Metal vapour vacuum arcs are capable to generate multiply charged metallic ions, which are widely used in fields such as ion deposition, ion thrusters, and ion sources, etc. According to the stationary model of cathode spot, those ions are generated
by electron-impact single ionization in a step-wise manner, which is M -> M+ -> M2+ -> ... mainly. This paper is designed to study quantitatively the role of double ionization M -> M2+ in the breakdown initiation of copper vacuum arcs. A direct simulation Monte Carlo (DSMC) scheme of double ionization is proposed and incorporated into a 2D particle-in-cell (PIC) method. The super-particles of Cu2+ ions generated from different channels are labelled independently in the PIC-DSMC modelling of vacuum arc breakdown. The cathode erosion rate based on PIC modelling is about 40{mu}g/C in arc burning regime, which agrees well with previous experiments. The temporal discharge behaviours such as arc current, arc voltage, and ionization degree of arc plasma, are influenced with or without double ionization negligibly. However, additional Cu2+ ions are generated near the cathode in breakdown initiation from the double ionization channel, with a lower kinetic energy on average. Therefore, the results on spatial distribution and energy spectra of Cu2+ ions are different with or without double ionization. This paper provides a quantitative research method to evaluate the role of multiply ionization in vacuum arcs.
We translate a closed text that is known in advance and available in many languages into a new and severely low resource language. Most human translation efforts adopt a portion-based approach to translate consecutive pages/chapters in order, which m
ay not suit machine translation. We compare the portion-based approach that optimizes coherence of the text locally with the random sampling approach that increases coverage of the text globally. Our results show that the random sampling approach performs better. When training on a seed corpus of ~1,000 lines from the Bible and testing on the rest of the Bible (~30,000 lines), random sampling gives a performance gain of +11.0 BLEU using English as a simulated low resource language, and +4.9 BLEU using Eastern Pokomchi, a Mayan language. Furthermore, we compare three ways of updating machine translation models with increasing amount of human post-edited data through iterations. We find that adding newly post-edited data to training after vocabulary update without self-supervision performs the best. We propose an algorithm for human and machine to work together seamlessly to translate a closed text into a severely low resource language.
We analyze the quasi-two-body charmed $B$ decays $B^{+,0}_{(s)} to D_{(s)}^* P_2 to D_{(s)} P_1 P_2$ with $P_{1,2}$ as a pion or kaon. The intermediate processes $B_{(s)} to D_{(s)}^* P_2 $ are calculated with the factorization-assisted topological-a
mplitude approach and the resonant effects are calculated with the Breit-Wigner formalism. Taking all p-wave resonance states $ bar D_{(s)}^*$ into consideration, we present the related branching fractions, calculate the Breit-Wigner-Tail effects, and investigate the flavor $SU(3)$ breaking effects. Most of our branching fractions are consistent with the perturbative QCD approachs predictions as well as the current experimental data. With more precision calculation of the intermediate two body charmed B meson decays, our quasi-two-body B decays calculation has significantly less theoretical uncertainty than the perturbative QCD approach. Many of those channels without any experimental data will be confronted with the future more accurate experiment measurements. Our results of the Breit-Wigner-tail effects also agree with the experimental very well. In $B^0$ decays this effect can reach approximately to $5%$. It is also found that the Breit-Wigner-tail effects are not sensitive to the widths of their corresponding resonances. The flavor $SU(3)$ symmetry breaking effect is also investigated.
A uniform hypergraph $mathcal{H}$ is corresponding to an adjacency tensor $mathcal{A}_mathcal{H}$. We define an Estrada index of $mathcal{H}$ by using all the eigenvalues $lambda_1,dots,lambda_k$ of $mathcal{A}_mathcal{H}$ as $sum_{i=1}^k e^{lambda_i
}$. The bounds for the Estrada indices of uniform hypergraphs are given. And we characterize the Estrada indices of $m$-uniform hypergraphs whose spectra of the adjacency tensors are $m$-symmetric. Specially, we characterize the Estrada indices of uniform hyperstars.
In semantic segmentation, we aim to train a pixel-level classifier to assign category labels to all pixels in an image, where labeled training images and unlabeled test images are from the same distribution and share the same label set. However, in a
n open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images. Hence, in this paper, we consider a new, more realistic, and more challenging problem setting where the pixel-level classifier has to be trained with labeled images and unlabeled open-world images -- we name it open world semantic segmentation (OSS). In OSS, the trained classifier is expected to identify unknown-class pixels and classify known-class pixels well. To solve OSS, we first investigate which distribution that unknown-class pixels obey. Then, motivated by the goodness-of-fit test, we use statistical measurements to show how a pixel fits the distribution of an unknown class and select highly-fitted pixels to form the unknown region in each image. Eventually, we propose an end-to-end learning framework, known-region-aware domain alignment (KRADA), to distinguish unknown classes while aligning distributions of known classes in labeled and unlabeled open-world images. The effectiveness of KRADA has been verified on two synthetic tasks and one COVID-19 segmentation task.
We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. In other words, given a text in 124 source languages, we translate it into a severely low resource language using only
~1,000 lines of low resource data without any external help. Firstly, we propose a systematic method to rank and choose source languages that are close to the low resource language. We call the linguistic definition of language family Family of Origin (FAMO), and we call the empirical definition of higher-ranked languages using our metrics Family of Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual Order-preserving Lexiconized Transformer (IPML) to train on ~1,000 lines (~3.5%) of low resource data. To translate named entities correctly, we build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages. Moreover, we also build a novel method of combining translations from different source languages into one. Using English as a hypothetical low resource language, we get a +23.9 BLEU increase over a multilingual baseline, and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset. We also have good results for a real severely low resource Mayan language, Eastern Pokomchi.
Solar cell electroluminescence (EL) defect segmentation is an interesting and challenging topic. Many methods have been proposed for EL defect detection, but these methods are still unsatisfactory due to the diversity of the defect and background. In
this paper, we provide a new idea of using generative adversarial network (GAN) for defect segmentation. Firstly, the GAN-based method removes the defect region in the input defective image to get a defect-free image, while keeping the background almost unchanged. Then, the subtracted image is obtained by making difference between the defective input image with the generated defect-free image. Finally, the defect region can be segmented through thresholding the subtracted image. To keep the background unchanged before and after image generation, we propose a novel strong identity GAN (SIGAN), which adopts a novel strong identity loss to constraint the background consistency. The SIGAN can be used not only for defect segmentation, but also small-samples defective dataset augmentation. Moreover, we release a new solar cell EL image dataset named as EL-2019, which includes three types of images: crack, finger interruption and defect-free. Experiments on EL-2019 dataset show that the proposed method achieves 90.34% F-score, which outperforms many state-of-the-art methods in terms of solar cell defects segmentation results.
Let $kappa$ be a positive real number and $minmathbb{N}cup{infty}$ be given. Let $p_{kappa, m}(n)$ denote the number of partitions of $n$ into the parts from the Piatestki-Shapiro sequence $(lfloor ell^{kappa}rfloor)_{ellin mathbb{N}}$ with at most $
m$ times (repetition allowed). In this paper we establish asymptotic formulas of Hardy-Ramanujan type for $p_{kappa, m}(n)$, by employing a framework of asymptotics of partitions established by Roth-Szekeres in 1953, as well as some results on equidistribution.
In this paper, we study properties of the coefficients appearing in the $q$-series expansion of $prod_{nge 1}[(1-q^n)/(1-q^{pn})]^delta$, the infinite Borwein product for an arbitrary prime $p$, raised to an arbitrary positive real power $delta$. We
use the Hardy--Ramanujan--Rademacher circle method to give an asymptotic formula for the coefficients. For $p=3$ we give an estimate of their growth which enables us to partially confirm an earlier conjecture of the first author concerning an observed sign pattern of the coefficients when the exponent $delta$ is within a specified range of positive real numbers. We further establish some vanishing and divisibility properties of the coefficients of the cube of the infinite Borwein product. We conclude with an Appendix presenting several new conjectures on precise sign patterns of infinite products raised to a real power which are similar to the conjecture we made in the $p=3$ case.
The recent paper by Chiara et al. provided the first experimental evidence of nuclear excitation by electron capture (NEEC), responding a long-standing theoretical prediction. NEEC was inferred to be the main channel to excite an isomer in Molybdenum
-93 to a higher state, leading to a rapid release of full isomer energy (isomer depletion). The deduced large excitation probability $P_{exc}$=0.010(3) for this mechanism implied strong influence on the survival of nuclei in stellar environments. However, the excitation probability is much higher than the estimated NEEC probability $P_{NEEC}$ according to a following theoretical work by approximately 9 orders of magnitude. Nevertheless, the reported $P_{exc}$ is predicted to be due to other unknown mechanism causing isomer depletion, which is expected to open up a new era of the storage and release of nuclear energy. Here we report an analysis of the reported experimental results, showing that the observed isomer depletion is significantly overestimated due to the contamination.
