353 research outputs found
Time-Staging Enhancement of Hybrid System Falsification
Optimization-based falsification employs stochastic optimization algorithms
to search for error input of hybrid systems. In this paper we introduce a
simple idea to enhance falsification, namely time staging, that allows the
time-causal structure of time-dependent signals to be exploited by the
optimizers. Time staging consists of running a falsification solver multiple
times, from one interval to another, incrementally constructing an input signal
candidate. Our experiments show that time staging can dramatically increase
performance in some realistic examples. We also present theoretical results
that suggest the kinds of models and specifications for which time staging is
likely to be effective
Dynamics of fractional N-soliton solutions with anomalous dispersions of integrable fractional higher-order nonlinear Schr\"odinger equations
In this paper, we explore the anomalous dispersive relations, inverse
scattering transform and fractional N-soliton solutions of the integrable
fractional higher-order nonlinear Schrodinger (fHONLS) equations, containing
the fractional Hirota (fHirota), fractional complex mKdV (fcmKdV), and
fractional Lakshmanan-Porsezian-Daniel (fLPD) equations, etc. The inverse
scattering problem can be solved exactly by means of the matrix Riemann-Hilbert
problem with simple poles. As a consequence, an explicit formula is found for
the fractional N-soliton solutions of the fHONLS equations in the
reflectionless case. In particular, we analyze the fractional one-, two- and
three-soliton solutions with anomalous dispersions of fHirota and fcmKdV
equations. The wave, group, and phase velocities of these envelope fractional
1-soliton solutions are related to the power laws of their amplitudes. These
obtained fractional N-soliton solutions may be useful to explain the related
super-dispersion transports of nonlinear waves in fractional nonlinear media.Comment: 14 pages, 4 figure
Transcribing Content from Structural Images with Spotlight Mechanism
Transcribing content from structural images, e.g., writing notes from music
scores, is a challenging task as not only the content objects should be
recognized, but the internal structure should also be preserved. Existing image
recognition methods mainly work on images with simple content (e.g., text lines
with characters), but are not capable to identify ones with more complex
content (e.g., structured symbols), which often follow a fine-grained grammar.
To this end, in this paper, we propose a hierarchical Spotlight Transcribing
Network (STN) framework followed by a two-stage "where-to-what" solution.
Specifically, we first decide "where-to-look" through a novel spotlight
mechanism to focus on different areas of the original image following its
structure. Then, we decide "what-to-write" by developing a GRU based network
with the spotlight areas for transcribing the content accordingly. Moreover, we
propose two implementations on the basis of STN, i.e., STNM and STNR, where the
spotlight movement follows the Markov property and Recurrent modeling,
respectively. We also design a reinforcement method to refine the framework by
self-improving the spotlight mechanism. We conduct extensive experiments on
many structural image datasets, where the results clearly demonstrate the
effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining
(KDD'18
Online Causation Monitoring of Signal Temporal Logic
Online monitoring is an effective validation approach for hybrid systems,
that, at runtime, checks whether the (partial) signals of a system satisfy a
specification in, e.g., Signal Temporal Logic (STL). The classic STL monitoring
is performed by computing a robustness interval that specifies, at each
instant, how far the monitored signals are from violating and satisfying the
specification. However, since a robustness interval monotonically shrinks
during monitoring, classic online monitors may fail in reporting new violations
or in precisely describing the system evolution at the current instant. In this
paper, we tackle these issues by considering the causation of violation or
satisfaction, instead of directly using the robustness. We first introduce a
Boolean causation monitor that decides whether each instant is relevant to the
violation or satisfaction of the specification. We then extend this monitor to
a quantitative causation monitor that tells how far an instant is from being
relevant to the violation or satisfaction. We further show that classic
monitors can be derived from our proposed ones. Experimental results show that
the two proposed monitors are able to provide more detailed information about
system evolution, without requiring a significantly higher monitoring cost.Comment: 31 pages, 7 figures, the full version of the paper accepted by CAV
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