An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection

Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems. Though many papers were devoted to study incident classification algorithms, few study investigated how to enhance feature representation of incidents to improve AID performance. In this paper, we propose to use an unsupervised feature learning algorithm to generate higher level features to represent incidents. We used real incident data in the experiments and found that effective feature mapping function can be learnt from the data crosses the test sites. With the enhanced features, detection rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are significantly improved in all of the three representative cases. This approach also provides an alternative way to reduce the amount of labeled data, which is expensive to obtain, required in training better incident classifiers since the feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation Systems (ITSC 2012

When to Fire a CEO: Optimal Termination in Dynamic Contracts

The repeated agency model has been widely applied to a number of interesting and important problems in economics, though in many instances, the fact that the standard model generates transient dynamics limits the usefulness of the results obtained from the model for the simple reason that purely transient dynamc phenomena are empirically irrelevant since they cannot be systematically observed and studied. \ In this paper, we show to embed the standard long-term contracting model in an economic environment in which the inherently transient dynamics of the model are transformed into dyanmics which are stationary and ergodic. \ We then use the model to study CEO termination and issues of corporate takeover.

Producing type Iax supernovae from a specific class of helium-ignited WD explosions?

It has recently been proposed that one sub-class of type Ia supernovae (SNe Ia) is sufficiently both distinct and common to be classified separately from the bulk of SNe Ia, with a suggested class name of "type Iax supernovae" (SNe Iax), after SN 2002cx. However, their progenitors are still uncertain. We study whether the population properties of this class might be understood if the events originate from a subset of sub-Chandrasekhar mass explosions. In this potential progenitor population, a carbon--oxygen white dwarf (CO WD) accumulates a helium layer from a non-degenerate helium star; ignition of that helium layer then leads to ignition of the CO WD. We incorporated detailed binary evolution calculations for the progenitor systems into a binary population synthesis model to obtain rates and delay times for such events. The predicted Galactic event rate of these explosions is ~1.5\times10^{-3}{yr}^{-1} according to our standard model, in good agreement with the measured rates of SNe Iax. In addition, predicted delay times are ~70Myr-800Myr, consistent with the fact that most of SNe Iax have been discovered in late-type galaxies. If the explosions are assumed to be double-detonations -- following current model expectations -- then based on the CO WD masses at explosion we also estimate the distribution of resulting SN brightness (-13 \gtrsim M_{bol} \gtrsim -19mag), which can reproduce the empirical diversity of SNe Iax. We speculate on why binaries with non-degenerate donor stars might lead to SNe Iax if similar systems with degenerate donors do not. We suggest that the high mass of the helium layer necessary for ignition at the lower accretion rates typically delivered from non-degenerate donors might be necessary to produce SN 2002cx-like characteristics, perhaps even by changing the nature of the CO ignition.Comment: 8 pages, 10 figures, 1 table, accepted for publication in Astronomy and Astrophysic

Nucleation in binary polymer blends: A self-consistent field study

We study the structure and thermodynamics of the critical nuclei in metastable binary polymer blends using the self-consistent field method. At the mean-field level, our results are valid throughout the entire metastable region and provide a smooth crossover from the classical capillary-theory predictions near the coexistence curve to the density functional predictions of Cahn and Hilliard (properly transcribed into expressions involving the parameters of the binary polymer blends) near the spinodal. An estimate of the free energy barrier provides a quantitative criterion (the Ginzburg criterion) for the validity of the (mean-field) self-consistent approach. The region where mean-field theory is valid and where there can be a measurable nucleation rate is shown to be poorly described by the existing limiting theories; our predictions are therefore most relevant in this region. We discuss our results in connection with recent experimental observations by Balsara and co-workers