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

Satellite personal communications system

Voice channel communication between low power mobile stations dispersed over a large area is provided by a system which includes a geostationary satellite utilizing a large UHF antenna that can receive a transmission from a caller and retransmit it over any one beam of a matrix of narrow beams, so the chosen beam covers an area in which a designated called party is located. A single up-link control channel occupying a narrow frequency band, can be utilized to receive dial up signals from a caller, and another single down link control channel can be utilized to ring up the called party located anywhere within the continental United States. The satellite antenna includes a matrix of feed horns that not only direct the beams in a controlled matrix onto the area of the continental United States, but also permit detection of the region from which the caller's signal is transmitted and the region from which the called party's answer is received, to enable the interconnection of signals received from these two regions. The system is particularly useful for rural areas

Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences

A search for rapid optical variability in radio-quiet quasars

The detection of rapid variability on a time-scale of hours in radio-quiet quasars (RQQSOs) could be a powerful discriminator between starburst, accretion disc and relativistic jet models of these sources. This paper contains an account of a dedicated search for rapid optical variability in RQQSOs. The technique used differential photometry between the RQQSO and stars in the same field of view of the CCD. The 23 RQQSOs that were observed all have high luminosities (-27 1. The total amount of observation time was about 60 hours and these observations are part of an ongoing programme, started in September 1990, to search for rapid variability in RQQSOs. No evidence for short-term variability greater than about 0.1 magnitudes was detected in any of the 23 sources, however long-term variability was recorded for the radio-quiet quasar PG 2112+059. The finding charts are included here because they identify the RQQSO and the reference stars used in the photometry, and hence are available for use by other observers.Comment: Accepted for publication in A&AS. 10 pages, 3 figures. Figure 1 (finding charts) available by anonymous ftp from: bermuda.ucd.ie:/pub/outgoing/charts.eps.g

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

Light echoes reveal an unexpectedly cool Eta Carinae during its 19th-century Great Eruption

Eta Carinae (Eta Car) is one of the most massive binary stars in the Milky Way. It became the second-brightest star in the sky during its mid-19th century "Great Eruption," but then faded from view (with only naked-eye estimates of brightness). Its eruption is unique among known astronomical transients in that it exceeded the Eddington luminosity limit for 10 years. Because it is only 2.3 kpc away, spatially resolved studies of the nebula have constrained the ejected mass and velocity, indicating that in its 19th century eruption, Eta Car ejected more than 10 M_solar in an event that had 10% of the energy of a typical core-collapse supernova without destroying the star. Here we report the discovery of light echoes of Eta Carinae which appear to be from the 1838-1858 Great Eruption. Spectra of these light echoes show only absorption lines, which are blueshifted by -210 km/s, in good agreement with predicted expansion speeds. The light-echo spectra correlate best with those of G2-G5 supergiant spectra, which have effective temperatures of ~5000 K. In contrast to the class of extragalactic outbursts assumed to be analogs of Eta Car's Great Eruption, the effective temperature of its outburst is significantly cooler than allowed by standard opaque wind models. This indicates that other physical mechanisms like an energetic blast wave may have triggered and influenced the eruption.Comment: Accepted for publication by Nature; 4 pages, 4 figures, SI: 6 pages, 3 figures, 5 table