Nonexistence of Minimal Pairs in $$L[{\mathbf d}]$$

Fang, Chengling; Liu, Jiang; Wu, Guohua; Yamaleev, M. M.

doi:10.1007/978-3-319-20028-6_18

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…In Ref. [17], only 136 Te was studied both in the shell model and the quasiparticle random phase approximation (QRPA), while 140 Xe was computed only using the latter due to computational constraints [61]. Here we have calculated transitions from both nuclei in the nuclear shell model using the jj56b model space.…”

Section: E Comparison With Existing Literaturementioning

confidence: 99%

First-forbidden transitions in the reactor anomaly

et al. 2019

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We describe here microscopic calculations performed on the dominant forbidden transitions in reactor antineutrino spectra above 4 MeV using the nuclear shell model. By taking into account Coulomb corrections in the most complete way, we calculate the shape factor with the highest fidelity and show strong deviations from allowed approximations and previously published results. Despite small differences in the ab initio electron cumulative spectra, large differences on the order of several percents are found in the antineutrino spectra. Based on the behaviour of the numerically calculated shape factors we propose a parametrization of forbidden spectra. Using Monte Carlo techniques we derive an estimated spectral correction and uncertainty due to forbidden transitions. We establish the dominance and importance of forbidden transitions in both the reactor anomaly and spectral shoulder analysis. Based on these results, we conclude that a correct treatment of forbidden transitions is indispensable in both the normalization anomaly and spectral shoulder.

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Section: E Comparison With Existing Literaturementioning

confidence: 99%

First-forbidden transitions in the reactor anomaly

et al. 2019

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“…Explaining relationships among entities. Several works aim at identifying the best subgraphs/paths to describe how some input entities are related [18], [20], [34]. Sun et.…”

Section: Related Workmentioning

confidence: 99%

Conditional Reliability in Uncertain Graphs

Khan

Bonchi

Gullo

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Many approaches and problem variants have been considered in the literature, majority of them assuming that edge-existence probabilities are fixed. Nevertheless, in real-world graphs, edge probabilities typically depend on external conditions. In metabolic networks, a protein can be converted into another protein with some probability depending on the presence of certain enzymes. In social influence networks, the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In transportation networks, the probability that a network segment will work properly or not, might depend on external conditions such as weather or time of the day. In this paper, we overcome this limitation and focus on conditional reliability, that is, assessing reliability when edge-existence probabilities depend on a set of conditions. In particular, we study the problem of determining the top-k conditions that maximize the reliability between two nodes. We deeply characterize our problem and show that, even employing polynomial-time reliability-estimation methods, it is NP-hard, does not admit any PTAS, and the underlying objective function is non-submodular. We then devise a practical method that targets both accuracy and efficiency. We also study natural generalizations of the problem with multiple source and target nodes. An extensive empirical evaluation on several large, real-life graphs demonstrates effectiveness and scalability of our methods.

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“…[Hnmp]HSO4, [Hmin]HSO4, [Tma]HSO4, [Tea]HSO4, [BSMin]HSO4, [BSTea]HSO4, [BSTma]HSO4, [BSTma]H2PO4 and [BSTma]p-TA were prepared using previously reported procedures [18][19][20][21][22][23][24]. The resulting ILs were identified by 1 H NMR and IR analyses.…”

Section: Preparation Of Ionic Liquidsmentioning

confidence: 99%