Virginia\u27s pelagic recreational fishery: Biological, socioeconomic and fishery components

Catch, effort, fleet size and boat owner expenditure data were collected on Virginia\u27s recreational marlin/tuna fishery for the 1983-1985 seasons. Logbooks, dockside interviews and a telephone survey were evaluated to determine which method was the most efficient and effective for collecting and estimating catch and effort for Virginia\u27s pelagic recreational fishery. In 1984, logbooks were used to collect catch and effort data and fishing effort was estimated using Bochenek\u27s method. Very few fishermen returned their logbooks and as a result this data is probably less reliable than the data collected in other years. Due to the poor return of logbooks, this method should not be used to assess Virginia\u27s marlin/tuna fishery. For the 1985 season, Figley\u27s telephone survey (1984) was compared to the NMFS dockside interview technique for large pelagics. Both the telephone survey using Figley\u27s technique (1984) and dockside interviews using Bochenek\u27s method for calculating effort appear to provide similar estimates of projected total catch. However, the dockside method is very labor intensive, costly and fraught with problems in estimating fishing effort. Therefore, the telephone survey technique using Figley\u27s method for estimating effort appears to be a better method for analyzing this fishery. If telephone interviewing will not work in an area and dockside sampling methods must be relied upon to study the pelagic fishery, Bochenek\u27s method appears to produce a better estimate of fishing effort. Using Figley\u27s (1984) mark-recapture technique, Virginia\u27s pelagic recreational fleet was estimated at 455 and 774 vessels in 1983 and 1985, respectively. Boat owner expenditures for this fleet were estimated at &3,863,045 in 1983, \&4,057,020 in 1984 and &5,538,191 in 1985. Bluefin tuna were caught at SST ranging from 58-83 F but seem to prefer SST of 70 to 75 F. Yellowfin tuna were caught at SST ranging from 68-86 F with the majority landed at SST of 76-80 F. White marlin appear to prefer SST of 74 to 81 F

Implicit automata in typed λ\lambda-calculi II: streaming transducers vs categorical semantics

We characterize regular string transductions as programs in a linear $\lambda$-calculus with additives. One direction of this equivalence is proved by encoding copyless streaming string transducers (SSTs), which compute regular functions, into our $\lambda$-calculus. For the converse, we consider a categorical framework for defining automata and transducers over words, which allows us to relate register updates in SSTs to the semantics of the linear $\lambda$-calculus in a suitable monoidal closed category. To illustrate the relevance of monoidal closure to automata theory, we also leverage this notion to give abstract generalizations of the arguments showing that copyless SSTs may be determinized and that the composition of two regular functions may be implemented by a copyless SST. Our main result is then generalized from strings to trees using a similar approach. In doing so, we exhibit a connection between a feature of streaming tree transducers and the multiplicative/additive distinction of linear logic. Keywords: MSO transductions, implicit complexity, Dialectica categories, Church encodingsComment: 105 pages, 24 figure

Secondary use of electronic medical records for early identification of raised condition likelihoods in individuals: a machine learning approach

With many symptoms being common to multiple diseases, there is a challenge in producing an initial diagnosis or recommendation for diagnostic tests from a set of symptoms that could have been produced by a number of diseases. Often the initial choice of diagnosis or testing is based on a clinician’s impression of the likelihood of that condition in a general population; however the opportunity may exist for modification of these likelihoods based on individuals’ recorded medical histories. This data-driven approach utilises existing data and is thus cheap and non-invasive. A method is proposed by which an individual’s likelihoods of having specified medical conditions are modified by the similarity of that individual’s medical history to the medical histories of other individuals, comparing the prevalence of conditions in those other individuals’ records who are similar to the individual of interest versus the prevalence of the conditions in those individuals who are dissimilar. In order to maximise the number of records available for analysis, a process was developed for the merging of data from disparate sources that used different clinical coding systems, including extensive development of a technique for semi automatically mapping clinical events coded in ICD9-CM to Clinical Terms Version 3 (CTV3), for which no existing mapping table was found. Semantically similar fields in the source code sets were identified and retained in the combined data set. ‘Codelists’ comprising multiple CTV3 codes for a variety of conditions were built that defined the presence of those conditions within individual records. The hierarchical structure of the CTV3 code table was utilised as a method of identifying codes that differed in structure but had clinically similar or related meaning. The optimum degree of granularity of the coded data to use in identifying similar records was investigated and used in subsequent analysis. Two methods were used for discovering groups of similar and dissimilar individuals: the ‘nearest neighbours’ method and the grouping of records using a clustering process. Altered likelihoods for a range of conditions were investigated and results for the nearest-neighbours approach compared to the clustering approach. Results for adjusted condition likelihoods for 18 conditions are reported, together with a discussion of possible reasons for a change, or otherwise, in the condition likelihood, and a discussion of the clinical significance and potential use of information about such a change. logistic regressions performed on a selection of conditions KNN performed better than logistic regression when judged by F-score (or sensitivity and specificity separately), however situation more nuanced when looking at likelihood ratios: Logistic regression produced higher (better) positive likelihood ratios, but KNN produced lower (better) negative likelihood ratios. Logistic regression produced higher odds ratios

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book