Sound the Alarm: Limitations of Liability in Alarm Service Contracts

Home and business owners increasingly rely on alarm systems to protect against theft and property damage. When a burglary or fire occurs and an alarm service customer discovers that the alarm company negligently failed to call the police or fire department, the customer understandably would expect redress for the company’s failure to provide its service. Many customers would be surprised, though, to discover that an alarm company’s liability is often contractually limited to a relatively token amount unrelated to the cost of the service, even when the alarm company is negligent. Some states view these limitations of liability as exculpatory clauses and determine their enforceability based on whether they are unconscionable or violate public policy. Other states view them as liquidated damages and apply a penalty test to determine their enforceability. This Note addresses the differences between these two approaches in the context of the unique remedy difficulties inherent in alarm service contracts. This Note then argues that the prevailing policy rationales for enforcing alarm service provisions that limit a party’s liability for its own negligence are misguided and advocates that these provisions should not be enforced as a matter of public policy

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

Solutions of Backward Stochastic Differential Equations on Markov Chains

We consider backward stochastic differential equations (BSDEs) related to finite state, continuous time Markov chains. We show that appropriate solutions exist for arbitrary terminal conditions, and are unique up to sets of measure zero. We do not require the generating functions to be monotonic, instead using only an appropriate Lipschitz continuity condition.Comment: To appear in Communications on Stochastic Analysis, August 200

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.

Boundary-Layer Similar Solutions for Equilibrium Dissociated Air and Application to the Calculation of Laminar Heat-Transfer Distribution on Blunt Bodies in High-Speed Flow

No abstract availabl

An integrated neuro-mechanical model of C. elegans forward locomotion

One of the most tractable organisms for the study of nervous systems is the nematode Caenorhabditis elegans, whose locomotion in particular has been the subject of a number of models. In this paper we present a first integrated neuro-mechanical model of forward locomotion. We find that a previous neural model is robust to the addition of a body with mechanical properties, and that the integrated model produces oscillations with a more realistic frequency and waveform than the neural model alone. We conclude that the body and environment are likely to be important components of the worm’s locomotion subsystem

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

Detailed Abundances of Two Very Metal-Poor Stars in Dwarf Galaxies

The most metal-poor stars in dwarf spheroidal galaxies (dSphs) can show the nucleosynthetic patterns of one or a few supernovae (SNe). These SNe could have zero metallicity, making metal-poor dSph stars the closest surviving links to Population III stars. Metal-poor dSph stars also help to reveal the formation mechanism of the Milky Way (MW) halo. We present the detailed abundances from Keck/HIRES spectroscopy for two very metal-poor stars in two MW dSphs. One star, in the Sculptor dSph, has [Fe I/H] = -2.40. The other star, in the Ursa Minor dSph, has [Fe I/H] = -3.16. Both stars fall in the previously discovered low-metallicity, high-[α/Fe] plateau. Most abundance ratios of very metal-poor stars in these two dSphs are largely consistent with very metal-poor halo stars. However, the abundances of Na and some r-process elements lie at the lower end of the envelope defined by inner halo stars of similar metallicity. We propose that the metallicity dependence of SN yields is the cause. The earliest SNe in low-mass dSphs have less gas to pollute than the earliest SNe in massive halo progenitors. As a result, dSph stars at –3 < [Fe/H] < –2 sample SNe with [Fe/H] Lt –3, whereas halo stars in the same metallicity range sample SNe with [Fe/H] ~ –3. Consequently, enhancements in [Na/Fe] and [r/Fe] were deferred to higher metallicity in dSphs than in the progenitors of the inner halo