In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems. They can exactly describe dynamics of finite-rank systems, and be efficiently learned from data by moment based algorithms. Almost all OOM learning algorithms are developed based on the assumption of equilibrium data which is very difficult to guarantee in real life, especially for complex processes with large time scales. In this paper, we derive a nonequilibrium learning algorithm for OOMs, which dismisses this assumption and can effectively extract the equilibrium dynamics of a system from nonequilibrium observation data. In addition, we propose binless OOMs for the application of nonequilibrium learning to continuous-valued systems. In comparison with the other OOMs with continuous observations, binless OOMs can achieve consistent estimation from nonequilibrium data with only linear computational complexity.
[A]: Spectral learning of dynamic systems from nonequilibrium data∗


[Q]: Cooperative games model the allocation of profit from joint actions, following considerations such as stability and fairness. We propose the reliability extension of such games, where agents may fail to participate in the game. In the reliability extension, each agent only “survives” with a certain probability, and a coalition’s value is the probability that its surviving members would be a winning coalition in the base game. We study prominent solution concepts in such games, showing how to approximate the Shapley value and how to compute the core in games with few agent types. We also show that applying the reliability extension may stabilize the game, making the core non-empty even when the base game has an empty core.
[A]: Solving Cooperative Reliability Games


[Q]: This paper tests the hypothesis that distinctive feature classifiers anchored at phonetic landmarks can be transferred crosslingually without loss of accuracy. Three consonant voicing classifiers were developed: (1) manually selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either averaged across the segment or anchored at the landmark), and (3) acoustic features computed using a convolutional neural network (CNN). All detectors are trained on English data (TIMIT), and tested on English, Turkish, and Spanish (performance measured using F1 and accuracy). Experiments demonstrate that manual features outperform all MFCC classifiers, while CNN features outperform both. MFCC-based classifiers suffer an overall error rate increase of up to 96.1% when generalized from English to other languages. Manual features suffer only an up to 35.2% relative error rate increase, and CNN features actually perform the best on Turkish and Spanish, demonstrating that features capable of representing long-term spectral dynamics (CNN and landmark-based features) are able to generalize cross-lingually with little or no loss of accuracy.
[A]:
LANDMARK-BASED CONSONANT VOICING DETECTION ON MULTILINGUAL CORPORA