Sampling from Stochastic Finite Automata with Applications to CTC Decoding

Stochastic finite automata arise naturally in many language and speech processing tasks. They include stochastic acceptors, which represent certain probability distributions over random strings. We consider the problem of efficient sampling: drawing random string variates from the probability distribution represented by stochastic automata and transformations of those. We show that path-sampling is effective and can be efficient if the epsilon-graph of a finite automaton is acyclic. We provide an algorithm that ensures this by conflating epsilon-cycles within strongly connected components. Sampling is also effective in the presence of non-injective transformations of strings. We illustrate this in the context of decoding for Connectionist Temporal Classification (CTC), where the predictive probabilities yield auxiliary sequences which are transformed into shorter labeling strings. We can sample efficiently from the transformed labeling distribution and use this in two different strategies for finding the most probable CTC labeling

Attack-Resilient Supervisory Control of Discrete-Event Systems

In this work, we study the problem of supervisory control of discrete-event systems (DES) in the presence of attacks that tamper with inputs and outputs of the plant. We consider a very general system setup as we focus on both deterministic and nondeterministic plants that we model as finite state transducers (FSTs); this also covers the conventional approach to modeling DES as deterministic finite automata. Furthermore, we cover a wide class of attacks that can nondeterministically add, remove, or rewrite a sensing and/or actuation word to any word from predefined regular languages, and show how such attacks can be modeled by nondeterministic FSTs; we also present how the use of FSTs facilitates modeling realistic (and very complex) attacks, as well as provides the foundation for design of attack-resilient supervisory controllers. Specifically, we first consider the supervisory control problem for deterministic plants with attacks (i) only on their sensors, (ii) only on their actuators, and (iii) both on their sensors and actuators. For each case, we develop new conditions for controllability in the presence of attacks, as well as synthesizing algorithms to obtain FST-based description of such attack-resilient supervisors. A derived resilient controller provides a set of all safe control words that can keep the plant work desirably even in the presence of corrupted observation and/or if the control words are subjected to actuation attacks. Then, we extend the controllability theorems and the supervisor synthesizing algorithms to nondeterministic plants that satisfy a nonblocking condition. Finally, we illustrate applicability of our methodology on several examples and numerical case-studies

Multi-dimensional Boltzmann Sampling of Languages

This paper addresses the uniform random generation of words from a context-free language (over an alphabet of size $k$), while constraining every letter to a targeted frequency of occurrence. Our approach consists in a multidimensional extension of Boltzmann samplers \cite{Duchon2004}. We show that, under mostly \emph{strong-connectivity} hypotheses, our samplers return a word of size in $[(1-\varepsilon)n, (1+\varepsilon)n]$ and exact frequency in $\mathcal{O}(n^{1+k/2})$ expected time. Moreover, if we accept tolerance intervals of width in $\Omega(\sqrt{n})$ for the number of occurrences of each letters, our samplers perform an approximate-size generation of words in expected $\mathcal{O}(n)$ time. We illustrate these techniques on the generation of Tetris tessellations with uniform statistics in the different types of tetraminoes.Comment: 12p