Adaptive MCA-Matched Filter Algorithms for Binary Detection

In this work, we present a method for signal-to-noise ratio maximization using a linear filter based on minor component analysis of the noise covariance matrix. As we will see, the greatest benefits are obtained when both filter and signal design are treated as a single problem. This general problem is then related to the minimization of the probability of error of a digital communication. In particular, the classical binary detection problem is considered when nonstationary and (possibly) nonwhite additive Gaussian noise is present. Two algorithms are given to solve the problem at hand with cuadratic and linear computational complexity with respect to the dimension of the problem.Sociedad Argentina de Informática e Investigación Operativ

Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release

The explosion of data collection has raised serious privacy concerns in users due to the possibility that sharing data may also reveal sensitive information. The main goal of a privacy-preserving mechanism is to prevent a malicious third party from inferring sensitive information while keeping the shared data useful. In this paper, we study this problem in the context of time series data and smart meters (SMs) power consumption measurements in particular. Although Mutual Information (MI) between private and released variables has been used as a common information-theoretic privacy measure, it fails to capture the causal time dependencies present in the power consumption time series data. To overcome this limitation, we introduce the Directed Information (DI) as a more meaningful measure of privacy in the considered setting and propose a novel loss function. The optimization is then performed using an adversarial framework where two Recurrent Neural Networks (RNNs), referred to as the releaser and the adversary, are trained with opposite goals. Our empirical studies on real-world data sets from SMs measurements in the worst-case scenario where an attacker has access to all the training data set used by the releaser, validate the proposed method and show the existing trade-offs between privacy and utility.Comment: to appear in IEEESmartGridComm 2019. arXiv admin note: substantial text overlap with arXiv:1906.0642

Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning

The rapid development and expansion of the Internet of Things (IoT) paradigm has drastically increased the collection and exchange of data between sensors and systems, a phenomenon that raises serious privacy concerns in some domains. In particular, Smart Meters (SMs) share fine-grained electricity consumption of households with utility providers that can potentially violate users' privacy as sensitive information is leaked through the data. In order to enhance privacy, the electricity consumers can exploit the availability of physical resources such as a rechargeable battery (RB) to shape their power demand as dictated by a Privacy-Cost Management Unit (PCMU). In this paper, we present a novel method to learn the PCMU policy using Deep Reinforcement Learning (DRL). We adopt the mutual information (MI) between the user's demand load and the masked load seen by the power grid as a reliable and general privacy measure. Unlike previous studies, we model the whole temporal correlation in the data to learn the MI in its general form and use a neural network to estimate the MI-based reward signal to guide the PCMU learning process. This approach is combined with a model-free DRL algorithm known as the Deep Double Q-Learning (DDQL) method. The performance of the complete DDQL-MI algorithm is assessed empirically using an actual SMs dataset and compared with simpler privacy measures. Our results show significant improvements over state-of-the-art privacy-aware demand shaping methods

On the Impact of Side Information on Smart Meter Privacy-Preserving Methods

Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of distortion-based real-time privacy-preserving algorithms for SMs. In particular, we consider a deep adversarial learning framework, in which the desired releaser (a recurrent neural network) is trained by fighting against an adversary network until convergence. To define the loss functions, two different approaches are considered: the Causal Adversarial Learning (CAL) and the Directed Information (DI)-based learning. The main difference between these approaches is in how the privacy term is measured during the training process. On the one hand, the releaser in the CAL method, by getting supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood. On the other hand, the releaser in the DI approach completely relies on the feedback received from the adversary and is optimized to maximize its uncertainty. The performance of these two algorithms is evaluated empirically using real-world SMs data, considering an attacker with access to SI (e.g., the day of the week) that tries to infer the occupancy status from the released SMs data. The results show that, although they perform similarly when the attacker does not exploit the SI, in general, the CAL method is less sensitive to the inclusion of SI. However, in both cases, privacy levels are significantly affected, particularly when multiple sources of SI are included

Control of Immunoregulatory Molecules by miRNAs in T Cell Activation

MiRNA targeting of key immunoregulatory molecules fine-tunes the immune response. This mechanism boosts or dampens immune functions to preserve homeostasis while supporting the full development of effector functions. MiRNA expression changes during T cell activation, highlighting that their function is constrained by a specific spatiotemporal frame related to the signals that induce T cell-based effector functions. Here, we update the state of the art regarding the miRNAs that are differentially expressed during T cell stimulation. We also revisit the existing data on miRNA function in T cell activation, with a special focus on the modulation of the most relevant immunoregulatory molecules.We thank Dr M. Vicente-Manzanares for critical reading of the manuscript and for assistance with English editing. This study was supported by the following grants from the Spanish Ministry of Economy and Competitiveness, (grant SAF2017-82886-R to FSM), CIBER CARDIOVASCULAR and PIE 13.0004-BIOIMID from the Instituto de Salud Carlos III (Fondo de Investigacion Sanitaria del Instituto de Salud Carlos III with co-funding from the Fondo Europeo de Desarrollo Regional; FEDER), Programa de Actividades en Biomedicina de la Comunidad de Madrid-B2017/BMD-3671-INFLAMUNE to FS-M, and ERC-2011-AdG294340-GENTRIS to FS-M. The Centro Nacional de Investigaciones Cardiovasculares (CNIC) is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (MINECOaward SEV-2015-0505). AR-G is supported by the FPU program (Spanish Ministry of Education). LF-M is funded by the CIBER CARDIOVASCULAR.S

Labor market experience and falling earnings inequality in Brazil: 1995–2012

The Gini coefficient of labor earnings in Brazil fell by nearly a fifth between 1995 and 2012, from 0.50 to 0.41. The decline in other measures of earnings inequality was even larger, with the 90-10 percentile ratio falling by almost 40 percent. Applying micro-econometric decomposition techniques, this study parses out the proximate determinants of this substantial reduction in earnings inequality. Although a falling education premium did play a role, in line with received wisdom, this study finds that a reduction in the returns to labor market experience was a much more important factor driving lower wage disparities. It accounted for 53 percent of the observed decline in the Gini index during the period. Reductions in horizontal inequalities – the gender, race, regional and urban-rural wage gaps, conditional on human capital and institutional variables – also contributed. Two main factors operated against the decline: a greater disparity in wage premia to different sectors of economic activity, and the “paradox of progress”: the mechanical inequality-increasing effect of a more educated labor force when returns to education are convex

MicroRNAs in T Cell-Immunotherapy.

MicroRNAs (miRNAs) act as master regulators of gene expression in homeostasis and disease. Despite the rapidly growing body of evidence on the theranostic potential of restoring miRNA levels in pre-clinical models, the translation into clinics remains limited. Here, we review the current knowledge of miRNAs as T-cell targeting immunotherapeutic tools, and we offer an overview of the recent advances in miRNA delivery strategies, clinical trials and future perspectives in RNA interference technologies.This manuscript was funded by grants AEI/10.13039/501100011033, PID-2020-120412RBI100 and PDC2021-121797-I00 (F.S.-M.) from the Spanish Ministry of Economy and Competitiveness; CAM (S2017/BMD-3671-INFLAMUNE-CM) from the Comunidad de Madrid (F.S.-M.), CIBERCV (CB16/11/00272) and BIOIMID PIE13/041 from the Instituto de Salud Carlos “la Caixa” Foundation under the project code HR17-00016. The current research is supported by AECC-Coordinated Grant 2022 (PRYCO223002PEIN). The CNIC is supported by the Ministerio de Ciencia, Innovacion y Universidades and the Pro-CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015- 0505). IMDEA Nanociencia acknowledges support from the ‘Severo Ochoa’ Programme for Centres of Excellence in R&D (MINECO, CEX2020-001039-S). S.G.D. is supported by a grant from the Spanish Ministry of Universities.S

Loss of Excitation Detection in Synchronous Generators Based on Dynamic State Estimation

In this article, we present a new approach to the detection of Loss-Of-Excitation (LOE), a typical failure of synchronous generators. Unlike most of the algorithms proposed in the literature, which only use the information available at the point of connection, we also take advantage of prior knowledge of the generator model. To track the field voltage and the other state variables, we have chosen the Constrained Unscented Kalman Filter (CUKF) as the core estimation technique, with phasor measurements as the input for this filtering algorithm. Detection of LOE is then performed by using the Faulty Modes Detection and Diagnosis (FMDD) algorithm, which combines the normal operation and a LOE-based model to decide in real-time whether an LOE has occurred or not. Results of simulations using a small two-area power system and the IEEE 39-bus system show that LOE detection times can be significantly reduced as compared to conventional and state-of-the-art approaches. Moreover, we observe that the new fault detection signal used to trip the generator can avoid short-term voltage stability problems.Fil: Marchi, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Gill Estevez, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Messina, Francisco Javier. Mc Gill University; CanadáFil: Galarza, Cecilia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentin