Elastic Decision Transformers (EDTs) augmented with intrinsic motivation exhibit improved performance in offline reinforcement learning, yet the cognitive processes driving these gains remain unclear.We present a systematic post-hoc explainability framework that examines how intrinsic motivation influences learned embeddings through statistical characterization of covariance structure, vector magnitudes, and orthogonality.Our findings show that distinct intrinsic-motivation variants induce qualitatively different representational organizations: EDT-SIL (state-based) produces significantly more compact embedding spaces than baseline EDT, whereas EDT-TIL (transformer output-based) increases representational orthogonality.We identify environment-dependent correlations between embedding metrics and performance across locomotion domains.The results indicate that intrinsic motivation acts as a representational prior that shapes embedding geometry in cognitively meaningful ways, yielding environment-specific structures that support improved decision-making beyond simple exploration bonuses.

Multiple theories exist for the role of feedback connections in the brain and in the artificial neural networks, but remain untested using modern tools.In this work, we undertake this task by exploring the utility of explainability methods like GradCAMs [1] in investigating bio-inspired recurrent networks-provided with the predify[2] package-that perform hierarchical updates inspired by the predictive coding theory in neuroscience.We report an extensive search with different levels of feedforward and feedback information.Our preliminary results show that the dynamics are able to recover the GradCAMs on noisy images, providing promising avenues for future work aiming to understand the role of recurrence.

Effective decision-making is a key challenge in artificial intelligence, with Reinforcement Learning (RL) emerging as one of the main approaches.However, RL often depends on complex reward functions, which are difficult to design.Intrinsic motivation, inspired by psychological concepts like curiosity, offers an alternative by generating agent-driven rewards to foster exploration.This paper introduces intrinsic motivation into the Elastic Decision Transformer (EDT) framework for Offline RL.By using an auxiliary intrinsic loss, we enhance representation learning without altering fixed reward signals.Experiments in locomotion tasks demonstrate improved performance, underscoring the potential of intrinsic motivation to advance RL in offline settings.

Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model’s loss function. To this end, we introduce Logic Tensor Networks (LTNs) to encode medical background knowledge using first-order logic (FOL) rules. The encoded rules span from constraints on the shape of the produced segmentation, to relationships between different segmented areas. We apply LTNs in an end-to-end framework with a SwinUNETR for semantic segmentation. We evaluate our method on the task of segmenting the hippocampus in brain MRI scans. Our experiments show that LTNs improve the baseline segmentation performance, especially when training data is scarce. Despite being in its preliminary stages, we argue that neurosymbolic methods are general enough to be adapted and applied to other medical semantic segmentation tasks.

Image Visual Sentiment Analysis (VSA) requires the availability of large annotated datasets, whose construction presents many challenges. The necessity of gathering a large amount of labeled images contrasts with the rigorous, but lengthy, process required for manual annotation based on psychovisual experiments, and with the automatic gathering of large amounts of data roughly labeled based on the sentiment analysis of the text accompanying the images, like captions, tweets and tags. An additional limitation is the scarcity of high-quality datasets with a neutral class, which forces the images to be classified into emotions even when the observers show no emotional activation. In this work, we present a scalable methodology rooted in semiotics and art theory for the construction of a 3-class (positive, negative and neutral) VSA dataset, enabling the downloading of a desired quantity of images while maintaining labeling coherence and accuracy. Based on the proposed methodology, we introduce and make publicly available a VSA dataset of over 100,000 images. To validate the quality of the dataset, we used it to train several classifiers and compared their performance with those of classifiers trained on other datasets. The results, we got, show that the classifiers trained on the new dataset provide better performance when tested on independent datasets, including those commonly used for psycho-visual experiments.

This work describes the ongoing design and development of the METRIQA platform, hosting the Italian agrifood data space.Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities.We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy.To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.

The importance of kidney biopsy, a medical procedure in which a small tissue sample is extracted from the kidney for examination, is increasing due to the rising incidence of kidney disorders. This procedure helps diagnosing several kidney diseases which are cause of kidney function changes, as well as guiding treatment decisions, and evaluating the suitability of potential donor kidneys for transplantation. In this work, a deep learning system for the automatic segmentation of glomeruli in biopsy kidney images is presented. A novel cross–species transfer learning approach, in which a semantic segmentation network is trained on mouse kidney tissue images and then fine–tuned on human data, is proposed to boost the segmentation performance. The experiments conducted using two deep semantic segmentation networks, MobileNet and SegNeXt, demonstrated the effectiveness of the cross–species pre–training approach leading to an increased generalization ability of both models.

Abstract In today’s business landscape, Chatbots play a pivotal role in innovation and process optimization. In this paper, we introduced a novel advanced Emotional Chatbot AI, introducing sentiment analysis for human chatbot conversations. Adding an emotional component within the human-computer interaction, can in fact dramatically improve the quality of the final conversation between Chatbots and humans. More specifically, in our paper, we provided a practical evaluation of the EmoROBERTA software, introducing it into a novel implementation of an Emotional Chatbot. The pipeline we present is novel, and we developed it within a business context in which the use of sentimental and emotional responses can act in a significant and fundamental way toward the final success and use of the Chatbot itself. The architecture enriches user experience with real-time updates on the topic of interest, maintaining a user-centric design, toward an affective-response enhancement of the interaction established between the Chatbot and the user. The source code is fully available on GitHub: https://github.com/filippoflorindi/F-One .

Image geolocalization is receiving increasing attention due to its importance in several applications, such as image retrieval, criminal investigations and fact-checking. Previous works focused on several instances of image geolocalization including place recognition, GPS coordinates estimation and country recognition. In this paper, we tackle an even more challenging problem, which is recognizing the city where an image has been taken. Due to the vast number of cities in the world, we cast the problem as a verification problem, whereby the system has to decide whether a certain image has been taken in a given city or not. In particular, we present a system that given a query image and a small set of images taken in a target city, decides if the query image has been shot in the target city or not. To allow the system to handle the case of images, taken in cities that have not been used during training, we use a Siamese network based on Vision Transformer as a backbone. The experiments we run prove the validity of the proposed system which outperforms solutions based on state-of-the-art techniques, even in the challenging case of images shot in different cities of the same country.

Recognizing the country where a picture has been taken has many potential applications, such as identification of fake news and prevention of disinformation campaigns. Previous works focused on the estimation of the geo-coordinates where a picture has been taken. Yet, recognizing in which country an image was taken could be more critical, from a semantic and forensic point of view, than estimating its spatial coordinates. In the above framework, this paper provides two contributions. First, we introduce the VIPPGeo dataset, containing 3.8 million geo-tagged images. Secondly, we used the dataset to train a model casting the country recognition problem as a classification problem. The experiments show that our model provides better results than the current state of the art. Notably, we found that asking the network to identify the country provides better results than estimating the geo-coordinates and then tracing them back to the country where the picture was taken.

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side-effects are triggered by complex biological processes involving many different entities, from drug structures to protein-protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph-structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side-effects.

Owing to their realistic features and continuous improvements, images manipulated by Generative Adversarial Network (GAN) have become a compelling research topic.In this paper, we apply detection and localization to GAN manipulated images by means of models, based on EfficientNet-B4 architectures.Detection is tested on multiple generated multi-spectral datasets from several world regions and different GAN architectures, whereas localization is tested on an inpainted images dataset of sizes 2048×2048×13.The results obtained for both detection and localization are shown to be promising.

Recently, deep learning models have had a huge impact on computer vision applications, in particular in semantic segmentation, in which many challenges are open.As an example, the lack of large annotated datasets implies the need for new semi-supervised and unsupervised techniques.This problem is particularly relevant in the medical field due to privacy issues and high costs of image tagging by medical experts.The aim of this tutorial overview paper is to provide a short overview of the recent results and advances regarding deep learning applications in computer vision particularly for what concerns semantic segmentation.

Deep learning (DL) is widely applied in biomedical image processing nowadays.In this paper, we propose the use of DL architectures for glomerulus segmentation in histopathological images of mouse kidneys.Indeed, in humans, the analysis of the glomeruli is fundamental to decide on the transplantability of the organ.However, no datasets with human samples are publicly available.Therefore, obtaining good segmentation performance on the kidneys of mice could be the first step for a transfer learning approach to humans.We compared the use of two well-known architectures for image segmentation, namely MobileNet and DeepLab V2.Both models showed very promising results.

Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents a common complication in ultra-rare disorders like AKU. This is the reason why we developed a comprehensive tool, called ApreciseKUre, able to collect AKU patients deriving data, to analyse the complex network among genotypic and phenotypic information and to get new insight in such multi-systemic disease. By taking advantage of the dataset, containing the highest number of AKU patient ever considered, it is possible to apply more sophisticated computational methods (such as machine learning) to achieve a first AKU patient stratification based on phenotypic and genotypic data in a typical precision medicine perspective. Thanks to our sufficiently populated and organized dataset, it is possible, for the first time, to extensively explore the phenotype-genotype relationships unknown so far. This proof of principle study for rare diseases confirms the importance of a dedicated database, allowing data management and analysis and can be used to tailor treatments for every patient in a more effective way.