Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization

We consider the search for a maximum likelihood assignment of hidden derivations and grammar weights for a probabilistic context-free grammar, the problem approximately solved by “Viterbi training.” We show that solving and even approximating Viterbi training for PCFGs is NP-hard. We motivate the use of uniformat-random initialization for Viterbi EM as an optimal initializer in absence of further information about the correct model parameters, providing an approximate bound on the log-likelihood.

Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning

Probabilistic grammars are generative statistical models that are useful for compositional and sequential structures. They are used ubiquitously in computational linguistics. We present a framework, reminiscent of structural risk minimization, for empirical risk minimization of probabilistic grammars using the log-loss. We derive sample complexity bounds in this framework that apply both to the supervised setting and the unsupervised setting. By making assumptions about the underlying distribution that are appropriate for natural language scenarios, we are able to derive distribution-dependent sample complexity bounds for probabilistic grammars. We also give simple algorithms for carrying out empirical risk minimization using this framework in both the supervised and unsupervised settings. In the unsupervised case, we show that the problem of minimizing empirical risk is NP-hard. We therefore suggest an approximate algorithm, similar to expectation-maximization, to minimize the empirical risk. Learning from data is central to contemporary computational linguistics. It is in common in such learning to estimate a model in a parametric family using the maximum likelihood principle. This principle applies in the supervised case (i.e., using annotate

Empirical Risk Minimization with Approximations of Probabilistic Grammars

Probabilistic grammars are generative statistical models that are useful for compositional and sequential structures. We present a framework, reminiscent of structural risk minimization, for empirical risk minimization of the parameters of a fixed probabilistic grammar using the log-loss. We derive sample complexity bounds in this framework that apply both to the supervised setting and the unsupervised setting.

Nitramine propellants

Nitramine propellants without a pressure exponent shift in the burning rate curves are prepared by matching the burning rate of a selected nitramine or combination of nitramines within 10% of burning rate of a plasticized active binder so as to smooth out the break point appearance in the burning rate curve

Toward a dynamical systems analysis of neuromodulation

This work presents some first steps toward a more thorough understanding of the control systems employed in evolutionary robotics. In order to choose an appropriate architecture or to construct an effective novel control system we need insights into what makes control systems successful, robust, evolvable, etc. Here we present analysis intended to shed light on this type of question as it applies to a novel class of artificial neural networks that include a neuromodulatory mechanism: GasNets. We begin by instantiating a particular GasNet subcircuit responsible for tuneable pattern generation and thought to underpin the attractive property of “temporal adaptivity”. Rather than work within the GasNet formalism, we develop an extension of the well-known FitzHugh-Nagumo equations. The continuous nature of our model allows us to conduct a thorough dynamical systems analysis and to draw parallels between this subcircuit and beating/bursting phenomena reported in the neuroscience literature. We then proceed to explore the effects of different types of parameter modulation on the system dynamics. We conclude that while there are key differences between the gain modulation used in the GasNet and alternative schemes (including threshold modulation of more traditional synaptic input), both approaches are able to produce tuneable pattern generation. While it appears, at least in this study, that the GasNet’s gain modulation may not be crucial to pattern generation , we go on to suggest some possible advantages it could confer

Joint Morphological and Syntactic Disambiguation

In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a single problem? We describe several efficient, probabilistically interpretable ways to apply joint inference to morphological and syntactic disambiguation using lattice parsing. Joint inference is shown to compare favorably to pipeline parsing methods across a variety of component models. State-of-the-art performance on Hebrew Treebank parsing is demonstrated using the new method. The benefits of joint inference are modest with the current component models, but appear to increase as components themselves improve

Transient processes in the combustion of nitramine propellants

A transient combustion model of nitramine propellants is combined with an isentropic compression shock formation model to determine the role of nitramine propellant combustion in DDT, excluding effects associated with propellant structural properties or mechanical behavior. The model is derived to represent the closed pipe experiment that is widely used to characterize explosives, except that the combustible material is a monolithic charge rather than compressed powder. Computations reveal that the transient combustion process cannot by itself produce DDT by this model. Compressibility of the solid at high pressure is the key factor limiting pressure buildups created by the combustion. On the other hand, combustion mechanisms which promote pressure buildups are identified and related to propellant formulation variables. Additional combustion instability data for nitramine propellants are presented. Although measured combustion response continues to be low, more data are required to distinguish HMX and active binder component contributions. A design for a closed vessel apparatus for experimental studies of high pressure combustion is discussed

Nitramine smokeless propellant research

A transient ballistics and combustion model is derived to represent the closed vessel experiment that is widely used to characterize propellants. A computer program is developed to solve the time-dependent equations, and is applied to explain aspects of closed vessel behavior. In the case of nitramine propellants the cratering of the burning surface associated with combustion above break-point pressures augments the effective burning rate as deduced from the closed vessel experiment. Low pressure combustion is significantly affected by the ignition process and, in the case of nitramine propellants, by the developing and changing surface structure. Thus, burning rates deduced from the closed vessel experiment may or may not agree with those measured in the equilibrium strand burner. Series of T burner experiments are performed to compare the combustion instability characteristics of nitramine (HMX) containing propellants and ammonium perchlorate (AP)propellants. Although ash produced by more fuel rich propellants could have provided mechanical suppression, results from clean-burning propellants permit the conclusion that HMX reduces the acoustic driving

On Markovian solutions to Markov Chain BSDEs

We study (backward) stochastic differential equations with noise coming from a finite state Markov chain. We show that, for the solutions of these equations to be `Markovian', in the sense that they are deterministic functions of the state of the underlying chain, the integrand must be of a specific form. This allows us to connect these equations to coupled systems of ODEs, and hence to give fast numerical methods for the evaluation of Markov-Chain BSDEs

Functional characterization of a glutamate/aspartate transporter from the mosquito Aedes aegypti

Glutamate elicits a variety of effects in insects, including inhibitory and excitatory signals at both neuromuscular junctions and brain. Insect glutamatergic neurotransmission has been studied in great depth especially from the standpoint of the receptor-mediated effects, but the molecular mechanisms involved in the termination of the numerous glutamatergic signals have only recently begun to receive attention. In vertebrates, glutamatergic signals are terminated by Na^+/K^+-dependent high-affinity excitatory amino acid transporters (EAAT), which have been cloned and characterized extensively. Cloning and characterization of a few insect homologues have followed, but functional information for these homologues is still limited. Here we report a study conducted on a cloned mosquito EAAT homologue isolated from the vector of the dengue virus, Aedes aegypti. The deduced amino acid sequence of the protein, AeaEAAT, exhibits 40–50% identity with mammalian EAATs, and 45–50% identity to other insect EAATs characterized thus far. It transports l-glutamate as well as l- and d-aspartate with high affinity in the micromolar range, and demonstrates a substrate-elicited anion conductance when heterologously expressed in Xenopus laevis oocytes, as found with mammalian homologues. Analysis of the spatial distribution of the protein demonstrates high expression levels in the adult thorax, which is mostly observed in the thoracic ganglia. Together, the work presented here provides a thorough examination of the role played by glutamate transport in Ae. aegypti