Polite Combination of Algebraic Datatypes

Journal of Automated Reasoning - Algebraic datatypes, and among them lists and trees, have attracted a lot of interest in automated reasoning and Satisfiability Modulo Theories (SMT). Since its...     

Learning Causes: Psychological Explanations of Causal Explanation1

I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between mechanist and associationist psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, incorrectly let large causes overshadow small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations.     

Refining the level for anticipated hepatotoxicity in acetaminophen poisoning

Treatment of an acetaminophen overdose with N-acetyl cysteine usually is based on the position of the 4-h acetaminophen (APAP) level on the Rumack-Matthew nomogram; however, there is disagreement on the level at which clinically relevant hepatotoxicity occurs. A retrospective review of all acute adult formulation APAP exposures reported to our poison center between 1986 and 1993 was performed and cases corresponding to the "possible risk or toxicity" range on the nomogram were identified. Our current poison center protocol for APAP poisoning does not recommend treatment with N-acetylcysteine (NAC) in low-risk patients if the 4-h serum APAP level or the extrapolated equivalent falls within the possible toxicity range on the nomogram. Seventeen cases met the inclusion criteria for the study and received no NAC; six additional patients met inclusion criteria but received one or two doses of NAC before therapy was discontinued. No patients in either group demonstrated clinical evidence of hepatotoxicity. This pilot study suggests that patients with no risk factors and APAP levels in the "possible risk" range may not require NAC therapy.     

Importance of participation rate in sampling of data in population based studies, with special reference to bone mass in Sweden

OBJECTIVE: To study the effects of participation rate in sampling on "normative" bone mass data. DESIGN: This was a comparison between two randomly selected samples from the same population. The participation rates in the two samples were 61.9% and 83.6%. Measurements were made of bone mass at different skeletal sites and of muscle strength, as well as an assessment of physical activity. SETTING: Malm?, Sweden. SUBJECTS: There were 230 subjects (117 men, 113 women), aged 21 to 42 years. RESULTS: Many subjects participated in both studies (163). Those who took part only in the study with the higher participation rate (67) almost invariably had higher values for bone mass density at the sites measured (up to 7.6% for men) than participants in the study with the lower participation rate. No differences in muscle strength were recorded. CONCLUSION: A high degree of compliance is important to achieve a reliable result in determining normal values in population based studies.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

The Malta cistern mapping project: Underwater robot mapping and localization within ancient tunnel systems

This paper documents the development of an underwater robot system enabled with several mapping and localization techniques applied to a particular archaeological expedition. The goal of the expedition was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of several connected chambers, still containing water. A sonar‐equipped remotely operated vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans, (2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous localization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans, (5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether position data. Top‐down‐view maps of 22 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed before the expedition. © 2010 Wiley Periodicals, Inc.