Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), among the most prevalent neurodegenerative disorders, disrupt brain activity and connectivity, highlighting the need for tools that can effectively capture these alterations. Effective Connectivity Networks (ECNs), which model causal interactions between brain regions, offer a promising approach to characterizing AD and FTD related neural changes. In this study, we estimate ECNs from EEG traces using a state-of-the-art causal discovery method specifically designed for time-series data, to recover the causal structure of the interactions between brain areas. The recovered ECNs are integrated into a novel Graph Neural Network architecture (ECoGNet), where nodes represent brain regions and edge features encode causal relationships. Our method combines ECNs with features summarizing local brain dynamics to improve AD and FTD detection. Evaluated on a publicly available EEG dataset, the proposed approach demonstrates superior performance compared to models that either use non-causal connectivity networks or omit connectivity information entirely.

This study examines seniors’ creative engagement in group activities using synchronous communication tools and explores automatic assessment methods through behavioral and psychophysiological measurements. Working with a small senior group on collaborative creative tasks, we implemented a comprehensive data collection approach using audio-visual and physiological measurements. Machine learning models were used to evaluate group creative engagement levels using various data subsets. Results show that engagement assessment can be effective with different feature combinations, allowing flexibility across contexts and constraints. The multimodal approach, combining facial, audio, and body analysis, achieved optimal performance and is recommended when conditions permit. Our research provides insights into seniors’ online creative participation and presents an automated system for detecting creative engagement in virtual teams, supporting active participation strategies.

This study investigates the potential of Graph Neural Networks (GNNs) for analyzing 3D facial morphology, leveraging facial landmarks as graph nodes to capture the intrinsic structure of 3D face scans. This research evaluates the effectiveness of three distinct approaches for defining graph vertices by associating them with: (1) a well-established set of anthropometric landmarks identified through tactile assessment, widely considered the gold standard in facial anthropometry; (2) automatically detected 3D facial keypoints estimated using advanced algorithms; and (3) geometry-based random point cloud sub-sampling via farthest point sampling (FPS). To evaluate the effectiveness of GNNs and facial landmarks in capturing and representing meaningful morphological patterns, the study employs two benchmark tasks: gender classification and age regression. Extensive experiments across various GNN architectures and three datasets — each presenting diverse and challenging conditions — demonstrate that semantically meaningful landmarks, whether anthropometric or automatically detected, consistently outperform non-semantic random samples in both tasks and across all datasets. These results highlight the crucial role of semantic contextualization in graph-based facial analysis. Notably, models utilizing automatically detected facial keypoints achieved performance comparable to those based on manually annotated anthropometric landmarks, offering a scalable and cost-effective alternative without compromising accuracy. These findings support the integration of automated GNN-based methodologies into a wide range of applications, including clinical diagnosis, forensic analysis, and biometric recognition. • Learning 3D facial features to classify gender and age. • Comparing anthropometric vs. automatic landmarks and FPS sub-sampling. • Characterizing graph nodes with local and global geometric features. • Comparing GCN, GAT, DynamicEdgeConv, and Transformer models.

We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The quantitative evaluation is applied to four open-source tools, producing a baseline for future de-identification methods.

BACKGROUND: One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. RESULTS: We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks -e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. CONCLUSIONS: UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.

BACKGROUND: Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks. RESULTS: We propose a novel semi-supervised parallel enhancement of COSNET, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. By adopting an efficient representation of the graph and assuming a sparse network topology, we empirically show that it can be efficiently applied to networks with millions of nodes. The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting the power of GPU accelerators. This parallel technique ensures the convergence to asymptotically stable attractors, while preserving the asynchronous dynamics of the original model. Detailed experiments on real data and artificial big instances of the problem highlight scalability and efficiency of the proposed method. CONCLUSIONS: By parallelizing COSNET we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes.

For decades, face recognition (FR) has attracted a lot of attention, and several systems have been successfully developed to solve this problem. However, the issue deserves further research effort so as to reduce the still existing gap between the computer and human ability in solving it. Among the others, one of the human skills concerns his ability in naturally conferring a “degree of reliability” to the face identification he carried out. We believe that providing a FR system with this feature would be of great help in real application contexts, making more flexible and treatable the identification process. In this spirit, we propose a completely automatic FR system robust to possible adverse illuminations and facial expression variations that provides together with the identity the corresponding degree of reliability. The method promotes sparse coding of multi-feature representations with LDA projections for dimensionality reduction, and uses a multistage classifier. The method has been evaluated in the challenging condition of having few (3–5) images per subject in the gallery. Extended experiments on several challenging databases (frontal faces of Extended YaleB, BANCA, FRGC v2.0, and frontal faces of Multi-PIE) show that our method outperforms several state-of-the-art sparse coding FR systems, thus demonstrating its effectiveness and generalizability.

In this paper we present a new local-based face recognition system that combines weak classifiers to create a robust system able to recognize faces in presence of either occlusions or large expression variations. The method relies on sparse approximation using dictionaries built on local features. Experiments on the AR database show the effectiveness of our method, which achieves better performance than those obtained by the state-of-the-art ℓ1norm-based sparse representation classifier (SRC).

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph theory.

This article presents a distributed version of the adjSolver algorithm for computing the answer sets of logic programs. adjSolver operates a classical branch-and-bound structure; its intrinsic parallelism is exploited to control, with a centralized architecture, the delegation of promising search subspaces to distributed handling agents. adjSolver has been implemented and tested on a Beowulf platform, using MPI message passing. The communication overhead was minimized by adopting a compact representation of the data exchanged among agents and by reusing previously-computed partial solutions.

We propose a synthetic trace generator that, although based on a simple mobility model, generates traces with statistical properties (like inter-contact time and contact duration) resembling those of well-known real traces.The proposed model is based on a waypoint scheme, with some modifications: the introduction of two categories of nodes, steady and nomadic, with different mobility rates; the grouping of nodes in communities sharing the same location preferences, and the definition of a micro-mobility model inside each location.The statistical properties of the traces generated with this model are compared to those obtained from publicly available real traces.

In this paper, a genetic model based on the operations of recombination and mutation is studied and applied to combinatorial optimization problems. Results are: 1. The equations of the deterministic dynamics in the thermodynamic limit (infinite populations) are derived and, for a sufficiently small mutation rate, the attractors are characterized; 2. A general approximation algorithm for combinatorial optimization problems is designed. The algorithm is applied to the Max Ek-Sat problem, and the quality of the solution is analyzed. It is proved to be optimal for k > or = 3 with respect to the worst case analysis; for Max E3-Sat the average case performances are experimentally compared with other optimization techniques.