Compositional Explanation of Types and Algorithmic Debugging of Type Errors

The type systems of most typed functional programming languages are based on the Hindley-Milner type system. A practical problem with these type systems is that it is often hard to understand why a program is not type correct or a function does not have the intended type. We suggest that at the core of this problem is the difficulty of explaining why a given expression has a certain type. The type system is not defined compositionally. We propose to explain types using a variant of the Hindley-Milner type system that defines a compositional type explanation graph of principal typings. We describe how the programmer understands types by interactive navigation through the explanation graph. Furthermore, the explanation graph can be the foundation for algorithmic debugging of type errors, that is, semi-automatic localisation of the source of a type error without even having to understand the type inference steps. We implemented a prototype of a tool to explore the usefulness of the proposed methods

A systematic review of the nature of dispensing errors in hospital pharmacies

Background: Dispensing errors are common in hospital pharmacies. Investigating dispensing errors is important for identifying the factors involved and developing strategies to reduce their occurrence. Objectives: To review published studies exploring the incidence and types of dispensing errors in hospital pharmacies and factors contributing to these errors. Methods: Electronic databases including PubMed, Scopus, Ovid, and Web of Science were searched for articles published between January 2000 and January 2015. Inclusion criteria were: studies published in English, and studies investigating type, incidence and factors contributing to dispensing errors in hospital pharmacies. One researcher searched for all relevant published articles, screened all titles and abstracts, and obtained complete articles. A second researcher assessed the titles, abstracts, and complete articles to verify the reliability of the selected articles. Key findings: Fifteen studies met the inclusion criteria all of which were conducted in just four countries. Reviewing incident reports and direct observation were the main methods used to investigate dispensing errors. Dispensing error rates varied between countries (0.015%–33.5%) depending on the dispensing system, research method, and classification of dispensing error types. The most frequent dispensing errors reported were dispensing the wrong medicine, dispensing the wrong drug strength, and dispensing the wrong dosage form. The most common factors associated with dispensing errors were: high workload, low staffing, mix-up of look-alike/sound-alike drugs, lack of knowledge/experience, distractions/interruptions, and communication problems within the dispensary team. Conclusion: Studies relating to dispensing errors in hospital pharmacies are few in number and have been conducted in just four countries. The majority of these studies focused on the investigation of dispensing error types with no mention of contributing factors or strategies for reducing dispensing errors. Others studies are thus needed to investigate dispensing errors in hospital pharmacies, and a combined approach is recommended to investigate contributing factors associated with dispensing errors and explore strategies for reducing these errors.Peer reviewe

Modelling children's negation errors using probabilistic learning in MOSAIC.

Cognitive models of language development have often been used to simulate the pattern of errors in children’s speech. One relatively infrequent error in English involves placing inflection to the right of a negative, rather than to the left. The pattern of negation errors in English is explained by Harris & Wexler (1996) in terms of very early knowledge of inflection on the part of the child. We present data from three children which demonstrates that although negation errors are rare, error types predicted not to occur by Harris & Wexler do occur, as well as error types that are predicted to occur. Data from MOSAIC, a model of language acquisition, is also presented. MOSAIC is able to simulate the pattern of negation errors in children’s speech. The phenomenon is modelled more accurately when a probabilistic learning algorithm is used

AN ERROR ANALYSIS ON SENTENCE TYPES BASED ON STRUCTURE IN ATMARITA’S SHORT STORY“ WHEN BIRUNI REMEMBERS IT”

Errors may occur in different components of language, such as grammar, pronunciation, and vocabulary. Errors can occur because people have not internalized the grammar of the second language. This study aimed to get the answer of research problems, namely (1) What kinds of sentence types that are used in short story in “Reform” magazine No (19,2001)?, (2) What kinds of errors in sentence types that are found in the object of the study? and (3) What is the most dominant error that are found in this investigation?. This thesis used descriptive research design. Besides, the thesis writer used a documentary analysis and selected one short story “When Biruni Remembers It” in “Reform” magazine No (19,2001) as the object of the study. In analyzing the data, the writer investigated the kinds of sentence types based on structure and the kinds of errors in sentence types that are found in the object of the study by taking some steps: (1) Classifying the kinds of sentence types based on structure that are used in short story in “Reform” magazine No (19, 2001), (2) Classifying the kinds of errors in sentence types that are found in short story, (3) Describing the kinds of sentence types based on structure that are found in short story, (4) Describing the kinds of errors in sentence types based on their classification, and (5) Describing the most dominant error in sentence types that is found in short story. From the research findings and discussions, it can be concluded that all the types of sentence based on structure are used in the short story. They are: two simple sentences, nine compound sentences, seven complex sentences, and third teen compound complex sentences. All of them are 31 sentences. In addition, in terms of errors, Oshima’s theory was used in this investigation, that is errors of sentence fragments, errors of choppy sentences, and errors of stringy sentences. Based on the result of the data analysis, it is found out that there are two errors of choppy sentences. However, there is no error of sentence fragments or error of stringy sentences. Therefore, it is concluded that the most dominant error is error of choppy sentence

New in-sample prediction errors in time series with applications

^aThis article introduces two new types of prediction errors in time series: the filtered prediction errors and the deletion prediction errors. These two prediction errors are obtained in the same sample used for estimation, but in such a way that they share some common properties with out of sample prediction errors. It is proved that the filtered prediction errors are uncorrelated, up to terms of magnitude order O(T^-2), with the in sample innovations, a property that share with the out-of-sample prediction errors. On the other hand, deletion prediction errors assume that the values to be predicted are unobserved, a property that they also share with out-of-sample prediction errors. It is shown that these prediction errors can be computed with parameters estimated by assuming innovative or additive outliers, respectively, at the points to be predicted. Then the prediction errors are obtained by running the procedure for all the points in the sample of data. Two applications of these new prediction errors are presented. The first is the estimation and comparison of the prediction mean squared errors of competing predictors. The second is the determination of the order of an ARMA model. In the two applications the proposed filtered prediction errors have some advantages over alternative existing methods.

Are 'unbiased' forecasts really unbiased? Another look at the Fed forecasts

This paper reconciles contradictory findings obtained from forecast evaluations: the existence of systematic errors and the failure to reject rationality in the presence of such errors. Systematic errors in one economic state may offset the opposite types of errors in the other state such that the null of rationality is not rejected. A modified test applied to the Fed forecasts shows that the forecasts were ex post biased.Greenbook Forecasts, forecast evaluation, systematic errors