Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry.

Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations using 2 years of data for fishers (Pekania pennanti) as a case study. We evaluated (1) camera trap efficacy by estimating how camera detection probabilities are related to nearby telemetry relocations and (2) whether home range utilization density estimated from telemetry data adequately predicts camera detection probabilities, which would indicate consistency of the two data types. The number of telemetry relocations within 250 and 500 m from camera traps predicted detection probability well. For the same number of relocations, females were more likely to be detected during the first year. During the second year, all fishers were more likely to be detected during the fall/winter season. Models predicting camera detection probability and photo counts solely from telemetry utilization density had the best or nearly best Akaike Information Criterion (AIC), suggesting that telemetry and camera traps provide consistent information on space use. Given the same utilization density, males were more likely to be photo-captured due to larger home ranges and higher movement rates. Although methods that combine data types (spatially explicit capture-recapture) make simple assumptions about home range shapes, it is reasonable to conclude that in our case, camera trap data do reflect space use in a manner consistent with telemetry data. However, differences between the 2 years of data suggest that camera efficacy is not fully consistent across ecological conditions and make the case for integrating other sources of space-use data

Cohesive Devices Used in the Headline News of the Jakarta Post

The study deals with the types of cohesive devices used in Headline News of The Jakarta Post. The objectives of study are to identify and to derive the types of cohesive devices dominantly used in headline news of the Jakarta Post. The data are taken from headline column of The Jakarta Post from online edition. There are seven articles which are analyzed in this study. This research is conducted by using descriptive method. The findings show that there are 162 references, 4 substitutions, 34 Ellipsis, 349 conjunctions, and 36 lexical cohesion occur in this study. The most dominant type of cohesive devices is conjunction because the writer would like to connect ideas within the text to make its readers easy to understand. Then the writer would like to make their news more informative. Keyword : cohesion, cohesive device, The Jakarta Post

Unlocking the Treasure Chest of Labor Market Information: Crucial Information for Job Seekers, Educators, and Employers in a Tough Economy

Quality labor market data and analysis is critical to developing effective market-driven workforce and economic strategies in states, regions, and localities. Such information can be complex, intimidating, and overwhelming to many users, however. This issue brief offers a framework for understanding workforce information, including a summary of the different types of consumers of information and their need for comprehensive data and analysis about the labor market. It identifies the publicly available information sources that produce the data and provides suggestions on how to identify and address the gaps between user needs and the availability of reliable and timely data and analytical capacity to enable effective and informed decision-making by data consumers. Finally, it recommends ways in which states and local areas can make workforce information more readily available to consumers who must make important decisions

Inductive-data-type Systems

In a previous work ("Abstract Data Type Systems", TCS 173(2), 1997), the last two authors presented a combined language made of a (strongly normalizing) algebraic rewrite system and a typed lambda-calculus enriched by pattern-matching definitions following a certain format, called the "General Schema", which generalizes the usual recursor definitions for natural numbers and similar "basic inductive types". This combined language was shown to be strongly normalizing. The purpose of this paper is to reformulate and extend the General Schema in order to make it easily extensible, to capture a more general class of inductive types, called "strictly positive", and to ease the strong normalization proof of the resulting system. This result provides a computation model for the combination of an algebraic specification language based on abstract data types and of a strongly typed functional language with strictly positive inductive types.Comment: Theoretical Computer Science (2002

Time and Financial Transfers Within and Beyond the Family: Results From the Health and Retirement Study

Research on time and financial transfers is often conducted along two distinct lines—transfers within the family and transfers beyond the family—without considering the fact that the two types of transfers are actually interrelated. Using longitudinal data from the Health and Retirement Study (HRS), this article investigates the links between the time and financial transfers within and beyond the family. The concepts of within and beyond the family transfers are discussed. Several data quality problems with the transfer measures in the HRS are corrected. Focusing on the interrelationships among the four types of transfers, the study finds that the transfers within and beyond the family are complements in the sense that households that are more willing to make within-family transfers are also more willing to make beyond-family transfers, and vice versa. Income and wealth are strong predictors of financial transfers. Black and Hispanic households lag systematically in the generosity to help the people both within and beyond their families.time and financial transfers; transfers within the family; transfers beyond the family; philanthropy and volunteerism; HRS

Who Said What: Modeling Individual Labelers Improves Classification

Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label or to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computer-aided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels for training.Comment: AAAI 201