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Fault tolerance is a critical aspect of modern computing systems, ensuring correct functionality in the presence of faults. This paper presents a comprehensive survey of fault tolerance methods and software-based mitigation techniques in embedded systems. The focus is on real-time embedded systems, considering their resource constraints and the increasing interconnectivity of computing systems in commercial and industrial applications. The survey covers various fault-tolerance methods, including hardware, software, and hybrid redundancy. Particular emphasis is given to software faults, acknowledging their significance as a leading cause of system failures. Moreover, the paper explores the challenges posed by soft errors in modern computing systems. The survey concludes by emphasizing the need for continued research and development in fault-tolerance methods, specifically in the context of real-time embedded systems, and highlights the potential for extending fault-tolerance approaches to diverse computing environments.

The last decade's market has been characterized by wearable devices, mainly smartwatches, edge, and cloud computing. A possible application of these technologies is to improve the safety of dangerous activities, especially driving motor vehicles. Common enabling technologies, such as system-on-chip, ultra-low-power computational platforms, and wide-band wireless connectivity, push all these trends. On the one hand, wearable devices, thanks to the continuous contact with the user's body, can measure physiological parameters. On the other hand, edge computing and machine learning techniques, alongside cameras, allow the implementation of contactless computer vision systems capable of providing information about the user's current behavior. Another trend is the usage of RADARs in automotive applications, both for collision avoidance and monitoring driver behavior. These technologies can be combined to develop systems designed to aid the driver. For the sake of this paper, we are focusing on warning drivers, allowing them to know whenever they are drowsy and hence risking a sleep onset or are not paying attention to the road. Developing such systems poses many challenges, such as automatic classification of physiological signal patterns, facial expression recognition, head movements and eye gaze detection. These challenges have been individually addressed in the literature. Anyway, we noticed a need for more description on implementing data fusion. Two main reasons for adopting the fusion approach are to improve the quality of the overall representation (increasing accuracy and specificity against drowsy) and make a more reliable system due to redundancy., Comment: Accepted for presentation at the CPS workshop 2023 (http://www.cpsschool.eu/cps-workshop)

An algorithm for representing irregular satellite data obtained from advanced synthetic aperture radar (ASAR) uniformly in space and time is introduced and comparison with WAVEWATCH III model (WW3) data is carried out. Swell global data for 2007 are analysed using statistical parameters such as bias and scattering index. Satellite data showed a great spatial uniformity compared with WW3 model. Results show underestimation of swell height by the model in the tropics and at specific higher latitude regions.

This work reports on the design, implementation and evaluation of a survey used as a remote sanitary filter, which was applied in the Adventist Educational System of Baja California. During the SARS-CoV-2 sanitary contingency. This with the intention of acquiring information safely and in real time. Allowing to safeguard the integrity of the educational community as a whole. Accessible and easily distributed digital resources were used in order to meet the necessary requirements of distribution, acquisition and management of the information. These were adapted to the previously established communication dynamic of the educational system. The use of the survey as a sanitary filter allowed to acquire relevant health information from the student community and facilitated decision-making regarding class attendance by their tutors.These in consideration to the conditions established by educational and health authorities. Despite the short time during the design and implementation, the communication of the educational system allowed the effective distribution, application and monitoring of the digital survey. Similar strategies can be used in the follow-up during the reactivation after lockdown and to analyze social components that may be involved in these scenarios, Comment: Spanish, 9 pages, 3 tables, 3 figures

This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic-related keywords appearing in the text. The index assesses the importance of the economic-related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

This article, written in Italian language, explores the contribution given by Bayes' rule and by subjective probability in the work at Bletchley Park towards cracking Enigma cyphered messages during WWII. -- In questo articolo, scritto in Italiano, esploriamo il contributo dato dal teorema di Bayes e dalle idee della probabilit\`a soggettiva nel lavoro compiuto a Bletchley Park che ha portato a decifrare i messaggi cifrati con macchine Enigma durante la Seconda Guerra Mondiale., Comment: 39 pages, 10 figures, in Italian

Seizure activity is a ubiquitous and pernicious pathophysiology that, in principle, should yield to mathematical treatments of (neuronal) ensemble dynamics - and therefore interventions on stochastic chaos. A seizure can be characterised as a deviation of neural activity from a stable dynamical regime, i.e. one in which signals fluctuate only within a limited range. In silico treatments of neural activity are an important tool for understanding how the brain can achieve stability, as well as how pathology can lead to seizures and potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the (neuronal) state equation used in Dynamic Causal Modelling generalises to a Fokker-Planck formalism when propagation of neuronal activity along structural connections is considered. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity (i.e., connectivity that disperses neuronal fluctuations). We show, for neural systems prone to epileptic seizures, that such a reduction can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror epileptic activity in the areas adjoining an affected brain region - thus 'fighting seizures with seizures'. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network. Our hope is that this technique can form the basis for real-time monitoring and intervention devices that are capable of suppressing or even preventing seizures in a non-invasive manner.

Models of coupled oscillators are used to describe a wide variety of phenomena in neuroimaging. These models typically rest on the premise that oscillator dynamics do not evolve beyond their respective limit cycles, and hence that interactions can be described purely in terms of phase differences. Whilst mathematically convenient, the restrictive nature of phase-only models can limit their explanatory power. We therefore propose a generalisation of dynamic causal modelling that incorporates both phase and amplitude. This allows for the separate quantifications of phase and amplitude contributions to the connectivity between neural regions. We establish, using model-generated data and simulations of coupled pendula, that phase-only models perform well only under weak coupling conditions. We also show that, despite their higher complexity, phase-amplitude models can describe strongly coupled systems more effectively than their phase-only counterparts. We relate our findings to four metrics commonly used in neuroimaging: the Kuramoto order parameter, cross-correlation, phase-lag index, and spectral entropy. We find that, with the exception of spectral entropy, the phase-amplitude model is able to capture all metrics more effectively than the phase-only model. We then demonstrate, using local field potential recordings in rodents and functional magnetic resonance imaging in macaque monkeys, that amplitudes in oscillator models play an important role in describing neural dynamics in anaesthetised brain states., Comment: arXiv admin note: text overlap with arXiv:1812.06315

Models of coupled oscillators are useful in describing a wide variety of phenomena in physics, biology and economics. These models typically rest on the premise that the oscillators are weakly coupled, meaning that amplitudes can be assumed to be constant and dynamics can therefore be described purely in terms of phase differences. Whilst mathematically convenient, the restrictive nature of the weak coupling assumption can limit the explanatory power of these phase-coupled oscillator models. We therefore propose an extension to the weakly-coupled oscillator model that incorporates both amplitude and phase as dependent variables. We use the bilinear neuronal state equations of dynamic causal modelling as a foundation in deriving coupled differential equations that describe the activity of both weakly and strongly coupled oscillators. We show that weakly-coupled oscillator models are inadequate in describing the processes underlying the temporally variable signals observed in a variety of systems. We demonstrate that phase-coupled models perform well on simulations of weakly coupled systems but fail when connectivity is no longer weak. On the other hand, using Bayesian model selection, we show that our phase-amplitude coupling model can describe non-weakly coupled systems more effectively despite the added complexity associated with using amplitude as an extra dependent variable. We demonstrate the advantage of our phase-amplitude model in the context of model-generated data, as well as of a simulation of inter-connected pendula, neural local field potential recordings in rodents under anaesthesia and international economic gross domestic product data., Comment: 17 pages, 5 figures

Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on behavioral and cognitive performance is constantly growing, those studies fail to address the importance of subject- specific stimulation protocols. With this study here, we set the foundation to combine tACS with a recently presented framework that utilizes real-time fRMI and Bayesian optimization in order to identify the most optimal tACS protocol for a given individual. While Bayesian optimization is particularly relevant to such a scenario, its success depends on two fundamental choices: the choice of covariance kernel for the Gaussian process prior as well as the choice of acquisition function that guides the search. Using empirical (functional neuroimaging) as well as simulation data, we identified the squared exponential kernel and the upper confidence bound acquisition function to work best for our problem. These results will be used to inform our upcoming real- time experiments., Comment: accepted for oral presentation at PRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging

Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined with real-time fMRI to propose a novel framework which turns on its head the conventional functional neuroimaging approach. This closed-loop method automatically designs the optimal experiment to evoke a desired target brain pattern. One of the challenges associated with extending such methods to real-time brain imaging is the need for adequate stopping criteria, an aspect of Bayesian optimization which has received limited attention. In light of high scanning costs and limited attentional capacities of subjects an accurate and reliable stopping criteria is essential. In order to address this issue we propose and empirically study the performance of two stopping criteria., Comment: Oral presentation at MLINI 2015 - 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner

A standard approach in functional neuroimaging explores how a particular cognitive task activates a set of brain regions (one task-to-many regions mapping). Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system (many tasks-to-region mapping). In our work, presented here we propose an alternative framework, the Automatic Neuroscientist, which turns the typical fMRI approach on its head. We use real-time fMRI in combination with state-of-the-art optimisation techniques to automatically design the optimal experiment to evoke a desired target brain state. Here, we present two proof-of-principle studies involving visual and auditory stimuli. The data demonstrate this closed-loop approach to be very powerful, hugely speeding up fMRI and providing an accurate estimation of the underlying relationship between stimuli and neural responses across an extensive experimental parameter space. Finally, we detail four scenarios where our approach can be applied, suggesting how it provides a novel description of how cognition and the brain interrelate., Comment: 22 pages, 7 figures, work presented at OHBM 2015

We report on the coherent control and transport of indirect exciton (IX) spins in GaAs double quantum well (DQW) nanostructures. The spin dynamics was investigated by optically generating spins using a focused, circularly polarized light spot and by probing their spatial distribution using spatially and polarization resolved photoluminescence spectroscopy. Optically injected exciton spins precess while diffusing over distances exceeding 20 {\mu}m from the excitation spot with a spatial precession frequency that depends on the spin transport direction as well as on the bias applied across the DQW structure. This behavior is attributed to the spin precession in the effective magnetic field induced by the spin-orbit interaction. From the dependence of the spin dynamics on the transport direction, bias and external magnetic fields we directly determined the Dresselhaus and Rashba spin splitting coefficients for the structure. The precession dynamics is essentially independent on the IX density, thus indicating that the long spin lifetimes are not associated with IX collective effects. The latter, together with the negligible contribution of holes to the spin dynamics, are rather attributed to spatial separation of the electron and hole wave functions by the electric field, which reduces the electron-hole exchange interaction. Coherent spin precession over long transport distances as well as the control of the spin vector using electric and magnetic fields open the way for the application of IX spins in the quantum information processing.

Our recent paper on the "Search for Neutron Flux Generation in a Plasma Discharge Electrolytic Cell" [1] has as main goal the validation of the experiment in Ref.[2]. As a follow-up, Ref.[3] moves a set of objections on our procedure and presents argumentations on why the experiments should not yield the same results. We collect here additional material and calculations that contribute to understanding the observed discrepancies. Furthermore we prove that the absence of signals from Indium activation detectors reported also for the experiment of Ref.[2] is a clear indication that neutron production does not occur. [1] R.Faccini et al arXiv:1310.4749 [2] D.Cirillo et al, Key Engineering Materials 495, 104 (2012). [3] A.Widom et al. arXiv:1311.2447, Comment: 5 pages, 3 figures

We report on the modulation of indirect excitons (IXs) as well as their transport by moving periodic potentials produced by surface acoustic waves (SAWs). The potential modulation induced by the SAW strain modifies both the band gap and the electrostatic field in the quantum wells confining the IX, leading to changes in their energy. In addition, this potential capture and transports IXs over several hundreds of {\mu}m. While the IX packets keep to a great extent their spatial shape during transport by the moving potential, the effective transport velocity is lower than the SAW group velocity and increases with the SAW amplitude. This behavior is attributed to the capture of IXs by traps along the transport path, thereby increasing the IX transit time. The experimental results are well-reproduced by an analytical model for the interaction between trapping centers and IXs during transport.

Excitons, quasi-particles consisting of electron-hole pairs bound by the Coulomb interaction, are a potential medium for processing of photonic information in the solid-state. Information processing via excitons requires efficient techniques for the transport and manipulation of these uncharged particles. We introduce here a novel concept for the interconnection of multiple remote exciton systems based on the long-range transport of dipolar excitons by a network of configurable interconnects driven by acoustic wave beams. By combining this network with electrostatic gates, we demonstrate an integrated exciton multiplexer capable of interconnecting, gating and routing exciton systems separated by millimeter distances. The multiplexer provides a scalable platform for the manipulation of exciton fluids with potential applications in information processing.

Following some recent unexpected hints of neutron production in setups like high-voltage atmospheric discharges and plasma discharges in electrolytic cells, we present a measurement of the neutron flux in a configuration similar to the latter. We use two different types of neutron detectors, poly-allyl-diglicol-carbonate (PADC, aka CR-39) tracers and Indium disks. At 95% C.L. we provide an upper limit of 1.5 neutrons cm^-2 s^-1 for the thermal neutron flux at ~5 cm from the center of the cell. Allowing for a higher energy neutron component the largest allowed flux is 64 neutrons cm^-2 s^-1. This upper limit is two orders of magnitude smaller than what previously claimed in an electrolytic cell plasma discharge experiment. Furthermore the behavior of the CR-39 is discussed to point our possible sources of spurious signals., Comment: 4 pages, 3 figures

In the compagnion paper [Marginal density expansions for diffusions and stochastic volatility, part I] we discussed density expansions for multidimensional diffusions $(X^1,...,X^d)$, at fixed time $T$ and projected to their first $l$ coordinates, in the small noise regime. Global conditions were found which replace the well-known "not-in-cutlocus" condition known from heat-kernel asymptotics. In the present paper we discuss financial applications; these include tail and implied volatility asymptotics in some correlated stochastic volatility models. In particular, we solve a problem left open by A. Gulisashvili and E.M. Stein (2009)., Comment: to appear in Comm. Pure Appl. Math

Density expansions for hypoelliptic diffusions $(X^1,...,X^d)$ are revisited. In particular, we are interested in density expansions of the projection $(X_T^1,...,X_T^l)$, at time $T>0$, with $l \leq d$. Global conditions are found which replace the well-known "not-in-cutlocus" condition known from heat-kernel asymptotics. Our small noise expansion allows for a "second order" exponential factor. As application, new light is shed on the Takanobu--Watanabe expansion of Brownian motion and Levy's stochastic area. Further applications include tail and implied volatility asymptotics in some stochastic volatility models, discussed in a compagnion paper., Comment: 2 figures; to appear in Comm. Pure Appl. Math