Blocking of word-boundary consonant lengthening in Sienese Italian

This paper examines an aspect of Raddoppiamento sintattico (RS), the lengthening of word-initial consonants following certain words e.g. tre [mm]ele ‘three apples’ in Italian. Most phonological accounts claim the phenomenon is predictable and obligatory (e.g. Nespor & Vogel 1986). However, descriptive sources on Italian (e.g. Camilli 1941) have long claimed that RS interacts with and can be blocked by other phenomena operative in natural speech e.g. pausing. In this paper we outline the phonetic details of the RS blocking phenomena and present the results of an auditory and preliminary acoustic analysis of the interaction between RS and these other phenomena based on a corpus of spontaneous speech data

Hierarchical Knowledge-Gradient for Sequential Sampling

We consider the problem of selecting the best of a finite but very large set of alternatives. Each alternative may be characterized by a multi-dimensional vector and has independent normal rewards. This problem arises in various settings such as (i) ranking and selection, (ii) simulation optimization where the unknown mean of each alternative is estimated with stochastic simulation output, and (iii) approximate dynamic programming where we need to estimate values based on Monte-Carlo simulation. We use a Bayesian probability model for the unknown reward of each alternative and follow a fully sequential sampling policy called the knowledge-gradient policy. This policy myopically optimizes the expected increment in the value of sampling information in each time period. Because the number of alternatives is large, we propose a hierarchical aggregation technique that uses the common features shared by alternatives to learn about many alternatives from even a single measurement, thus greatly reducing the measurement effort required. We demonstrate how this hierarchical knowledge-gradient policy can be applied to efficiently maximize a continuous function and prove that this policy finds a globally optimal alternative in the limit

A New Optimal Stepsize For Approximate Dynamic Programming

Approximate dynamic programming (ADP) has proven itself in a wide range of applications spanning large-scale transportation problems, health care, revenue management, and energy systems. The design of effective ADP algorithms has many dimensions, but one crucial factor is the stepsize rule used to update a value function approximation. Many operations research applications are computationally intensive, and it is important to obtain good results quickly. Furthermore, the most popular stepsize formulas use tunable parameters and can produce very poor results if tuned improperly. We derive a new stepsize rule that optimizes the prediction error in order to improve the short-term performance of an ADP algorithm. With only one, relatively insensitive tunable parameter, the new rule adapts to the level of noise in the problem and produces faster convergence in numerical experiments.Comment: Matlab files are included with the paper sourc

Loggerhead turtle (Caretta caretta) nest predation at Cape Range National Park

Most of the existing sea turtle populations worldwide are in decline. In particular, loggerhead turtles (Caretta caretta) are listed as endangered and loggerhead nesting populations in Eastern Australia have declined by 86% since the 1970s. However, whilst Eastern Australian loggerhead populations have been extensively studied and monitored, not much is known about the Western Australian nesting population

THE PROGRESSIVEISM OF THE MUCKRAKERS

Photograph of the 2nd floor Ironing Room of Nu-Way Cleaners Dyers Hatters, 811 North Western Avenue, Oklahoma City, OK. Photo by Waterhouse, Oklahoma City, OK, c. 1929-1933

Biomass estimations of invasives Yaupon, Chinese Privet and Chinese Tallow in east Texas Hardwood and Pine Ecosystems

Forest understory fuels can have profound effects on fire behavior and crown fire initiation. Accurate fire behavior prediction in understory fuels is an essential component for estimating fire intensity and severity during wildfire and prescribed fire events. This study focused on estimating temporal and seasonal changes in fuel loading parameters associated with the expansion of invasive yaupon (Ilex vomitoria), Chinese privet (Ligustrum sinense), and Chinese tallow (Triadica sebifera) in East Texas pine and hardwood ecosystems. Fuel loading data of invasive species infested sites indicated significant increases in understory biomass when compared to 1988 estimates, suggesting a clear need to revise regional fuel models. Multiple and simple regression biomass prediction equations were developed for all three-invasive species to facilitate fuel load estimates. These improved prediction equations will enhance fire management efforts as well as invasive species mitigation efforts in east Texas