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This paper addresses the challenge of integrating low-resource languages into multilingual automatic speech recognition (ASR) systems. We introduce a novel application of weighted cross-entropy, typically used for unbalanced datasets, to facilitate the integration of low-resource languages into pre-trained multilingual ASR models within the context of continual multilingual learning. We fine-tune the Whisper multilingual ASR model on five high-resource languages and one low-resource language, employing language-weighted dynamic cross-entropy and data augmentation. The results show a remarkable 6.69% word error rate (WER) reduction for the low-resource language compared to the fine-tuned model without applying our approach, and a 48.86% WER reduction compared to the original Whisper model. In addition, our approach yields an average WER reduction of 3.29% across the six languages, showing no degradation for the high-resource languages., Comment: 5 pages, 1 figure. Presented at Interspeech 2024

As solar power continues to grow and replace traditional energy sources, the need for reliable forecasting models becomes increasingly important to ensure the stability and efficiency of the grid. However, the management of these models still needs to be improved, and new tools and technologies are required to handle the deployment and control of solar facilities. This work introduces a novel framework named Cloud-based Analysis and Integration for Data Efficiency (CAIDE), designed for real-time monitoring, management, and forecasting of solar irradiance sensor farms. CAIDE is designed to manage multiple sensor farms simultaneously while improving predictive models in real-time using well-grounded Modeling and Simulation (M&S) methodologies. The framework leverages Model Based Systems Engineering (MBSE) and an Internet of Things (IoT) infrastructure to support the deployment and analysis of solar plants in dynamic environments. The system can adapt and re-train the model when given incorrect results, ensuring that forecasts remain accurate and up-to-date. Furthermore, CAIDE can be executed in sequential, parallel, and distributed architectures, assuring scalability. The effectiveness of CAIDE is demonstrated in a complex scenario composed of several solar irradiance sensor farms connected to a centralized management system. Our results show that CAIDE is scalable and effective in managing and forecasting solar power production while improving the accuracy of predictive models in real time. The framework has important implications for the deployment of solar plants and the future of renewable energy sources.

Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, nearfar effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms, which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radiofrequency (UWB RF) systems.

Performing nanoscale scanning electron paramagnetic resonance (EPR) requires three essential ingredients. First, a static magnetic field together to field gradients to Zeeman split the electronic energy levels with spatial resolution. Second, a radiofrequency (rf) magnetic field capable of inducing spin transitions. Finally, a sensitive detection method to quantify the energy absorbed by spins. This is usually achieved by combining externally applied magnetic fields with inductive coils or cavities, fluorescent defects or scanning probes. Here, we theoretically propose the realization of a EPR scanning sensor merging all three characteristics into a single device: the vortex core stabilized in ferromagnetic thin-film discs. On one hand, the vortex ground state generates a significant static magnetic field and field gradients. On the other hand, the precessional motion of the vortex core around its equilibrium position produces a circularly polarized oscillating magnetic field, which is enough to produce spin transitions. Finally, the spin-magnon coupling broadens the vortex gyrotropic frequency, suggesting a direct measure of the presence of unpaired electrons. Moreover, the vortex core can be displaced by simply using external magnetic fields of a few mT, enabling EPR scanning microscopy with large spatial resolution. Our numerical simulations show that, by using low damping magnets, it is theoretically possible to detect single spins located on the disc's surface. Vortex nanocavities could also attain strong coupling to individual spin molecular qubits, with potential applications to mediate qubit-qubit interactions or to implement qubit readout protocols.

We derive an expression for the nearest-neighbor spacing distribution $P(s)$ of the energy levels of quantum systems with intermediate dynamics between regularity and chaos and missing levels due to random experimental errors. The expression is based on the Brody distribution, the most widely used for fitting mixed spectra as a function of one parameter. By using Monte Carlo simulations of intermediate spectra based on the $\beta$-Hermite ensemble of Random Matrix Theory, we evaluate the quality of the formula and its suitability for fitting purposes. Estimations of the Brody parameter and the fraction of missing levels can be obtained by a least-square two-parameter fitting of the experimental $P(s)$. The results should be important to distinguish the origins of deviations from RMT in experimental spectra.

In this work logistic regression when both the response and the predictor variables may be missing is considered. Several existing approaches are reviewed, including complete case analysis, inverse probability weighting, multiple imputation and maximum likelihood. The methods are compared in a simulation study, which serves to evaluate the bias, the variance and the mean squared error of the estimators for the regression coefficients. In the simulations, the maximum likelihood methodology is the one that presents the best results, followed by multiple imputation with five imputations, which is the second best. The methods are applied to a case study on the obesity for schoolchildren in the municipality of Viana do Castelo, North Portugal, where a logistic regression model is used to predict the International Obesity Task Force (IOTF) indicator from physical examinations and the past values of the obesity status. All the variables in the case study are potentially missing, with gender as the only exception. The results provided by the several methods are in well agreement, indicating the relevance of the past values of IOTF and physical scores for the prediction of obesity. Practical recommendations are given., Comment: 13 pages, 14 tables

Cavity photons and ferromagnetic spins excitations can exchange information coherently in hybrid architectures, at speeds set by their mutual coupling strength. Speed enhancement is usually achieved by optimizing the geometry of the electromagnetic cavity. Here we show that the geometry of the ferromagnet plays also an important role, by setting the fundamental frequency of the magnonic resonator. Using focused ion beam patterning, we vary the aspect ratio of different Permalloy samples reaching operation frequencies above 10 GHz while working at low external magnetic fields. Additionally, we perform broad band ferromagnetic resonance measurements and cavity experiments that demonstrate that the magnon-photon coupling strength can be estimated using either open transmission lines or resonant cavities, yielding very good agreement. Finally, we describe a simple theoretical framework based on electromagnetic and micromagnetic simulations that successfully accounts for the experimental results. This approach can be used to design hybrid quantum systems exploiting whatsoever magnetostatic mode excited in ferromagnets of arbitrary size and shape and to tune their operation conditions.