Neural Networks Architecture Evaluation in a Quantum Computer

In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artificial neural networks. Unlike conventional algorithms for evaluating neural network architectures, QNNAE does not depend on initialization of weights. The proposed algorithm has a binary output and results in 0 with probability proportional to the performance of the network. And its computational cost is equal to the computational cost to train a neural network

Estimation of 5G Core and RAN End-to-End Delay through Gaussian Mixture Models

Funding Information: This research was funded by Fundação para a Ciência e Tecnologia (FCT) under the projects 2022.08786.PTDC and UIDB/50008/2020. Publisher Copyright: © 2022 by the authors.Network analytics provide a comprehensive picture of the network’s Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the Core and the Radio Access Network (RAN) E2E delay of 5G networks with the Standalone (SA) and Non-Standalone (NSA) topologies when a single known Probability Density Function (PDF) is not suitable to model its distribution. To this end, multiple PDFs, denominated as components, are combined in a Gaussian Mixture Model (GMM) to represent the distribution of the E2E delay. The accuracy and computation time of the GMM is evaluated for a different number of components and a number of samples. The results presented in the paper are based on a dataset of E2E delay values sampled from both SA and NSA 5G networks. Finally, we show that the GMM can be adopted to estimate a high diversity of E2E delay patterns found in 5G networks and its computation time can be adequate for a large range of applications.publishersversionpublishe

Distribution of the Residual Self-Interference Power in In-Band Full-Duplex Wireless Systems

This paper derives the distribution of the residual self-interference (SI) power in an analog post-mixer canceler adopted in a wireless in-band full-duplex communication system. We focus on the amount of uncanceled SI power due to SI channel estimation errors. Closed form expressions are provided for the distribution of the residual SI power when Rician and Rayleigh fading SI channels are considered. Moreover, the distribution of the residual SI power is derived for low and high channel gain dynamics, by considering the cases when the SI channel gain is time-invariant and time-variant. While for time-invariant channels the residual SI power is exponentially distributed, for time-variant channels the exponential distribution is not a valid assumption. Instead, the distribution of the residual SI power can be approximated by a product distribution. Several Monte Carlo simulation results show the influence of the channel dynamics on the distribution of the residual SI power. Finally, the accuracy of the theoretical approach is assessed through the comparison of numerical and simulated results, which confirm its effectiveness.publishe

Recombinant feline interferon omega therapy in cats naturally infected with Feline Immunodeficiency Virus : clinical, viral and immunological relevance

Tese de Doutoramento em Ciências Veterinárias Especialidade de ClínicaType-I Interferons are well-known cytokines which among their main functions are key components of the host immune response against viral infections. Due to its immune modulation properties, they are commonly used in the therapeutic approach of various diseases such as retroviral infections. Recombinant feline interferon omega (rFeIFN-ω) is the first interferon licensed for use in veterinary medicine. Although it is commonly administered in retroviral infections, namely in Feline Immunodeficiency Virus (FIV) and Feline Leukemia Virus (FeLV) infected cats, few studies reported its clinical benefits and mechanisms of action. This thesis aims to clarify the main properties of the licensed rFeIFN-ω protocol (3 cycles of 5 daily subcutaneous administrations of 1MU/kg beginning on days 0, 14 and 60) in naturally retroviral infected cats living in an animal shelter, evaluating its effect not only on clinical improvement but also on concurrent viral excretion, viremia/proviral load and various immune biomarkers such as acute phase proteins and cytokine profile. Recognizing the non specific and subtle clinical presentation of the majority of FIV-infected cats, this work also presents and evaluates an alternative oral rFeIFN-ω protocol (0.1MU/cat during 90 days) to be used in client-owned FIV-infected cats. Results showed that the licensed rFeIFN-ω protocol induces a significant clinical improvement, with a concurrent reduction of opportunistic viral infections and an increase on acute phase proteins (APP) profile. The alternative protocol also revealed an important clinical improvement but without significant changes on opportunistic viral infections (which were of low level in the tested group) or on APP profile. In both protocols, no changes were remarked on viremia neither on T-helper 1/T-helper 2 cytokine profiles meaning that this compound may lack an anti-viral activity for retroviruses in vivo and do not act on the acquired immune response of FIV-positive cats. However, there was a significant reduction of the interleukin-6 plasma levels (pro-inflammatory cytokine) in cats treated with the licensed protocol and a decrease on its mRNA expression in cats treated orally. This shows that rFeIFN-ω can have anti-inflammatory properties, which are more evident in the higher doses of the licensed protocol. More than contributing for a better knowledge of rFeIFN-ω, this thesis explores its immune modulation properties and validates a new oral protocol which can be included on future FIV-guidelines.Resumo - A terapêutica com interferão ómega felino em gatos naturalmente infectados com o vírus da imunodeficiência felina: relevância clinica, virológica e imunitária - Os interferões do tipo I são citoquinas chave do sistema imunitário. Devido às suas propriedades imunomoduladoras, são um recurso terapêutico frequente em diferentes doenças como as infecções retrovirais. O interferão ómega felino (rFeIFN-ω) é o primeiro interferão licenciado para medicina veterinária. Apesar do seu uso no tratamento de infeções retrovirais como o vírus da imunodeficiência felina (FIV) e o vírus da leucemia felina (FeLV), são poucos os estudos que fundamentam o seu benefício clinico. Esta tese visa clarificar as propriedades terapêuticas e imunomoduladoras do protocolo licenciado de rFeIFN-ω (3 ciclos de 5 administrações subcutâneas de 1MU/kg uma vez ao dia a iniciar aos dias 0, 14 e 60) em gatos naturalmente infectados por retrovírus e residentes em gatil. Em detalhe, este trabalho avalia o efeito deste fármaco na melhoria clinica, na excreção de vírus concomitantes, na virémia/provirus e na variação de diferentes marcadores imunitários como proteínas de fase aguda e perfil de citoquinas. Esta tese contempla ainda o desenvolvimento de um protocolo terapêutico alternativo baseado na administração oral de rFeIFN-ω (0.1MU/gato durante 90 dias consecutivos) para uso em gatos FIV-positivos domésticos, os quais apresentam geralmente um quadro clinico subtil e pouco específico. Os resultados revelaram que o protocolo licenciado induz uma melhoria clinica significativa com redução concomitante das infecções oportunistas e um aumento do perfil de proteínas de fase aguda (APP). O protocolo alternativo revelou-se eficaz na melhoria clinica dos animais tratados, apesar de não induzir alterações significativas do perfil de APPs nem das infecções concomitantes (residuais no grupo de estudo). Ambos os protocolos não induziram alterações na virémia nem no perfil de citoquinas participantes nas respostas T-helper 1 ou T-helper 2 o que sugere que este composto não apresenta propriedades anti-virais nem actua na imunidade adquirida de gatos FIV positivos. Verificou-se contudo um decréscimo dos niveis plasmáticos de Interleucina-6 (citoquina pro-inflamatória) em gatos tratados com o protocolo subcutâneo e uma redução da sua expressão (mRNA) em gatos tratados por vira oral. Tal demonstra que o rFeIFN-ω apresenta propriedades anti-inflamatórias, as quais são mais evidentes aquando do tratamento com o protocolo licenciado. Mais que uma contribuição para um melhor conhecimento do rFeIFN-ω, esta tese explora as suas propriedades imunomoduladoras e valida um novo protocolo oral, o qual poderá ser incluído em futuras guidelines para o tratamento de gatos FIV-positivos.Trabalho financiado também pelo Centro de Investigação Interdisciplinar em Sanidade Animal (CIISA) da Faculdade de Medicina Veterinária, Universidade de Lisboa e Virbac (Centro de Custos phD_Virbac)

Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison

UIDB/ 50008/2020This paper investigates the detection of abnormal sequences of signaling packets purposely generated to perpetuate signaling-based attacks in computer networks. The problem is studied for the Session Initiation Protocol (SIP) using a dataset of signaling packets exchanged by multiple end-users. A sequence of SIP messages never observed before can indicate possible exploitation of a vulnerability and its detection or prediction is of high importance to avoid security attacks due to unknown abnormal SIP dialogs. The paper starts to briefly characterize the adopted dataset and introduces multiple definitions to detail how the deep learning-based approach is adopted to detect possible attacks. The proposed solution is based on a convolutional neural network capable of exploring the definition of an orthogonal space representing the SIP dialogs. The space is then used to train the neural network model to classify the type of SIP dialog according to a sequence of SIP packets prior observed. The classifier of unknown SIP dialogs relies on the statistical properties of the supervised learning of known SIP dialogs. Experimental results are presented to assess the solution in terms of SIP dialogs prediction, unknown SIP dialogs detection, and computational performance, demonstrating the usefulness of the proposed methodology to rapidly detect signaling-based attacks.publishersversionpublishe

Capacity and Energy Efficiency Trade-off in Multi-Packet Reception Wireless Systems

Publisher Copyright: © 2022 IEEE.The radio access in Internet-of-Things (IoT) networks requires minimizing the energy consumption while achieving the capacity requirements especially for high density deployment. The Multi-Packet Reception (MPR) systems can potentially increase the capacity of devices due to the capability of decoding multiple transmitted packets at the receiver. However, the aggregate interference in such scenarios can lead to unfair distribution of the resources and eventually waste of energy. Therefore, this work provides an analytical characterization of the trade-off between capacity and energy consumption while regulating the channel access of multiple transmitters to a single-MPR receiver. The theoretical modeling considers different densities of spatially distributed nodes and their channel propagation conditions, in addition to different capture sensitivity thresholds at the receiver. The model is validated through simulation and it is shown to be an effective tool to identify the optimal channel access probability that maximizes the capacity per energy consumption.authorsversionpublishe

Aggregate Interference Power Characterization for Directional Beamforming Wireless Networks

partially supported by the projects CoSHARE (LISBOA-01-0145-FEDER0307095 - PTDC/EEI-TEL/30709/2017), and UIDB/50008/2020.In this paper, we characterize the aggregate interference power in directional millimeter-wave communications, capturing the effect of beamforming. The analysis considers a general distance-based path loss with Rayleigh and Rician fading channels and a sectored antenna model. Moreover, the nodes are uniformly distributed over a circular or annular area centered at the receiver. The main contribution of the paper is the derivation of the moment generating function (MGF) of the aggregate interference power. The MGF is adopted in a moment-based approximation to a Gamma distribution and used as a model of the aggregate interference power. Several simulations confirm the effectiveness of the proposed approximation for different scenarios, highlighting the effect of directional communications on the aggregate interference power.authorsversionpublishe

Interference Distribution for Directional Beamforming Mobile Networks

Publisher Copyright: © 2013 IEEE.In this paper, we model the aggregate interference power in directional beamforming mobile networks. The work considers the random waypoint model to describe the mobility of the nodes and adopts directional beamforming for communication. The major contribution of this paper is the statistical characterization of the aggregate interference caused by directional beamforming transmissions of mobile interferers to a given node positioned at a reference point. The analysis assumes Rayleigh and Rician small-scale fading channels, a distance-based path-loss large-scale fading model, and a three gain levels sectored antenna model. The quality of the proposed approximations has been confirmed through various simulations for different mobility scenarios, channel conditions, and beamforming parameters, highlighting the effect of directional communications along with mobility on aggregate interference. To demonstrate the practical application of the work, we use two different estimators for the interference characterization. The results confirm the effectiveness of the estimators even when adopting a small set of samples.publishersversionpublishe

Analysis, Design and Implementation of Biodiesel Projects in Brazil

During the oil crisis of the seventies, Brazil has developed a successful program for gasoline substitution by ethanol (Proálcool). Nowadays the biomass accounts for 27% of total national energy consumed in Brazil and the ethanol participates with 40% of the total national fuel consumption of Otto cycle vehicles. In 2004, the National Program for the Production and Use of Biodiesel (Biodiesel Program) was launched. One priority of the Biodiesel Program is the inclusion of family agriculture and smallholders into the production chain. The Federal University of Viçosa (UFV) has developed a software for the analysis of biodiesel projects with the participation of family agriculture. Results of production chain analysis and economic indicators calculated by the Biosoft system have allowed identifying the regular supply of oil at competitive prices as the key point to the efficiency of biodiesel production chains. The use of oil cake as feedstock is the leverage point of chain performance. The meal sale can lead to a vegetal oil price reduction, without compromising farmers´ income, since they can be able to set up their own oil extraction plants. Coordination is then the critical element and has the potential to improve the performance of both the biodiesel industry and the animal production chain.Agribusiness, Agricultural and Food Policy, Farm Management, Food Consumption/Nutrition/Food Safety, Industrial Organization,

Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

Funding Information: This work was funded by Fundac¸ão para a Ciência e Tecnologia, under the projects InfoCent-IoT (PTDC/EEI-TEL/30433/2017), CoSHARE (PTDC/EEI-TEL/30709/2017), and Grant UIDB/50008/2020.This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles' mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.authorsversionpublishe