In this paper, we present a derivative-based, functional recognizer and parser generator for visibly pushdown grammars. The generated parser accepts ambiguous grammars and produces a parse forest containing all valid parse trees for an input string i
n linear time. Each parse tree in the forest can then be extracted also in linear time. Besides the parser generator, to allow more flexible forms of the visibly pushdown grammars, we also present a translator that converts a tagged CFG to a visibly pushdown grammar in a sound way, and the parse trees of the tagged CFG are further produced by running the semantic actions embedded in the parse trees of the translated visibly pushdown grammar. The performance of the parser is compared with a popular parsing tool ANTLR and other popular hand-crafted parsers. The correctness of the core parsing algorithm is formally verified in the proof assistant Coq.
We give two concrete examples of continuous valuations on dcpos to separate minimal valuations, point-continuous valuations and continuous valuations: (1) Let $mathcal J$ be the Johnstones non-sober dcpo, and $mu$ be the continuous valuation on $ma
thcal J$ with $mu(U) =1$ for nonempty Scott opens $U$ and $mu(U) = 0$ for $U=emptyset$. Then $mu$ is a point-continuous valuation on $mathcal J$ that is not minimal. (2) Lebesgue measure extends to a measure on the Sorgenfrey line $mathbb R_{l}$. Its restriction to the open subsets of $mathbb R_{l}$ is a continuous valuation $lambda$. Then its image valuation $overlinelambda$ through the embedding of $mathbb R_{l}$ into its Smyth powerdomain $mathcal Qmathbb R_{l}$ in the Scott topology is a continuous valuation that is not point-continuous. We believe that our construction $overlinelambda$ might be useful in giving counterexamples displaying the failure of the general Fubini-type equations on dcpos.
Hyper-parameters of time series models play an important role in time series analysis. Slight differences in hyper-parameters might lead to very different forecast results for a given model, and therefore, selecting good hyper-parameter values is ind
ispensable. Most of the existing generic hyper-parameter tuning methods, such as Grid Search, Random Search, Bayesian Optimal Search, are based on one key component - search, and thus they are computationally expensive and cannot be applied to fast and scalable time-series hyper-parameter tuning (HPT). We propose a self-supervised learning framework for HPT (SSL-HPT), which uses time series features as inputs and produces optimal hyper-parameters. SSL-HPT algorithm is 6-20x faster at getting hyper-parameters compared to other search based algorithms while producing comparable accurate forecasting results in various applications.
Impulsive signature enhancement (ISE) is an important topic in the monitoring of rotating machinery and many different methods have been proposed. Even though, the topic of how to leverage these ISE techniques to improve the data quality in terms of
prognostics and health management (PHM) still needs to be investigated. In this work, a systematic view for data quality enhancement is presented. The data quality issues for the prognostics and health management (PHM) of rotating machinery are identified, and the major steps to enhance data quality are organized. Based on this, a novel ISE algorithm is originally proposed, the importance of extracting scale invariant features are explained, and also related features are proposed for the PHM of rotating machinery. In order to demonstrate the effectiveness of the novelties, two experimental studies are presented. The final results indicate that the proposed method can be effectively employed to enhance the data quality for machine failure detection and diagnosis.
In this paper we will prove a uniformity result for the Iitaka fibration $f:X rightarrow Y$, provided that the generic fiber has a good minimal model and the variation of $f$ is zero or that $kappa(X)=rm{dim}(X)-1$.
A Jefferson Lab experiment proposal was discussed in this talk. The experiment is designed to measure the beam-target double-spin asymmetries $A_{1n}^h$ in semi-inclusive deep-inelastic $vec n({vec e}, e^prime pi^+)X$ and $vec n({vec e}, e^prime pi^-
)X$ reactions on a longitudinally polarized $^3$He target. In addition to $A_{1n}^h$, the flavor non-singlet combination $A_{1n}^{pi^+ - pi^-}$, in which the gluons do not contribute, will be determined with high precision to extract $Delta d_v(x)$ independent of the knowledge of the fragmentation functions. The data will also impose strong constraints on quark and gluon polarizations through a global NLO QCD fit.
Experiment E04-113 at Jefferson Lab Hall C plans to measure the beam-target double-spin asymmetries in semi-inclusive deep-inelastic $vec p(e, e^prime h)X$ and $vec d(e, e^prime h)X$ reactions ($h=pi^+, pi^-, K^+$ or$K^-$) with a 6 GeV polarized elec
tron beam and longitudinally polarized NH$_3$ and LiD targets. The high statistic data will allow a spin-flavor decomposition in the region of $x=0.12 sim 0.41$ at $Q^2=1.21sim 3.14$ GeV$^2$. Especially, leading-order and next-to-leading order spin-flavor decomposition of $Delta u_v$, $Delta d_v$ and $Delta bar{u} - Delta bar{d}$ will be extracted based on the measurement of the combined asymmetries $A_{1N}^{pi^+ - pi^-}$. The possible flavor asymmetry of the polarized sea will be addressed in this experiment.
