DEP51 - Artículos de revista

Notion meta-learner: A technique for few-shot learning in music genre recognition

2025-01-01T00:00:00Z

This paper presents the notion of meta-learner (NML), an innovative meta-learning methodology designed to enhance the performance of few-shot learning (FSL) regarding the recognition of music genres. Current FSL techniques frequently encounter difficulties due to the absence of organized representations and low capacity for generalization, which impede their efficacy in practical scenarios. The NML meta-learner overcomes these obstacles by acquiring the ability to learn across notion dimensions that humans can understand, thus improving its capacity for generalization and interpretability. Instead of gaining knowledge in a combined and disorganized metric space, the notion meta-learner acquires knowledge by mapping high-level notions into partially organized metric spaces. This technique allows for the efficient integration of several notion learners. We assessed the performance of NMLFSL by utilizing the GTZAN dataset and comparing employing seven different benchmarks. The experimental outcomes show that the NML performs superior to current FSL approaches in tasks that include recognizing music genres with only one or five examples, thereby demonstrating its potential to improve the current state of the art in this field. In addition, ablation experiments assess the influence of essential variables, offering valuable information about the effectiveness of the suggested method. NMLFSL is a notable advancement in using meta-learning to enhance the reliability and precision of music genre recognition (MGR) systems.

Notion meta-learner: A technique for few-shot learning in music genre recognition

2025-01-01T00:00:00Z

Feature Extraction of Galvanic Skin Responses by Nonnegative Sparse Deconvolution

2018-01-01T00:00:00Z

Wearable sensors are increasingly taking part in daily activities, not only because of the recent society health concern, but also due to their relevance in the medical industry. In this paper, a galvanic skin response (GSR) extraction technique has been developed in order to interpret electrodermal activity (EDA) records, which can be useful both for ambulatory and health applications. The core of the proposed approach is a novel feature extraction scheme that is based on a nonnegative sparse deconvolution of the observed GSR signals. Unlike previous approaches, the resulting SparsEDA algorithm is fast (immediately extracting the skin conductance level and response), efficient (being able to work with any sampling rate and signal length), and highly interpretable (due to the sparsity of the extracted phasic component of the GSR). Results on real data from 100 different subjects confirm the good performance of the method, which has been released through a free web-based code repository.

A hybrid bio-inspired approach for clustering and routing in UWSNs using MPA and HGS

2025-01-01T00:00:00Z

Underwater Wireless Sensor Networks (UWSNs) encounter serious challenges due to dynamic topology, energy constraints, and high latency underwater communication. Existing methods for clustering and routing often fail to strike an optimal balance between data delivery reliability, energy efficiency, and latency reduction. This paper overcomes these shortcomings by developing a hybrid model that integrates the Hunger Games Search (HGS) and Marine Predator Algorithm (MPA) for improved clustering and routing in UWSNs. The MPA was chosen due to its stability in selecting the first sensors/cluster heads and creating the clusters, drawing inspiration from the foraging strategies of marine predators, which guides it extensively in the balance of exploration and exploitation. Simulations demonstrate that the proposed method achieves significantly better results than classical methods. In particular, the HGS-MPA framework consumes 26.6 % less energy than GWO-PSO, increasing network lifetime by 22.1 % (FINOD) and 15.8 % (HANOD). The packet delivery ratio is improved by 3.1 % against the following best-performing method, reaching 92.4 %. A statistical test performed with ANOVA showed that these improvements are statistically significant at P < 0.001. The results reinforce how the HGS-MPA framework would help improve energy efficiency, network lifetime, and communication reliability in UWSN systems.

Enhancing unmanned marine vehicle path planning: A fractal-enhanced chaotic grey wolf and differential evolution approach

2025-01-01T00:00:00Z

Efficient path planning is challenging for optimizing the trajectory of uncrewed marine vehicles navigating complex environments. However, when the global optimum is zero, path planning optimization encounters a significant challenge, a major shortcoming of the grey wolf optimizer (GWO). This study intentionally integrates multiple approaches to present a comprehensive methodology called fractal-enhanced chaotic GWO (FECGWO) in conjunction with differential evolution (DE) to fill this research gap. This method uses DE to strengthen the local search or exploitation phases, chaotic maps to improve the exploration phase, and fractals to fine-tune the transition between the two phases. In addition to testing against 46 sophisticated benchmark maps, this study carries out practical experimentation over commonly utilized meta-heuristic algorithms to comprehensively evaluate the proposed hybrid model's performance (FECGWO-DE). This thorough evaluation demonstrates notable advancements in unmanned marine vehicle path planning. The evaluation criteria include path length, consistency, time complexity, and success rate. These metrics illustrate the statistical significance of the novel methodology's improvements. The study demonstrates that FECGWO can precisely identify the best routes in given test maps, offering insightful information for developing path planning optimization—especially concerning unmanned marine vehicles.

Diagnostic Driven Topology Adaptive Generative Adversarial Networks for Improved Breast Cancer Diagnosis

2025-01-01T00:00:00Z

Breast cancer is one of the major causes of deaths in women. In the meantime, proper and early diagnosis with the help of mammograms can greatly enhance the outcomes of treatment. Nevertheless, the volume of the available data and the inconsistency of lesions are significant obstacles to the development of reliable diagnostic models. Generative Adversarial Networks (GANs) can provide a solution to data augmentation but due to the static nature of their models, they are incapable of capturing diagnostically important features, like irregular mass margins or microcalcifications. The proposed research study proposes a new Diagnostic-Driven Topology-Adaptive GAN (DTA-GAN) framework that improves the performance by adapting the generator and discriminator structures in real-time during the training process based on the diagnosis. DTA-GAN is compared with two baseline models, four state-of-the-art GAN-based models, and five ablation-based DTA-GAN models in 13 metrics on three popular datasets: CBIS-DDSM, INbreast, and Mini-MIAS to perform an extensive assessment. DTA-GANs results remarkably exceed the benchmarks with an AUC of 0.90–0.92 (an increase of 10% over DCGANs 0.82), an FID of 8.40–8.45 (compared to StyleGAN2’s 7.89), and feature preservation LMS metrics of 0.87–0.89 and CDR 91.9–92.7% using both qualitative and quantitative assessment across the three datasets. DTA-GAN synthesizes mammograms with topology controller reward functions focusing on imaging and diagnostics to improve the classification accuracy of the following tasks, offering a breast cancer detection solution that can be scaled to be used widely. This is a breakthrough in medical imaging because it synthesizes data that meets the stringent diagnostic standards, making the systems that are used to make diagnoses more reliable and more generalizable.

Efficient solutions in uncertain multiobjective optimization with countably many scenarios

2026-01-01T00:00:00Z

We use a vector approach to address, from an efficient point of view, an uncertain unconstrained multiobjective optimization problem with countably many scenarios. Specifically, we introduce several efficient solution notions that work not only in the Pareto case, but also when the preferences in the image space depend on the scenario and they are defined by a convex cone in the usual way. We state basic properties of these notions and we relate the involved solution sets with other well-known solution sets of the literature. Particularly, it is shown that the so-called highly solutions are a particular case of efficient solutions. In addition, we obtain characterizations through solutions of associated scalar optimization problems and we derive existence theorems. Finally, two applications are provided to illustrate the main results.

Geometry of spaces of homogeneous trinomials on ℝ2

2021-01-01T00:00:00Z

For each pair of numbers m, n ∈ ℕ with m > n , we consider the norm on ℝ3 given by ‖(a, b, c)‖m,n = sup{|ax^m + bx^(m−n)y^n + cy^m| ∶ x, y ∈ [−1, 1]} for every (a, b, c) ∈ ℝ3 . We investigate some geometrical properties of these norms. We provide an explicit formula for ‖ ⋅ ‖m,n , a full description of the extreme points of the corresponding unit balls and a parametrization and a plot of their unit spheres for certain values of m and n.

On the size of special families of linear operators

2018-01-01T00:00:00Z

We continue the study, started in [12], of the search for algebraic structures one can find within the sets of injective linear functions. We shall focus on the cases when the operators are considered both on finite dimensional and infinite dimensional domains. We also study the set of continuous surjective linear operators.

A population structure-sensitive mathematical model assessing the effects of vaccination during the third surge of COVID-19 in Italy

2022-01-01T00:00:00Z

We provide a non-autonomous mathematical model to describe some of the most relevant parameters associated to the COVID-19 pandemic, such as daily and cumulative deaths, active cases, and cumulative incidence, among others. We will take into consideration the ways in which people from four different age ranges react to the virus. Using an appropriate transmission function, we estimate the impact of the third surge of COVID-19 in Italy. Also, we assess two different vaccination programmes. In one of them, a single shot is administered to all citizens over 16 years old before second shots are available. In the second model, first and second shots are administered to each citizen within, approximately, 20 days of time-gap.

Algebraic genericity and the differentiability of the convolution

2019-01-01T00:00:00Z

Despite the convolution preserving most of the smooth properties of the functions that take part in it, there exist differentiable functions whose convolution is not differentiable. In the present result, we study the algebraic genericity of the set of those functions. In particular, it is proved that periodic continuous functions can be approximated by functions belonging to a vector space each of whose nonzero members generates some convolution which is not differentiable.

The convolution of two differentiable functions on the circle need not be differentiable

2019-01-01T00:00:00Z

The convolution operator is well-known for preserving the best properties of its parent functions, and is often presented as a “smoothing” operator. In the present result, we construct two differentiable functions whose convolution is not differentiable.

A mathematical model to simulate the biological action of Infliximab on TNF-α in patients with Inflammatory Bowel Disease: the critical role of drug clearance

2026-01-01T00:00:00Z

Inflammatory bowel disease (IBD), including Crohn s disease (CD) and ulcerative colitis (UC), is characterized by chronic intestinal inflammation driven by elevated tumor necrosis factor-alpha (TNF-α). Infliximab, an anti-TNF-α monoclonal antibody, is widely used in the treatment of inflammatory bowel disease but shows variable effectiveness due to interindividual pharmacokinetic diversity. We develop a low-dimensional mathematical model of ordinary differential equations to describe TNF-α dynamics, its interactions with receptors and infliximab, and the influence of drug clearance on treatment outcomes in CD and UC. This model is combined with a pharmacokinetic framework that enables the estimation of the infliximab clearance coefficient, which can then be used to guide dosage adjustments in the treatment. The model balances biological realism with analytical tractability, enabling rigorous mathematical analysis and numerical simulations. The parameters are adapted for CD and UC. The study investigates how drug clearance influences treatment efficacy, initially using constant clearance values and later incorporating values that vary with the level of inflammation. Simulations are performed across a range of clearance rates and dosing regimens, providing detailed insights into infliximab and TNF-α dynamics, as well as therapeutic drug monitoring parameters. Our results highlight the critical role of clearance and therapeutic drug monitoring in optimizing infliximab therapy. This approach offers valuable insights to support personalized treatment strategies in IBD.

Cost-benefit analysis of the nesting approach in HARMONIE-AROME for a supercell outbreak case study

2026-01-01T00:00:00Z

Supercells are among the most hazardous convective systems, frequently producing large hail, destructive winds, and severe socio-economic impacts. The enhancement of weather simulations is identified as a primary strategy to optimise short-term forecasting. The present study investigates the performance of two high-resolution con- figurations of the HARMONIE-AROME model during a severe supercell outbreak over eastern Iberia on 31st July 2015, when six confirmed supercells caused significant damage. The setups tested include a two-step one-way nested approach (2.5 km outer domain and 500 m inner domain), and a single-domain configuration at 500 m resolution. The model outputs, which include reflectivity, precipitation and temperature, are validated against OPERA radar composites and surface observations. At the same time, key convective parameters, derived from the Murcia sounding, are analyzed to assess the pre-convective environment. Although the simulations demonstrate a similar structure to the observed event, the two-domain nested simulation offers a slightly su- perior depiction of reflectivity and thermodynamic profiles. Nevertheless, precipitation analysis reveals that while nesting improves moderate rainfall representation, it introduces larger errors for the most extreme amounts, limiting its overall benefit. The obtained gain is not sufficient to offset the 30% higher computational cost when the two-domain nested approach is used. The single-domain non-nested configuration demonstrates a superior level of efficiency, exhibiting equivalent accuracy while exhibiting a diminished resource requirement.

The Convergence Analysis of a Numerical Method for a Structured Consumer-Resource Model with Delay in the Resource Evolution Rate

2020-01-01T00:00:00Z

In this paper, we go through the development of a new numerical method to obtain the solution to a size-structured population model that describes the evolution of a consumer feeding on a dynamical resource that reacts to the environment with a lag-time response. The problem involves the coupling of the partial differential equation that represents the population evolution and an ordinary differential equation with a constant delay that describes the evolution of the resource. The numerical treatment of this problem has not been considered before when a delay is included in the resource evolution rate. We analyzed the numerical scheme and proved a second-order rate of convergence by assuming enough regularity of the solution. We numerically confirmed the theoretical results with an academic test problem.

Computational Study on the Dynamics of a Consumer-Resource Model: The Influence of the Growth Law in the Resource

2021-01-01T00:00:00Z

The dynamics of a specific consumer-resource model for Daphnia magna is studied from a numerical point of view. In this study, Malthusian, chemostatic, and Gompertz growth laws for the evolution of the resource population are considered, and the resulting global dynamics of the model are compared as different parameters involved in the model change. In the case of Gompertz growth law, a new complex dynamic is found as the carrying capacity for the resource population increases. The numerical study is carried out with a second-order scheme that approximates the size-dependent density function for individuals in the consumer population. The numerical method is well adapted to the situation in which the growth rate for the consumer individuals is allowed to change the sign and, therefore, individuals in the consumer population can shrink in size as time evolves. The numerical simulations confirm that the shortage of the resource has, as a biological consequence, the effective shrink in size of individuals of the consumer population. Moreover, the choice of the growth law for the resource population can be selected by how the dynamics of the populations match with the qualitative behaviour of the data.