Reverse-engineering a living creature’s functional connectome is a key challenge in neuroscience. Using K-Formal Concept Analysis, we explore the olfactory perception of Caenorhabditis elegans uncovering robust multimodal relationships between odorants and sensory neurons. Our findings reveal a many-to-many encoding scheme, where neurons respond to multiple odorants, and each odorant activates several neurons—an efficient strategy for sensory encoding in compact connectomes. Robustness tests confirm the stability of our results across dilutions and ablations.Overall, our work highlights the potential of K-Formal Concept Analysis and related techniques to reverse-engineer the sensory perception phase of the sensation-action loop in model organisms, paving the way for a deeper understanding of the energy-efficient intelligence of C. elegans.

Understanding soundscapes is essential for making sense of real-world scenarios in complex environments. However, real-life conditions present significant challenges for AI-based methods, particularly due to the highly imbalanced nature of Audio Tagging problems. In this work, we investigate the impact of data imbalance on the training dynamics of state-of-the-art models for Acoustic Scene Classification. Using the DCASE TAU Urban Acoustic Scenes 2022 dataset and the CP-Mobile model, we introduce controlled imbalance scenarios and analyze their effect through the Entropic Triangle framework. Our findings reveal that the training dynamics are strongly influenced by the chosen balancing approach. It also suggests longer training periods with conventional optimizer for re-balanced classification metrics. In this way, this study provides new insights into the role of entropy-based analysis in developing robust Acoustic Scene Classification systems for real-world applications. © 2025 International Speech Communication Association. All rights reserved.

We introduce a framework for the evaluation of multiclass classifiers by exploring their confusion matrices. Instead of using error-counting measures of performance, we concentrate in quantifying the information transfer from true to estimated labels using information-theoretic measures. First, the Entropy Triangle allows us to visualize the balance of mutual information, variation of information, and the deviation from uniformity in the true and estimated label distributions. Next, the Entropy-Modified Accuracy allows us to rank classifiers by performance while the Normalized Information Transfer rate allows us to evaluate classifiers by the amount of information accrued during learning. Finally, if the question rises to elucidate which errors are systematically committed by the classifier, we use a generalization of Formal Concept Analysis to elicit such knowledge. All such techniques can be applied either to artificially or biologically embodied classifiers-e.g., human performance on perceptual tasks. We instantiate the framework in a number of examples to provide guidelines for the use of these tools in the case of assessing single classifiers or populations of them-whether induced with the same technique or not-either on single tasks or in a set of them. These include well-known UCI tasks and the more complex KDD cup 99 competition on Intrusion Detection.

In this paper we investigate into analogue of a Singular Value Decomposition for rectangular matrices over a complete idempotent semifields (i-SVD), many of whose instances-the complete schedule algebra or completed max-plus semifield, the tropical algebra, and the max-times algebra-are useful in a range of applications. We carry on further the task of eliciting the relation between i-SVD and the extension of Formal Concept Analysis to complete idempotent semifields ($\mathcal{K}$-FCA) started in prior work. We find out that for each relation with entries considered in a complete idempotent semifield, for each of the four types of Galois connections defined by the relation, there are two basis of left- and right-singular vectors to choose from for reconstructing the matrix. These are each of the join-dense or meet-dense sets object or attribute concepts of the concept lattice created by the connection. We suggest why these can never form an orthonormal basis, and refer to prior art describing the spectral lattices of such matrices for meaningful representations in the idempotent settings.

BACKGROUND: Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA). RESULTS: We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA. First, we give a proper theoretical background to bi-clustering using Lattice Theory and provide a set of analysis tools revolving around [Formula: see text]-Formal Concept Analysis ([Formula: see text]-FCA), a lattice-theoretic unsupervised learning technique for real-valued matrices. By using different kinds of cost structures to quantify expression we obtain different sequences of hierarchical bi-clusterings for gene under- and over-expression using thresholds. Consequently, we provide a method with interleaved analysis steps and visualization devices so that the sequences of lattices for a particular experiment summarize the researcher's vision of the data. This also allows us to define measures of persistence and robustness of biclusters to assess them. Second, the resulting biclusters are used to index external omics databases-for instance, Gene Ontology (GO)-thus offering a new way of accessing publicly available resources. This provides different flavors of gene set enrichment against which to assess the biclusters, by obtaining their p-values according to the terminology of those resources. We illustrate the exploration procedure on a real data example confirming results previously published. CONCLUSIONS: The GED analysis problem gets transformed into the exploration of a sequence of lattices enabling the visualization of the hierarchical structure of the biclusters with a certain degree of granularity. The ability of FCA-based bi-clustering methods to index external databases such as GO allows us to obtain a quality measure of the biclusters, to observe the evolution of a gene throughout the different biclusters it appears in, to look for relevant biclusters-by observing their genes and what their persistence is-to infer, for instance, hypotheses on their function.

In this paper, we present advances in the modeling of the masking behavior of the human auditory system (HAS) to enhance the robustness of the feature extraction stage in automatic speech recognition (ASR). The solution adopted is based on a nonlinear filtering of a spectro-temporal representation applied simultaneously to both frequency and time domains - as if it were an image - using mathematical morphology operations. A particularly important component of this architecture is the so-called structuring element (SE) that in the present contribution is designed as a single three-dimensional pattern using physiological facts, in such a way that closely resembles the masking phenomena taking place in the cochlea. A proper choice of spectro-temporal representation lends validity to the model throughout the whole frequency spectrum and intensity spans assuming the variability of the masking properties of the HAS in these two domains. The best results were achieved with the representation introduced as part of the power normalized cepstral coefficients (PNCC) together with a spectral subtraction step. This method has been tested on Aurora 2, Wall Street Journal and ISOLET databases including both classical hidden Markov model (HMM) and hybrid artificial neural networks (ANN)-HMM back-ends. In these, the proposed front-end analysis provides substantial and significant improvements compared to baseline techniques: up to 39.5% relative improvement compared to MFCC, and 18.7% compared to PNCC in the Aurora 2 database.

Proceedings of: 15th Annual Conference of the International Speech Communication Association. Singapore, September 14-18, 2014.

In this article, we introduce a new representation based on lattice theory for lexical data from a lexical-database embodying the frame-semantic approach to language description, FrameNet. We present proof of the abundance of Concept Lattices as proposed in Formal Concept Analysis both in the theory of frames and in its present-day incarnation, the FrameNet resource, by constructing several types of these. We further argue for the adequacy of such lattices in representing linguistic data with contributions that range from data-visualization to the fine-tuning of some frame-theoretical concepts. We argue finally that FrameNet is better thought of as being a lexical resource rather than an ontology, but we make the case throughout the article for Concept Lattices being a linguistically adequate, formally effective intermediate representations from which knowledge representation languages may draw knowledge-rich, linguistic facts from FrameNet at their convenience.

We have extended our previous research on a new approach to automatic speech recognition (ASR) in the GSM environment. Instead of recognizing from the decoded speech signal, our system works from the digital speech representation used by the GSM encoder. We have compared the performance of a conventional system and the one we propose on a speaker independent, isolated-digit ASR task. For the half and full-rate GSM codecs, from our results, we conclude that the proposed approach is much more effective in coping with the coding distortion and transmission errors. Furthermore, in clean speech conditions, our approach does not impoverish the recognition performance, even recognizing from GSM digital speech, in comparison with a conventional system working on unencoded speech.

We present a new language model that includes some of the most promising techniques for overcoming linguistic inadequacy, including POS tagging [3] and refining [4], hierarchical, locally conditioned grammars [5], parallel modelling of acoustic and linguistic domains [6] – and some of our own: language modelling as language parsing, and a better integration of the whole process with the acoustic model resulting in a richer educt from the language modelling process. We are building this model for a translation into Spanish of the DARPA RM task, maintaining the same 1k words vocabulary and some 1000 sentences.