The effects of varying concentration and duration of CFC-113 exposure on end-exhaled breath CFC-113 concentration

Subjects were exposed to different concentrations of CFC-113 for variable duration while conducting 30-minute exercise regiments. End-exhaled breath samples were collected 25, 30 and 35 minutes after the exercise regiment and quantified using gas chromatography. Three comparative studies were conducted.;A short duration administration delivered at the beginning or at the end of the regiment was compared to a long duration administration delivered at the beginning or at the end of the exercise regiment. The second study compared breath samples when 500 ppm of CFC-113 was administered at the beginning or end six minutes of the regiment. The third compared continuous exposures (30 min of 100 ppm) to intermittent exposures (three intermittent 2-min exposures of 500 ppm). This study determined that equivalent doses of CFC-113 administered with variable exposure profiles to human subjects produced statistically equivalent end-exhaled CFC-113 concentration, except in two cases

DALE: Differential Accumulated Local Effects for efficient and accurate global explanations

Accumulated Local Effect (ALE) is a method for accurately estimating feature effects, overcoming fundamental failure modes of previously-existed methods, such as Partial Dependence Plots. However, ALE's approximation, i.e. the method for estimating ALE from the limited samples of the training set, faces two weaknesses. First, it does not scale well in cases where the input has high dimensionality, and, second, it is vulnerable to out-of-distribution (OOD) sampling when the training set is relatively small. In this paper, we propose a novel ALE approximation, called Differential Accumulated Local Effects (DALE), which can be used in cases where the ML model is differentiable and an auto-differentiable framework is accessible. Our proposal has significant computational advantages, making feature effect estimation applicable to high-dimensional Machine Learning scenarios with near-zero computational overhead. Furthermore, DALE does not create artificial points for calculating the feature effect, resolving misleading estimations due to OOD sampling. Finally, we formally prove that, under some hypotheses, DALE is an unbiased estimator of ALE and we present a method for quantifying the standard error of the explanation. Experiments using both synthetic and real datasets demonstrate the value of the proposed approach.Comment: 16 pages, to be published in Asian Conference of Machine Learning (ACML) 202

RHALE: Robust and Heterogeneity-aware Accumulated Local Effects

Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated features well. However, it has two limitations. First, it does not quantify the deviation of instance-level (local) effects from the average (global) effect, known as heterogeneity. Second, for estimating the average effect, it partitions the feature domain into user-defined, fixed-sized bins, where different bin sizes may lead to inconsistent ALE estimations. To address these limitations, we propose Robust and Heterogeneity-aware ALE (RHALE). RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting. In this paper, we prove that to achieve an unbiased approximation of the standard deviation of local effects within each bin, bin splitting must follow a set of sufficient conditions. Based on these conditions, we propose an algorithm that automatically determines the optimal partitioning, balancing the estimation bias and variance. Through evaluations on synthetic and real datasets, we demonstrate the superiority of RHALE compared to other methods, including the advantages of automatic bin splitting, especially in cases with correlated features.Comment: Accepted at ECAI 2023 (European Conference on Artificial Intelligence

Efficient evaluation of generalized path pattern queries on XML data

Finding the occurrences of structural patterns in XML data is a key operation in XML query processing. Existing algorithms for this operation focus almost exclusively on path-patterns or tree-patterns. Requirements in flexible querying of XML data have motivated recently the introduction of query languages that allow a partial specification of path-patterns in a query. In this paper, we focus on the efficient evaluation of partial path queries, a generalization of path pattern queries. Our approach explicitly deals with repeated labels (that is, multiple occurrences of the same label in a query). We show that partial path queries can be represented as rooted dags for which a topological ordering of the nodes exists. We present three algorithms for the efficient evaluation of these queries under the indexed streaming evaluation model. The first one exploits a structural summary of data to generate a set of path-patterns that together are equivalent to a partial path query. To evaluate these path-patterns, we extend PathStack so that it can work on path-patterns with repeated labels. The second one extracts a spanning tree from the query dag, uses a stack-based algorithm to find the matches of the root-to-leaf paths in the tree, and merge-joins the matches to compute the answer. Finally, the third one exploits multiple pointers of stack entries and a topological ordering of the query dag to apply a stack-based holistic technique. An analysis of the algorithms and extensive experimental evaluation shows that the holistic algorithm outperforms the other ones