-dependent material properties, phase transition, dielectric transition and phase explosion. The fluid dynamics model was used to calculate the plasma properties, taking into account ionization, plasma absorption and shielding. The ablation plasma velocity, temperature and electron number density were predicted by using the numerical method. The calculated results showed that the peak values of ablation plasma velocity, temperature and electron number density fraction were distributed at the front of the plasma plume. Moreover, the discharge characteristics and thrust performance were tested with different charged energy, structural parameters and propellants. The thrust performance was proven to be improved by electromagnetic acceleration

Computational evaluation of TIS annotation for prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.</p> <p>Results</p> <p>Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, <it>i.e</it>. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.</p> <p>Conclusion</p> <p>Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.</p

SD-GAN: Semantic Decomposition for Face Image Synthesis with Discrete Attribute

Manipulating latent code in generative adversarial networks (GANs) for facial image synthesis mainly focuses on continuous attribute synthesis (e.g., age, pose and emotion), while discrete attribute synthesis (like face mask and eyeglasses) receives less attention. Directly applying existing works to facial discrete attributes may cause inaccurate results. In this work, we propose an innovative framework to tackle challenging facial discrete attribute synthesis via semantic decomposing, dubbed SD-GAN. To be concrete, we explicitly decompose the discrete attribute representation into two components, i.e. the semantic prior basis and offset latent representation. The semantic prior basis shows an initializing direction for manipulating face representation in the latent space. The offset latent presentation obtained by 3D-aware semantic fusion network is proposed to adjust prior basis. In addition, the fusion network integrates 3D embedding for better identity preservation and discrete attribute synthesis. The combination of prior basis and offset latent representation enable our method to synthesize photo-realistic face images with discrete attributes. Notably, we construct a large and valuable dataset MEGN (Face Mask and Eyeglasses images crawled from Google and Naver) for completing the lack of discrete attributes in the existing dataset. Extensive qualitative and quantitative experiments demonstrate the state-of-the-art performance of our method. Our code is available at: https://github.com/MontaEllis/SD-GAN.Comment: 16 pages, 12 figures, Accepted by ACM MM202