arXiv subjects

Ricardo Sousa

Publications and source records attributed to Ricardo Sousa.

9 recordsLinked to original sources

arXiv2025

Entropy-Assisted Nanosecond stochastic operation in perpendicular superparamagnetic tunnel junctions

We demonstrate a good agreement between mean dwell times measured in 50~nm diameter, perpendicularly magnetized superparamagnetic tunnel junctions (SMTJ), and theoretical predictions based on Langer's theory. Due to a large entropic contribution, the theory yields Arrhenius prefactors in the femtosecond range for the measured junctions, in stark contrast to the typically assumed value of 1~ns. Thanks to the low prefactors, and fine-tuning of the perpendicular magnetic anisotropy, we report measured mean dwell times as low as 2.7~ns under an in-plane applied field at negligible bias voltage. Under a perpendicular applied field, we predict a Meyer-Neldel compensation phenomenon, whereby the prefactor scales like an exponential of the activation energy, in line with the exponential dependence of the measured dwell time on the field. We further predict the occurrence of (sub)nanosecond dwell times as a function of effective anisotropy and junction diameter at zero bias voltage. These findings pave the way towards the development of ultrafast, low-power, unconventional computing schemes operating by leveraging thermal noise in perpendicular SMTJs, which can be scaled down below 20~nm.

cond-mat.mtrl-sci↗

arXiv2024

Process Mining Embeddings: Learning Vector Representations for Petri Nets

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

cs.AI↗

arXiv2023

Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short and long term memory, non-linear fast response and relatively small footprint. Here we report how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions enable to emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic neuron response in a dense Neural Network (NN). The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks (SNN) to sub-100nm size elements.

physics.app-ph↗

arXiv2023

Task Conditioned BERT for Joint Intent Detection and Slot-filling

Dialogue systems need to deal with the unpredictability of user intents to track dialogue state and the heterogeneity of slots to understand user preferences. In this paper we investigate the hypothesis that solving these challenges as one unified model will allow the transfer of parameter support data across the different tasks. The proposed principled model is based on a Transformer encoder, trained on multiple tasks, and leveraged by a rich input that conditions the model on the target inferences. Conditioning the Transformer encoder on multiple target inferences over the same corpus, i.e., intent and multiple slot types, allows learning richer language interactions than a single-task model would be able to. In fact, experimental results demonstrate that conditioning the model on an increasing number of dialogue inference tasks leads to improved results: on the MultiWOZ dataset, the joint intent and slot detection can be improved by 3.2\% by conditioning on intent, 10.8\% by conditioning on slot and 14.4\% by conditioning on both intent and slots. Moreover, on real conversations with Farfetch costumers, the proposed conditioned BERT can achieve high joint-goal and intent detection performance throughout a dialogue.

cs.CL↗

arXiv2023

Electrical detection and nucleation of a magnetic skyrmion in a magnetic tunnel junction observed via operando magnetic microscopy

Magnetic skyrmions are topological spin textures which are envisioned as nanometre scale information carriers in magnetic memory and logic devices. The recent demonstration of room temperature stabilization of skyrmions and their current induced manipulation in industry compatible ultrathin films were first steps towards the realisation of such devices. However, important challenges remain regarding the electrical detection and the low-power nucleation of skyrmions, which are required for the read and write operations. Here, we demonstrate, using operando magnetic microscopy experiments, the electrical detection of a single magnetic skyrmion in a magnetic tunnel junction (MTJ) and its nucleation and annihilation by gate voltage via voltage control of magnetic anisotropy. The nucleated skyrmion can be further manipulated by both gate voltage and external magnetic field, leading to tunable intermediate resistance states. Our results unambiguously demonstrate the readout and voltage controlled write operations in a single MTJ device, which is a major milestone for low power skyrmion based technologies.

cond-mat.mtrl-sci↗

arXiv2022

Optical Switching in Tb/Co-Multilayer Based Nanoscale Magnetic Tunnel Junctions

Magnetic tunnel junctions (MTJs) are elementary units of magnetic memory devices. For high-speed and low-power data storage and processing applications, fast reversal by an ultrashort laser pulse is extremely important. We demonstrate optical switching of Tb/Comultilayer-based nanoscale MTJs by combining optical writing and electrical read-out methods. A 90 fs-long laser pulse switches the magnetization of the storage layer (SL). The change in magnetoresistance between the SL and a reference layer (RL) is probed electrically across the tunnel barrier. Single-shot switching is demonstrated by varying the cell diameter from 300 nm to 20 nm. The anisotropy, magnetostatic coupling, and switching probability exhibit cell-size dependence. By suitable association of laser fluence and magnetic field, successive commutation between high-resistance and low-resistance states is achieved. The switching dynamics in a continuous film is probed with the magneto-optical Kerr effect technique. Our experimental findings provide strong support for the growing interest in ultrafast spintronic devices.

physics.app-ph↗

arXiv2021

Observation of magnetic helicoidal dichroism with extreme ultraviolet light vortices

We report on the experimental evidence of magnetic helicoidal dichroism, observed in the interaction of an extreme ultraviolet vortex beam carrying orbital angular momentum with a magnetic vortex. Numerical simulations based on classical electromagnetic theory show that this dichroism is based on the interference of light modes with different orbital angular momenta, which are populated after the interaction between the light phase chirality and the magnetic topology. This observation gives insight into the interplay between orbital angular momentum and magnetism, and sets the framework for the development of new analytical tools to investigate ultrafast magnetization dynamics.

physics.optics↗

arXiv2020

Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information

Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associated with other variables present in the data, without losing interpretability. Moreover, the metrics for calculating dissimilarity between qualitative values often scale poorly for high dimensional mixed datasets. In this study, we propose a novel method for clustering qualitative values, based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC maintains the original interpretability of the qualitative information present in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by Spotify, we showcase how our method can be used for clustering music artists based on the quantitative features of thousands of songs. In addition, using financial features of companies, we cluster company industries, and discuss the implications in investment portfolios diversification.

cs.LG↗

arXiv2011

The Data Replication Method for the Classification with Reject Option

Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we adapt a paradigm initially proposed for the classification of ordinal data to address the classification problem with reject option. The technique reduces the problem of classifying with reject option to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Finally, the framework is extended to multiclass ordinal data with reject option. An experimental study with synthetic and real data sets, verifies the usefulness of the proposed approach.

cs.CV↗