446,510 research outputs found
Computation of moments for probabilistic finite-state automata
[EN] The computation of moments of probabilistic finite-state automata (PFA) is researched in this article. First, the computation of moments of the length of the paths is introduced for general PFA, and then, the computation of moments of the number of times that a symbol appears in the strings generated by the PFA is described. These computations require a matrix inversion. Acyclic PFA, such as word graphs, are quite common in many practical applications. Algorithms for the efficient computation of the moments for acyclic PFA are also presented in this paper.This work has been partially supported by the Ministerio de Ciencia y Tecnologia under the grant TIN2017-91452-EXP (IBEM), by the Generalitat Valenciana under the grant PROMETE0/2019/121 (DeepPattern), and by the grant "Ayudas Fundacion BBVA a equipos de investigacion cientifica 2018" (PR[8]_HUM_C2_0087).Sánchez Peiró, JA.; Romero, V. (2020). Computation of moments for probabilistic finite-state automata. Information Sciences. 516:388-400. https://doi.org/10.1016/j.ins.2019.12.052S388400516Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sjölander, K., Underwood, R. C., & Haussler, D. (1994). Stochastic context-free grammers for tRNA modeling. Nucleic Acids Research, 22(23), 5112-5120. doi:10.1093/nar/22.23.5112Álvaro, F., Sánchez, J.-A., & Benedí, J.-M. (2016). An integrated grammar-based approach for mathematical expression recognition. Pattern Recognition, 51, 135-147. doi:10.1016/j.patcog.2015.09.013Mohri, M., Pereira, F., & Riley, M. (2002). Weighted finite-state transducers in speech recognition. Computer Speech & Language, 16(1), 69-88. doi:10.1006/csla.2001.0184Casacuberta, F., & Vidal, E. (2004). Machine Translation with Inferred Stochastic Finite-State Transducers. Computational Linguistics, 30(2), 205-225. doi:10.1162/089120104323093294Ortmanns, S., Ney, H., & Aubert, X. (1997). A word graph algorithm for large vocabulary continuous speech recognition. Computer Speech & Language, 11(1), 43-72. doi:10.1006/csla.1996.0022Soule, S. (1974). Entropies of probabilistic grammars. Information and Control, 25(1), 57-74. doi:10.1016/s0019-9958(74)90799-2Justesen, J., & Larsen, K. J. (1975). On probabilistic context-free grammars that achieve capacity. Information and Control, 29(3), 268-285. doi:10.1016/s0019-9958(75)90437-4Hernando, D., Crespi, V., & Cybenko, G. (2005). Efficient Computation of the Hidden Markov Model Entropy for a Given Observation Sequence. IEEE Transactions on Information Theory, 51(7), 2681-2685. doi:10.1109/tit.2005.850223Nederhof, M.-J., & Satta, G. (2008). Computation of distances for regular and context-free probabilistic languages. Theoretical Computer Science, 395(2-3), 235-254. doi:10.1016/j.tcs.2008.01.010CORTES, C., MOHRI, M., RASTOGI, A., & RILEY, M. (2008). ON THE COMPUTATION OF THE RELATIVE ENTROPY OF PROBABILISTIC AUTOMATA. International Journal of Foundations of Computer Science, 19(01), 219-242. doi:10.1142/s0129054108005644Ilic, V. M., Stankovi, M. S., & Todorovic, B. T. (2011). Entropy Message Passing. IEEE Transactions on Information Theory, 57(1), 375-380. doi:10.1109/tit.2010.2090235Booth, T. L., & Thompson, R. A. (1973). Applying Probability Measures to Abstract Languages. IEEE Transactions on Computers, C-22(5), 442-450. doi:10.1109/t-c.1973.223746Thompson, R. A. (1974). Determination of Probabilistic Grammars for Functionally Specified Probability-Measure Languages. IEEE Transactions on Computers, C-23(6), 603-614. doi:10.1109/t-c.1974.224001Wetherell, C. S. (1980). Probabilistic Languages: A Review and Some Open Questions. ACM Computing Surveys, 12(4), 361-379. doi:10.1145/356827.356829Sanchez, J.-A., & Benedi, J.-M. (1997). Consistency of stochastic context-free grammars from probabilistic estimation based on growth transformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(9), 1052-1055. doi:10.1109/34.615455Hutchins, S. E. (1972). Moments of string and derivation lengths of stochastic context-free grammars. Information Sciences, 4(2), 179-191. doi:10.1016/0020-0255(72)90011-4Heim, A., Sidorenko, V., & Sorger, U. (2008). Computation of distributions and their moments in the trellis. Advances in Mathematics of Communications, 2(4), 373-391. doi:10.3934/amc.2008.2.373Vidal, E., Thollard, F., de la Higuera, C., Casacuberta, F., & Carrasco, R. C. (2005). Probabilistic finite-state machines - part I. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7), 1013-1025. doi:10.1109/tpami.2005.147Sánchez, J. A., Rocha, M. A., Romero, V., & Villegas, M. (2018). On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models. Computational Linguistics, 44(1), 17-37. doi:10.1162/coli_a_0030
Termination of rewriting strategies: a generic approach
We propose a generic termination proof method for rewriting under strategies,
based on an explicit induction on the termination property. Rewriting trees on
ground terms are modeled by proof trees, generated by alternatively applying
narrowing and abstracting steps. The induction principle is applied through the
abstraction mechanism, where terms are replaced by variables representing any
of their normal forms. The induction ordering is not given a priori, but
defined with ordering constraints, incrementally set during the proof.
Abstraction constraints can be used to control the narrowing mechanism, well
known to easily diverge. The generic method is then instantiated for the
innermost, outermost and local strategies.Comment: 49 page
An Analysis of Publication Venues for Automatic Differentiation Research
We present the results of our analysis of publication venues for papers on
automatic differentiation (AD), covering academic journals and conference
proceedings. Our data are collected from the AD publications database
maintained by the autodiff.org community website. The database is purpose-built
for the AD field and is expanding via submissions by AD researchers. Therefore,
it provides a relatively noise-free list of publications relating to the field.
However, it does include noise in the form of variant spellings of journal and
conference names. We handle this by manually correcting and merging these
variants under the official names of corresponding venues. We also share the
raw data we get after these corrections.Comment: 6 pages, 3 figure
Verifying nondeterministic probabilistic channel systems against -regular linear-time properties
Lossy channel systems (LCSs) are systems of finite state automata that
communicate via unreliable unbounded fifo channels. In order to circumvent the
undecidability of model checking for nondeterministic
LCSs, probabilistic models have been introduced, where it can be decided
whether a linear-time property holds almost surely. However, such fully
probabilistic systems are not a faithful model of nondeterministic protocols.
We study a hybrid model for LCSs where losses of messages are seen as faults
occurring with some given probability, and where the internal behavior of the
system remains nondeterministic. Thus the semantics is in terms of
infinite-state Markov decision processes. The purpose of this article is to
discuss the decidability of linear-time properties formalized by formulas of
linear temporal logic (LTL). Our focus is on the qualitative setting where one
asks, e.g., whether a LTL-formula holds almost surely or with zero probability
(in case the formula describes the bad behaviors). Surprisingly, it turns out
that -- in contrast to finite-state Markov decision processes -- the
satisfaction relation for LTL formulas depends on the chosen type of schedulers
that resolve the nondeterminism. While all variants of the qualitative LTL
model checking problem for the full class of history-dependent schedulers are
undecidable, the same questions for finite-memory scheduler can be solved
algorithmically. However, the restriction to reachability properties and
special kinds of recurrent reachability properties yields decidable
verification problems for the full class of schedulers, which -- for this
restricted class of properties -- are as powerful as finite-memory schedulers,
or even a subclass of them.Comment: 39 page
Abstract Canonical Inference
An abstract framework of canonical inference is used to explore how different
proof orderings induce different variants of saturation and completeness.
Notions like completion, paramodulation, saturation, redundancy elimination,
and rewrite-system reduction are connected to proof orderings. Fairness of
deductive mechanisms is defined in terms of proof orderings, distinguishing
between (ordinary) "fairness," which yields completeness, and "uniform
fairness," which yields saturation.Comment: 28 pages, no figures, to appear in ACM Trans. on Computational Logi
An Algebra of Quantum Processes
We introduce an algebra qCCS of pure quantum processes in which no classical
data is involved, communications by moving quantum states physically are
allowed, and computations is modeled by super-operators. An operational
semantics of qCCS is presented in terms of (non-probabilistic) labeled
transition systems. Strong bisimulation between processes modeled in qCCS is
defined, and its fundamental algebraic properties are established, including
uniqueness of the solutions of recursive equations. To model sequential
computation in qCCS, a reduction relation between processes is defined. By
combining reduction relation and strong bisimulation we introduce the notion of
strong reduction-bisimulation, which is a device for observing interaction of
computation and communication in quantum systems. Finally, a notion of strong
approximate bisimulation (equivalently, strong bisimulation distance) and its
reduction counterpart are introduced. It is proved that both approximate
bisimilarity and approximate reduction-bisimilarity are preserved by various
constructors of quantum processes. This provides us with a formal tool for
observing robustness of quantum processes against inaccuracy in the
implementation of its elementary gates
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