MORPHOLOGY, MODES OF BEHAVIOR, AND MEASURES OF MARKET POWER: A FRAMEWORK FOR EVALUATING THE IMPACT OF INDUSTRIALIZATION OF SOUTHERN AGRICULTURE ON MARKETS AND MARKET STRUCTURE

Industrial Organization,

RESEARCH IN AGRICULTURAL ECONOMICS: PROGRESS, LIMITATIONS, COORDINATION, NEEDS AND PROSPECTS - A DISCUSSION

Research and Development/Tech Change/Emerging Technologies,

A fast analysis for thread-local garbage collection with dynamic class loading

Long-running, heavily multi-threaded, Java server applications make stringent demands of garbage collector (GC) performance. Synchronisation of all application threads before garbage collection is a significant bottleneck for JVMs that use native threads. We present a new static analysis and a novel GC framework designed to address this issue by allowing independent collection of thread-local heaps. In contrast to previous work, our solution safely classifies objects even in the presence of dynamic class loading, requires neither write-barriers that may do unbounded work, nor synchronisation, nor locks during thread-local collections; our analysis is sufficiently fast to permit its integration into a high-performance, production-quality virtual machine

NSSDC data listing

In a highly summarized way, data available from the National Space Science Data Center (NSSDC) is identified. Most data are offline data sets (on magnetic tape or as film/print products of various sizes) from individual instruments carried on spacecraft; these compose the Satellite Data Listing. Descriptive names, time spans, data form, and quantity of these data sets are identified in the listing, which is sorted alphabetically-first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets held at NSSDC, not associated with individual spaceflight instruments, are identified in separate listings following the Satellite Data Listing. These data sets make up the Supplementary Data Listings and include composite spacecraft data sets, ground-based data, models, and computer routines. The identifiers used in the Supplementary Data Listings were created by NSSDC and are explained in the pages preceding the listings. Data set form codes are listed. NSSDC offers primarily archival, retrieval, replication, and dissemination services associated with the data sets discussed in the two major listings identified above. NSSDC also provides documentation which enables the data recipient to use the data received. NSSDC is working toward expanding presently limited capabilities for data subsetting and for promotion of data files to online residence for user downloading. NSSDC data holdings span the range of scientific disciplines in which NASA is involved, and include astrophysics, lunar and planetary science, solar physics, space plasma physics, and Earth science. In addition to the functions mentioned above, NSSDC offers data via special services and systems in a number of areas, including Astronomical Data Center (ADC), Coordinated Data Analysis Workshops (CDAWs), NASA Climate Data System (NCDS), Pilot Land Data System (PLDS), and Crustal Dynamics Data Information System (CDDIS). Furthermore, NSSDC has a no-password account on its SPAN/Telenet-accessible VAX through which the NASA Master Directory and selected online data bases are accessible and through which any data described here may be ordered. Astrophysics data support by NSSDC is not limited to the ADC. Each of these special services/systems is described briefly

Congressional Vote Options

Among political practitioners, there is conventional wisdom about the outcomes of critical and salient legislative votes. 'This vote,' we hear, ' will either win by a little or lose by a lot.' Real-world examples suggest coalition leaders purchase 'hip-pocket' votes and "if you need me" pledges, which are converted to favorable votes when they will yield a victory. When the outcome is uncertain, such a process -- securing commitments in advance and calling them in if necessary -- is advantageous relative to traditional vote buying. Excess votes are not bought, nor are votes purchased for a losing effort. In effect, the leader secures options on votes. Given uncertainty, buying vote options yields two outcomes in conceivably winnable situations, one a narrow victory, the other a substantial loss. Such a distribution of outcomes is not explicable in a traditional vote-buying framework. We look for evidence of this pattern -- the tracings of 'if you need me pledges' -- by examining all Congressional Quarterly key votes from 1975 through 1998. On these critical and salient votes, narrow victories are much more frequent than narrow losses. Furthermore, when leaders lose key votes, as predicted, they lose by bigger margins than when they win. Finally, we discuss leadership strategies for keeping 'narrow wins' from unraveling into 'big losses.'

Importance Tempering

Simulated tempering (ST) is an established Markov chain Monte Carlo (MCMC) method for sampling from a multimodal density $\pi(\theta)$. Typically, ST involves introducing an auxiliary variable $k$ taking values in a finite subset of $[0,1]$ and indexing a set of tempered distributions, say $\pi_k(\theta) \propto \pi(\theta)^k$. In this case, small values of $k$ encourage better mixing, but samples from $\pi$ are only obtained when the joint chain for $(\theta,k)$ reaches $k=1$. However, the entire chain can be used to estimate expectations under $\pi$ of functions of interest, provided that importance sampling (IS) weights are calculated. Unfortunately this method, which we call importance tempering (IT), can disappoint. This is partly because the most immediately obvious implementation is na\"ive and can lead to high variance estimators. We derive a new optimal method for combining multiple IS estimators and prove that the resulting estimator has a highly desirable property related to the notion of effective sample size. We briefly report on the success of the optimal combination in two modelling scenarios requiring reversible-jump MCMC, where the na\"ive approach fails.Comment: 16 pages, 2 tables, significantly shortened from version 4 in response to referee comments, to appear in Statistics and Computin