A NOTE ON THE GRAMMATICAL INFERENCE PROBLEM FOR EVEN LINEAR LANGUAGES

The effect of different task settings within an industry on auditor behavior is examined for the going-concern task. Using an interactive computer process-tracing method, experienced auditors from four Big 6 accounting firms examined cases based on real data that differed on two dimensions of task settings: stage of organizational development (start-up and mature) and financial health (bankrupt and nonbankrupt). Auditors made judgments about each entity's ability to continue as a going concern and, if they had substantial doubt about continued existence, they listed evidence they would seek as mitigating factors. There are seven principal results. First, information acquisition and, by inference, problem representations were sensitive to differences in task settings. Second, financial mitigating factors dominated nonfinancial mitigating factors in both start-up and mature settings. Third, auditors' behavior reflected configural processing. Fourth, categorizing information into financial and nonfinancial dimensions was critical to understanding how auditors' information acquisition and, by inference, problem representations differed across settings. Fifth, Type I errors (determining that a healthy company is a going-concern problem) differed from correct judgments in terms of information acquisition, although Type II errors (determining that a problem company is viable) did not. This may indicate that Type II errors are primarily due to deficiencies in other stages of processing, such as evaluation. Sixth, auditors who were more accurate tended to follow flexible strategies for financial information acquisition. Finally, accurate performance in the going-concern task was found to be related to acquiring (1) fewer information cues, (2) proportionately more liquidity information and (3) nonfinancial information earlier in the process.

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Type Inference: Some Results, Some Problems1

Fundamenta Informaticae ◽

10.3233/fi-1993-191-205 ◽

1993 ◽

Vol 19 (1-2) ◽

pp. 87-125

Author(s):

Paola Giannini ◽

Furio Honsell ◽

Simona Ronchi Della Rocca

Keyword(s):

Large Class ◽

Higher Order ◽

Type Inference ◽

Principal Type ◽

Open Problems ◽

Inference Problem ◽

Type Assignment ◽

Unification Problem

In this paper we investigate the type inference problem for a large class of type assignment systems for the λ-calculus. This is the problem of determining if a term has a type in a given system. We discuss, in particular, a collection of type assignment systems which correspond to the typed systems of Barendregt’s “cube”. Type dependencies being shown redundant, we focus on the strongest of all, Fω, the type assignment version of the system Fω of Girard. In order to manipulate uniformly type inferences we give a syntax directed presentation of Fω and introduce the notions of scheme and of principal type scheme. Making essential use of them, we succeed in reducing the type inference problem for Fω to a restriction of the higher order semi-unification problem and in showing that the conditional type inference problem for Fω is undecidable. Throughout the paper we call attention to open problems and formulate some conjectures.

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ModularBoost: an efficient network inference algorithm based on module decomposition

BMC Bioinformatics ◽

10.1186/s12859-021-04074-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xinyu Li ◽

Wei Zhang ◽

Jianming Zhang ◽

Guang Li

Keyword(s):

Network Inference ◽

Detection Methods ◽

Inference Problem ◽

Topological Constraints ◽

Inference Algorithms ◽

Module Detection ◽

Series Expression ◽

Gene Modules ◽

Inference Methods ◽

Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

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Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

Political Analysis ◽

10.1093/pan/mpw001 ◽

2016 ◽

Vol 24 (2) ◽

pp. 263-272 ◽

Cited By ~ 29

Author(s):

Kosuke Imai ◽

Kabir Khanna

Keyword(s):

Mean Squared Error ◽

False Positive Rate ◽

True Positive Rate ◽

Voter Registration ◽

Racial Groups ◽

Ecological Inference ◽

Inference Problem ◽

Individual Level ◽

Positive Rate ◽

Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

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Approximate Bayesian Computation for Discrete Spaces

Entropy ◽

10.3390/e23030312 ◽

2021 ◽

Vol 23 (3) ◽

pp. 312

Author(s):

Ilze A. Auzina ◽

Jakub M. Tomczak

Keyword(s):

Approximate Bayesian Computation ◽

Likelihood Function ◽

Real Life ◽

Random Variables ◽

Bayesian Computation ◽

Inference Problem ◽

Markov Kernel ◽

Inference Problems ◽

Binary Neural Network ◽

Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

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SiFi

ACM Transactions on Internet of Things ◽

10.1145/3450567 ◽

2021 ◽

Vol 2 (3) ◽

pp. 1-21

Author(s):

Deke Guo ◽

Xiaoqiang Teng ◽

Yulan Guo ◽

Xiaolei Zhou ◽

Zhong Liu

Keyword(s):

Rapid Development ◽

Estimation Method ◽

Likelihood Estimation ◽

Location Based Services ◽

Shopping Mall ◽

Inference Problem ◽

Indoor Location ◽

Label Semantics ◽

Geometrical Constraints ◽

Indoor Scenes

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

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