Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future

Regularization and Bayesian methods for system identification have been repopularized in the recent years, and proved to be competitive w.r.t. classical parametric approaches. In this paper we shall make an attempt to illustrate how the use of regularization in system identification has evolved over the years, starting from the early contributions both in the Automatic Control as well as Econometrics and Statistics literature. In particular we shall discuss some fundamental issues such as compound estimation problems and exchangeability which play and important role in regularization and Bayesian approaches, as also illustrated in early publications in Statistics. The historical and foundational issues will be given more emphasis (and space), at the expense of the more recent developments which are only briefly discussed. The main reason for such a choice is that, while the recent literature is readily available, and surveys have already been published on the subject, in the author's opinion a clear link with past work had not been completely clarified.Comment: Plenary Presentation at the IFAC SYSID 2015. Submitted to Annual Reviews in Contro

Classical vs. Bayesian methods for linear system identification: point estimators and confidence sets

This paper compares classical parametric methods with recently developed Bayesian methods for system identification. A Full Bayes solution is considered together with one of the standard approximations based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for confidence regions are reported.Comment: number of pages = 8, number of figures =

Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

We propose a deep learning-based phase retrieval method to accurately reconstruct the optical field of a single-sideband minimum-phase signal from the directly detected intensity waveform. Our method relies on a fully convolutional Neural Network (NN) model to realize non-iterative and robust phase retrieval. The NN is trained so that it performs full-field reconstruction and jointly compensates for transmission impairments. Compared to the recently proposed Kramers-Kronig (KK) receiver, our method avoids the distortions introduced by the nonlinear operations involved in the KK phase-retrieval algorithm and hence does not require digital upsampling. We validate the proposed phase-retrieval method by means of extensive numerical simulations in relevant system settings, and we compare the performance of the proposed scheme with the conventional KK receiver operated with a 4-fold digital upsampling. The results show that the 7% hard-decision forward error correction (HD-FEC) threshold at BER 3.8e-3 can be achieved with up to 2.8 dB lower carrier-to-signal power ratio (CSPR) value and 1.8 dB better receiver sensitivity compared to the conventional 4-fold upsampled KK receiver. We also present a comparative analysis of the complexity of the proposed scheme with that of the KK receiver, showing that the proposed scheme can achieve the 7% HD-FEC threshold with 1.6 dB lower CSPR, 0.4 dB better receiver sensitivity, and 36% lower complexity

praja2 regulates KSR1 stability and mitogenic signaling

The kinase suppressor of Ras 1 (KSR1) has a fundamental role in mitogenic signaling by scaffolding components of the Ras/MAP kinase pathway. In response to Ras activation, KSR1 assembles a tripartite kinase complex that optimally transfers signals generated at the cell membrane to activate ERK. We describe a novel mechanism of ERK attenuation based on ubiquitin-dependent proteolysis of KSR1. Stimulation of membrane receptors by hormones or growth factors induced KSR1 polyubiquitination, which paralleled a decline of ERK1/2 signaling. We identified praja2 as the E3 ligase that ubiquitylates KSR1. We showed that praja2-dependent regulation of KSR1 is involved in the growth of cancer cells and in the maintenance of undifferentiated pluripotent state in mouse embryonic stem cells. The dynamic interplay between the ubiquitin system and the kinase scaffold of the Ras pathway shapes the activation profile of the mitogenic cascade. By controlling KSR1 levels, praja2 directly affects compartmentalized ERK activities, impacting on physiological events required for cell proliferation and maintenance of embryonic stem cell pluripotency

Impact of malnutrition on immunity and infection

Malnutrition may be a consequence of energy deficit or micronutrient deficiency. It is considered the most relevant risk factor for illness and death, particularly in developing countries. In this review we described the magnitude of this problem, as well as its direct effect on the immune system and how it results in higher susceptibility to infections. A special emphasis was given to experimental models used to investigate the relationship between undernutrition and immunity. Malnutrition is obviously a challenge that must be addressed to health authorities and the scientific community

Experimental Autoimmune Encephalomyelitis Development Is Aggravated by Candida albicans

Multiple sclerosis (MS) is an inflammatory/autoimmune disease of the central nervous system (CNS) mainly mediated by myelin specific T cells. It is widely believed that environmental factors, including fungal infections, contribute to disease induction or evolution. Even though Candida infection among MS patients has been described, the participation of this fungus in this pathology is not clear. The purpose of this work was to evaluate the effect of a Candida albicans infection on experimental autoimmune encephalomyelitis (EAE) that is a widely accepted model to study MS. Female C57BL/6 mice were infected with C. albicans and 3 days later, animals were submitted to EAE induction by immunization with myelin oligodendrocyte glycoprotein. Previous infection increased the clinical score and also the body weight loss. EAE aggravation was associated with expansion of peripheral CD4+ T cells and production of high levels of TNF-α, IFN-γ IL-6, and IL-17 by spleen and CNS cells. In addition to yeast and hyphae, fungus specific T cells were found in the CNS. These findings suggest that C. albicans infection before EAE induction aggravates EAE, and possibly MS, mainly by CNS dissemination and local induction of encephalitogenic cytokines. Peripheral production of encephalitogenic cytokines could also contribute to disease aggravation

Zinc Supplementation Attenuates Cardiac Remodeling After Experimental Myocardial Infarction

Background/Aims: The objective of our study was to evaluate the effects of zinc supplementation on cardiac remodeling following acute myocardial infarction in rats. Methods: Animals were subdivided into 4 groups and observed for 3 months: 1) Sham Control; 2) Sham Zinc: Sham animals receiving zinc supplementation; 3) Infarction Control; 4) Infarction Zinc. After the followup period, we studied hypertrophy and ventricular geometry, functional alterations in vivo and in vitro, changes related to collagen, oxidative stress, and inflammation, assessed by echocardiogram, isolated heart study, western blot, flow cytometer, morphometry, and spectrophotometry. Results: Infarction induced a significant worsening of the functional variables. On the other hand, zinc attenuated both systolic and diastolic cardiac dysfunction induced by infarction. Considering the infarct size, there was no difference between the groups. Catalase and superoxide dismutase decreased in infarcted animals, and zinc increased its activity. We found higher expression of collagens I and III in infarcted animals, but there was no effect of zinc supplementation. Likewise, infarcted animals had higher levels of IL-10, but without zinc interference. Nrf-2 values were not different among the groups. Infarction increased the amount of Treg cells in the spleen as well as the amount of total lymphocytes. Zinc increased the amount of CD4+ in infarcted animals, but we did not observe effects in relation to Treg cells. Conclusion: zinc attenuates cardiac remodeling after infarction in rats; this effect is associated with modulation of antioxidant enzymes, but without the involvement of collagens I and III, Nrf-2, IL-10, and Treg cells

Asymptotic variance of closed-loop subspace identification methods

In this paper, the asymptotic properties of a version of the "innovation estimation" algorithm by Qin and Ljung as well as of a version of the "whitening filter" based algorithm introduced by Jansson are studied. Expressions for the asymptotic error as the sum of a "bias" term plus a "variance" term are given. The analysis is performed under rather mild assumptions on the spectrum of the joint input-output process; however, in order to avoid unnecessary complications, the asymptotic variance formulas are computed explicitly only for finite memory systems, i.e., of the ARX type. This assumption could be removed at the price of some technical complications; the simulation results confirm that when the past horizon is large enough (as compared to the predictor dynamics) the asymptotic expressions provide a good approximation of the asymptotic variance also for ARMAX system