Abstract
Zero-shot learning enables the recognition of classes not seen during training through the use of semantic information comprising a visual description of the class either in textual or attribute form. Despite the advances in the performance of zero-shot learning methods, most of the works do not explicitly exploit the correlation between the visual attributes of the image and their corresponding semantic attributes for learning discriminative visual features. In this paper, we introduce an attention-based strategy for deriving features from the image regions regarding the most prominent attributes of the image class. In particular, we train a Convolutional Neural Network (CNN) for image attribute prediction and use a gradient-weighted method for deriving the attention activation maps of the most salient image attributes. These maps are then incorporated into the feature extraction process of Zero-Shot Learning (ZSL) approaches for improving the discriminability of the features produced through the implicit inclusion of semantic information. For experimental validation, the performance of state-of-the-art ZSL methods was determined using features with and without the proposed attention model. Surprisingly, we discover that the proposed strategy degrades the performance of ZSL methods in classical ZSL datasets (AWA2), but it can significantly improve performance when using face datasets. Our experiments show that these results are a consequence of the interpretability of the dataset attributes, suggesting that existing ZSL datasets attributes are, in most cases, difficult to be identifiable in the image. Source code is available at https://github.com/CristianoPatricio/SGAM.

Abstract
Machine Learning has been overtaken by a growing necessity to explain and understand decisions made by trained models as regulation and consumer awareness have increased. Alongside understanding the inner workings of a model comes the task of verifying how adequately we can model a problem with the learned functions. Traditional global assessment functions lack the granularity required to understand local differences in performance in different regions of the feature space, where the model can have problems adapting. Residual Analysis adds a layer of model understanding by interpreting prediction residuals in an exploratory manner. However, this task can be unfeasible for high-dimensionality datasets through hypotheses and visualizations alone. In this work, we use weak interpretable learners to identify regions of high prediction error in the feature space. We achieve this by examining the absolute residuals of predictions made by trained regressors. This methodology retains the interpretability of the identified regions. It allows practitioners to have tools to formulate hypotheses surrounding model failure on particular regions for future model tunning, data collection, or data augmentation on critical cohorts of data. We present a way of including information on different levels of model uncertainty in the feature space through the use of locally fitted Model Agnostic Prediction Intervals (MAPIE) in the identified regions, comparing this approach with other common forms of conformal predictions which do not take into account findings from weak segment identification, by assessing local and global coverage of the prediction intervals. To demonstrate the practical application of our approach, we present a real-world industry use case in the context of inbound retention call-centre operations for a Telecom Provider to determine optimal pairing between a customer and an available assistant through the prediction of contracted revenue.

Abstract

Abstract
In this article, the use of diffuse reflectance (R-d) spectroscopy is explored to evaluate the diffusion properties of water and sucrose in skeletal muscle during optical clearing treatments. Treating muscle samples with sucrose-water solutions with different osmolarities, collimated transmittance (T-c) and R-d measurements were performed to obtain the diffusion time (t) and the diffusion coefficient (D) values that characterize the unique water and sucrose fluxes in the muscle and also the optical clearing mechanisms designated as tissue dehydration and refractive index matching. Considering the R-d measurements, the estimated t and D values for water in the muscle were 63.1s and 1.72x10(-6) cm(2)/s, while the ones estimated for sucrose were 261s and 4.86x10(-7) cm(2)/s. Comparing these values with the ones estimated from the T-c measurements, the relative differences observed for t and D were 1.6% and 2.8% in the case of water and 0.3% and 0.4% in the case of sucrose.

Abstract
A distinctive feature of this review is a critical analysis of methods and results of measurements of the optical properties of tissues in a wide spectral range from deep UV to terahertz waves. Much attention is paid to measurements of the refractive index of biological tissues and liquids, the knowledge of which is necessary for the effective application of many methods of optical imaging and diagnostics. The optical parameters of healthy and pathological tissues are presented, and the reasons for their differences are discussed, which is important for the discrimination of pathologies and the demarcation of their boundaries. When considering the interaction of terahertz radiation with tissues, the concept of an effective medium is discussed, and relaxation models of the effective optical properties of tissues are presented. Attention is drawn to the manifestation of the scattering properties of tissues in the THz range and the problems of measuring the optical properties of tissues in this range are discussed. In conclusion, a method for the dynamic analysis of the optical properties of tissues under optical clearing using an application of immersion agents is presented. The main mechanisms and technologies of optical clearing, as well as examples of the successful application for differentiation of healthy and pathological tissues, are analyzed. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Abstract
[No abstract available]