Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence

Machine learning models may perform differently on different data subgroups, which we represent as itemsets (i.e., conjunctions of simple predicates). The identification of these critical data subgroups plays an important role in many applications, for example model validation and testing, or evaluation of model fairness. Typically, domain expert help is required to identify relevant (or sensitive) subgroups. We propose the notion of divergence over itemsets as a measure of different classification behavior on data subgroups, and the use of frequent pattern mining techniques for their identification. A quantification of the contribution of different attribute values to divergence, based on the mathematical foundations provided by Shapley values, allows us to identify both critical and peculiar behaviors of attributes. Extended experiments show the effectiveness of the approach in identifying critical subgroup behaviors

A Hierarchical Approach to Anomalous Subgroup Discovery

Understanding peculiar and anomalous behavior of machine learning models for specific data subgroups is a fundamental building block of model performance and fairness evaluation. The analysis of these data subgroups can provide useful insights into model inner working and highlight its potentially discriminatory behavior. Current approaches to subgroup exploration ignore the presence of hierarchies in the data, and can only be applied to discretized attributes. The discretization process required for continuous attributes may significantly affect the identification of relevant subgroups. We propose a hierarchical subgroup exploration technique to identify anomalous subgroup behavior at multiple granularity levels, along with a technique for the hierarchical discretization of data attributes. The hierarchical discretization produces, for each continuous attribute, a hierarchy of intervals. The subsequent hierarchical exploration can exploit data hierarchies, selecting for each attribute the optimal granularity to identify subgroups that are both anomalous, and with enough elements to be statistically and practically significant. Compared to nonhierarchical approaches, we show that our hierarchical approach is more powerful in identifying anomalous subgroups and more stable with respect to discretization and exploration parameters

How Divergent Is Your Data?

We present DivExplorer, a tool that enables users to explore datasets and find subgroups of data for which a classifier behaves in an anomalous manner. These subgroups, denoted as divergent subgroups, may exhibit, for example, higher-than-normal false positive or negative rates. DivExplorer can be used to analyze and debug classifiers. If the data has ethical or social implications, DivExplorer can be also used to identify bias in classifiers

KRIT1 loss of function causes a ROS-dependent upregulation of c-Jun

Loss-of-function mutations in the KRIT1 gene (CCM1) have been associated with the pathogenesis of cerebral cavernous malformations (CCM), a major cerebrovascular disease. However, KRIT1 functions and CCM pathogenetic mechanisms remain incompletely understood. Indeed, recent experiments in animal models have clearly demonstrated that the homozygous loss of KRIT1 is not sufficient to induce CCM lesions, suggesting that additional factors are necessary to cause CCM disease. Previously, we found that KRIT1 is involved in the maintenance of the intracellular reactive oxygen species (ROS) homeostasis to prevent ROS-induced cellular dysfunctions, including a reduced ability to maintain a quiescent state. Here, we show that KRIT1 loss of function leads to enhanced expression and phosphorylation of the redox-sensitive transcription factor c-Jun, as well as induction of its downstream target COX-2, in both cellular models and human CCM tissues. Furthermore, we demonstrate that c-Jun upregulation can be reversed by either KRIT1 re-expression or ROS scavenging, whereas KRIT1 overexpression prevents forced upregulation of c-Jun induced by oxidative stimuli. Taken together with the reported role of c-Jun in vascular dysfunctions triggered by oxidative stress, our findings shed new light on the molecular mechanisms underlying KRIT1 function and CCM pathogenesis

Host defense peptides identified in human apolipoprotein B as natural food bio-preservatives: Evaluation of their biosafety and digestibility

The employment of chemical agents in the food industry is raising several concerns by consumers and is leading to an increasing interest in natural food preservatives. Among alternatives, host defense peptides (HDPs) have attracted great interest for their ability to preserve food samples from contamination without altering their quality, taste, and organoleptic properties. Recently, we evaluated the applicability of ApoB-derived peptides as novel food bio-preservatives and demonstrated their ability to prevent chicken meat sample contamination when immobilized on chitosan films. To perform a further step towards the applicability of these peptides in the food field, here we evaluated peptides biosafety and digestibility. To do this, we used a multidisciplinary approach including the evaluation of the peptides' toxicity and antimicrobial activity, the analysis of resistance phenotype development, an in silico prediction of the peptides' susceptibility to proteases and the evaluation of the peptides' stability in simulated gastric and intestinal fluids. ApoB-derived peptides were found to be nontoxic when tested on human gastric carcinoma cells SNU-1 and on human colon-rectal adenocarcinoma cells HT-29, and not to induce resistance phenotype in Salmonella strains. Bioinformatic analyses showed that the peptides are susceptible to several proteases, as also confirmed by experiments in simulated gastric and intestinal fluids. Altogether these findings open interesting perspectives to the future applicability of ApoB-derived peptides as novel food biopreservatives

Exploring Subgroup Performance In End-to-End Speech Models

End-to-End Spoken Language Understanding models are generally evaluated according to their overall accuracy, or separately on (a priori defined) data subgroups of interest. We propose a technique for analyzing model performance at the subgroup level, which considers all subgroups that can be defined via a given set of metadata and are above a specified minimum size. The metadata can represent user characteristics, recording conditions, and speech targets. Our technique is based on advances in model bias analysis, enabling efficient exploration of resulting subgroups. A fine-grained analysis reveals how model performance varies across subgroups, identifying modeling issues or bias towards specific subgroups. We compare the subgroup-level performance of models based on wav2vec 2.0 and HuBERT on the Fluent Speech Commands dataset. The experimental results illustrate how subgroup-level analysis reveals a finer and more complete picture of performance changes when models are replaced, automatically identifying the subgroups that most benefit or fail to benefit from the chang

Deep-frying purple potato Purple Majesty using sunflower oil: effect on the polyphenols, anthocyanins and antioxidant activity

The potato is a root vegetable native to the Americas; it consists of the starchy tuber of the plant Solanum tuberosum. There are many varieties, and the flesh can have different colour ranging from yellow to red and purple. Coloured varieties have a denser texture and slightly nuttier, earthier flavour than other potatoes. The desirable quality characteristics of potatoes depends on the intended use, and the acceptability of raw potatoes is determined by size, shape, colour, and the quality of can be evaluated in terms of colour, flavour, and texture. Deep-frying is the century-old and it is among the most common cooking processes, still being used to prepare a variety of food products on both industrial and domestic scales. Frying the potatoes is among the tastiest and appreciated way to cook this vegetable. Purple fleshed potatoes are widely considered one of the best-tasting purple potatoes varieties, they have a nice taste and add colour to a meal. They are a source of beneficial health compounds which makes them interesting as functional food. The anthocyanins present in the Purple Majesty variety are interesting for their health promoting abilities, anti-oxidative activity, and even other health beneficial effects, e.g. anti-influenza virus activity, and anti-stomach cancer activity. The aim of this study has been to assess the effect of deep-frying of purple potato Purple Majesty using sunflower oil on the polyphenols, anthocyanins and to evaluate the antioxidant activity of the cooked matrix compared to the fresh one. The results seem to suggest that the healthy characteristics of this functional food are retained after the cooking by frying