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We study the dynamical behavior of medium-size multimode VCSELs with an elliptical oxide aperture, selected for the best trade-off between high output power and modulation speed for datacom applications, with a focus on their relative intensity noise (RIN) performance. Our experimental results, collected for various VCSELs, outline the presence of several peaks in the RIN spectra within the bandwidth of the transmission system, which can limit the eye opening under direct current modulation. Here, we present a rigorous model to explain for the first time the origin of these peaks. In particular, the frequencies of the spectral RIN peaks are analytically described as the result of the non-trivial interaction among transverse modes by addressing the laser dynamics and the related noise features through a time-domain mode expansion approach, accounting for coherent effects in multimode competition, spatial hole burning, and carrier diffusion. The laser modulation performance is addressed through dynamical simulations with PAM2 and PAM4 modulations, which clearly demonstrate the potential for high-bitrate optical interconnects. Finally, we address the effect of the oxide aperture aspect ratio via electromagnetic simulations, demonstrating how the ellipticity affects the modal frequency detuning and the RIN, thus providing design guidelines for VCSELs with low RIN performance and outlining a clear roadmap for a substantially improved bandwidth-power trade-off in these devices., Comment: 12 pages, 8 figures

The imputation of missing data is a common procedure in data analysis that consists in predicting missing values of incomplete data points. In this work we analyse a variational quantum circuit for the imputation of missing data. We construct variational quantum circuits with gates complexity $O(N)$ and $O(N^2)$ that return the last missing bit of a binary string for a specific distribution. We train and test the performance of the algorithms on a series of datasets finding good convergence of the results. Finally, we test the circuit for generalization to unseen data. For simple systems, we are able to describe the circuit analytically, making possible to skip the tedious and unresolved problem of training the circuit with repetitive measurements. We find beforehand the optimal values of the parameters and we make use of them to construct an optimal circuit suited to the generation of truly random data., Comment: 21 pages, 9 figures

Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly executed in data centers with energy-efficient hardware accelerators since FPGAs are well-suited for this task due to their inherent parallelism. We present the H2020 project EVEREST, which has developed a system development kit (SDK) to simplify the creation of FPGA-accelerated kernels and manage the execution at runtime through a virtualization environment. This paper describes the main components of the EVEREST SDK and the benefits that can be achieved in our use cases., Comment: Accepted for presentation at DATE 2024 (multi-partner project session)

The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGA energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector., Comment: Accepted for publication in IEEE Transactions on Sustainable Computing

The Quantum Approximate Optimization Algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this work we present a Bayesian optimization procedure to fulfil this optimization task, and we investigate its performance in comparison with other global optimizers. We show that our approach allows for a significant reduction in the number of calls to the quantum circuit, which is typically the most expensive part of the QAOA. We demonstrate that our method works well also in the regime of slow circuit repetition rates, and that few measurements of the quantum ansatz would already suffice to achieve a good estimate of the energy. In addition, we study the performance of our method in the presence of noise at gate level, and we find that for low circuit depths it is robust against noise. Our results suggest that the method proposed here is a promising framework to leverage the hybrid nature of QAOA on the noisy intermediate-scale quantum devices.

The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early detection of lung nodules is essential for the early cancer diagnosis and survival rate of patients. The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. Experiments for lung nodules segmentation has been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing as well as neural network training. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly available as a baseline reference., Comment: Workshop ICIAP 2021 - Deep-Learning and High Performance Computing to Boost Biomedical Applications

Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism. Modern FPGA devices can leverage high-bandwidth memory technologies, but when applications are memory-bound designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. This development process requires hardware design skills that are uncommon in domain-specific experts. In this paper, we propose an automated tool flow from a domain-specific language (DSL) for tensor expressions to generate massively-parallel accelerators on HBM-equipped FPGAs. Designers can use this flow to integrate and evaluate various compiler or hardware optimizations. We use computational fluid dynamics (CFD) as a paradigmatic example. Our flow starts from the high-level specification of tensor operations and combines an MLIR-based compiler with an in-house hardware generation flow to generate systems with parallel accelerators and a specialized memory architecture that moves data efficiently, aiming at fully exploiting the available CPU-FPGA bandwidth. We simulated applications with millions of elements, achieving up to 103 GFLOPS with one compute unit and custom precision when targeting a Xilinx Alveo U280. Our FPGA implementation is up to 25x more energy-efficient than expert-crafted Intel CPU implementations., Comment: Accepted for publication in ACM Transactions on Reconfigurable Technology and Systems (TRETS)

The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics, in order e.g. to classify the phases of matter at equilibrium or to predict the real-time dynamics of a large class of physical models. Typically in these works, a machine learning algorithm is trained and tested on data coming from the same physical model. Here we demonstrate that unsupervised and supervised machine learning techniques are able to predict phases of a non-exactly solvable model when trained on data of a solvable model. In particular, we employ a training set made by single-particle correlation functions of a non-interacting quantum wire and by using principal component analysis, k-means clustering, and convolutional neural networks we reconstruct the phase diagram of an interacting superconductor. We show that both the principal component analysis and the convolutional neural networks trained on the data of the non-interacting model can identify the topological phases of the interacting model with a high degree of accuracy. Our findings indicate that non-trivial phases of matter emerging from the presence of interactions can be identified by means of unsupervised and supervised techniques applied to data of non-interacting systems., Comment: 9 pages, 7 figures. Final version

Two Observing System Experiments were carried out at (ECMWF). The European Center for Medium Range Weather Forecasting impact of various observing systems were examined for two periods during the EGGE year: November 8 to 9, 1979 (OSE-1) and February 22 to March 7, 1979 (OSE-II). Attempts were made to understand the effect of the observing systems on both the analyses and the quality of the short and medium range forecasts. The results confirm that the impact of a particular observing system (e.g., SATEM) is dependent on the level of synoptic activity present in the areas where this particular observing system is the main soruce of meteorological information. SATOB data are also shown to be improtant for the analyses of tropical regions, whereas SATEM data are of paramount importance for the extratropical analyses over ocean areas. Aircraft data, where available, are an invaluable addition to the global observational data base. These results therefore demonstrate clearly the value of each of the observing systems. The fact that, in particular circumstances, there may be some redundancy between the systems is a strength rather than a weakness of the composite global observing system.