Sequential Update Research Articles

Sequential Updating of Quantitative Requirements for Increased Flexibility in Robust Systems Design

AbstractIn early development stages of complex systems, interacting subsystems (including components) are often designed simultaneously by distributed teams with limited information exchange. Distributed development becomes possible by assigning teams independent design goals expressed as quantitative requirements equipped with tolerances to provide flexibility for design: so-called solution-spaces are high-dimensional sets of permissible subsystem properties on which requirements on the system performance are satisfied. Edges of box-shaped solution spaces are permissible intervals serving as decoupled (mutually independent) requirements for subsystem design variables. Unfortunately, decoupling often leads to prohibitively small intervals. In so-called solution-compensation spaces, permissible intervals for early-decision variables are increased by a compensation mechanism using late-decision variables. This paper presents a multi-step development process where groups of design variables successively change role from early-decision to late-decision type in order to maximize flexibility. Applying this to a vehicle chassis design problem demonstrates the effectiveness of the approach.

High Model Uncertainty and Approximating Combined Distributions

Some frameworks of model uncertainty combine a large number of densities. Computing the quantiles of the combined distribution might be practically infeasible when the number of densities is large. We introduce a numerical procedure that can reduce the computational burden. This consists of proving a sequential update formula that estimates different moments of the combined distribution and a finite moment problem that reconstructs the distribution from the moments. Based on an example on the determinants of long-run economic growth, we show that tracking the first four moments of the combined distribution along with generalized lambda distribution fit, provides a good approximation for the overall shape of the distributions.

Convergence Tests for Transdimensional Markov Chains in Geoscience Imaging

Classic inversion methods adjust a model with a predefined number of parameters to the observed data. With transdimensional inversion algorithms such as the reversible-jump Markov Chain Monte Carlo (rjMCMC), it is possible to vary this number during the inversion and to interpret the observations in a more flexible way. Geoscience imaging applications use this behaviour to automatically adjust model resolution to the inhomogeneities of the investigated system, while keeping the model parameters on an optimal level. The rjMCMC algorithm produces an ensemble as result, a set of model realizations which together represent the posterior probability distribution of the investigated problem. The realizations are evolved via sequential updates from a randomly chosen initial solution, and converge toward the target posterior distribution of the inverse problem. Up to a point in the chain, the realizations may be strongly biased by the initial model, and have to be discarded from the final ensemble. With convergence assessment techniques, this point in the chain can be identified. Transdimensional MCMC methods produce ensembles which are not suitable for classic convergence assessment techniques because of the changes in parameter numbers. To overcome this hurdle, three solutions are introduced to convert model realizations to a common dimensionality while maintaining the statistical characteristics of the ensemble. A scalar, a vector and a matrix representation for models is presented, inferred from tomographic subsurface investigations, and three classic convergence assessment techniques are applied on them. It is shown that appropriately chosen scalar conversions of the models could retain similar statistical ensemble properties as geologic projections created by rasterization.

Robust Online Multilabel Learning Under Dynamic Changes in Data Distribution With Labels.

In this paper, a robust online multilabel learning method dealing with dynamically changing multilabel data streams is proposed. The proposed method has three advantages: 1) higher accuracy due to a newly defined objective function based on labels ranking; 2) fast training and update based on a newly derived closed-form (rather than gradient descent based) solution for the new objective function; and 3) high robustness to a newly identified concept drift in multilabel data streams, namely, changes in data distribution with labels (CDDL). The high robustness benefits from two novel works: 1) a new sequential update rule that preserves the labels ranking information learned from all old (but discarded) samples while updating the model only based on new incoming samples and 2) a fixed threshold for label bipartition that is insensitive to any kind of changes in data distribution including CDDL. The proposed method has been evaluated over 13 benchmark datasets from various domains. As shown in the experimental results, the proposed work is highly robust to CDDL in both the sequential model update and multilabel thresholding. Furthermore, the proposed method improves the performance in different evaluation measures, including Hamming loss, F1-measure, Precision, and Recall while taking short training time on most evaluated datasets.

Sequential Big Data-Based Macroeconomic Forecast for Industrial Value Added

Macroeconomic situation is the overall performance of a country’s and regional economic situation. At present, the vast majority of macroeconomic indicators are obtained through sampling surveys, step-by-step reporting, statistical calculations, and other processes, which are publicly released by the Statistical Bureau. There are some shortcomings, such as lag and non-authenticity. Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs. However, the timeliness of data has a direct impact on government decision-making. In this paper, the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators (such as industrial value added above the scale of Hefei), which is different from the traditional time series prediction model such as ARIMA model. Based on the macroeconomic prediction model of time series big data, multi-latitude data sources, sequential update, verification set screening model and other strategies are used to provide more reliable, timely, and easy-to-understand forecasting values of national economic accounting indicators. At the same time, the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.

Analyzing and forecasting the Ebola incidence in North Kivu, the Democratic Republic of the Congo from 2018–19 in real time

During an Ebola virus disease (EVD) outbreak, the analysis and forecasting of the incidence in real time is challenged by reporting of cases, especially the reporting delay. It should be remembered that the latest count of cases is likely underestimated in real time, and moreover, the effective reproduction number, i.e. the average number of secondary cases generated by a single primary case at a given point in time, is also underestimated without proper adjustment. The present study aimed to adjust the reporting delay to appropriately estimate the latest incidence and obtain short-term forecasts from weekly reporting data of EVD in North Kivu, the Democratic Republic of the Congo (DRC). A semi-structured modeling approach was taken, accounting for reporting delay which can depend on time. The mean reporting delay was estimated at 11.6 days (95% CI: 11.3, 11.9) and the standard deviation was estimated to have changed from 26 November 2019 from 8.5–6.0 days. Nowcasting was successfully implemented by account for the time-dependent reporting delay: it mostly contained future observed values within the 95% confidence intervals, but there were failures when the reported incidence abruptly changed over time. Forecasting was also exercised in a similar manner to the nowcasting, while we imposed an extrapolation approach to the effective reproduction number for two future weeks. Moving average of the reproduction numbers for a few weeks prior the latest time of observation outperformed other extrapolations. The information that we can gain from real time (i.e. sequential) update of “situation report” can be considerably improved by integrating the proposed nowcasting and forecasting to the surveillance system.

Dirichlet Latent Variable Model: A Dynamic Model Based on Dirichlet Prior for Audio Processing

We propose a dynamic latent variable model for learning latent bases from time varying, non-negative data. We take a probabilistic approach to modeling the temporal dependence in data by introducing a dynamic Dirichlet prior—a Dirichlet distribution with dynamic parameters. This new distribution allows us to assure non-negativity and avoid intractability when sequential updates are performed otherwise encountered in using Dirichlet prior. We refer to the proposed model as the Dirichlet latent variable model DLVM. We develop an expectation maximization algorithm for the proposed model, and also derive a maximum a posteriori estimate of the parameters. Furthermore, we connect the proposed DLVM to two popular latent basis learning methods—probabilistic latent component analysis PLCA and non-negative matrix factorization NMF. We show that 1 PLCA is a special case of our DLVM, and 2 DLVM can be interpreted as a dynamic version of NMF. The usefulness of DLVM is demonstrated for three audio processing applications—speaker source separation, denoising, and bandwidth expansion. To this end, a new algorithm for source separation is also proposed. Through extensive experiments on benchmark databases, we show that the proposed model outperforms several relevant existing methods in all three applications.

Transition to Frozen Antiferromagnetic Pattern in Delayed Logistic Map

We study nonlinearly coupled logistic map with feedback in the limit of large delay time ([Formula: see text]). In the [Formula: see text]’s, the mapping of delayed dynamical systems to the spatially extended system was pursued in the context of optical systems by Arecchi and coworkers. We map the delayed system to a coupled map lattice of size [Formula: see text] with a sequential update and strictly one-sided coupling. Apart from aiding visualization, this formal equivalence helps us identify “phases” in a delayed system. One of the easily identified phases is that of frozen antiferromagnetic phase which is abundant in phase space. As coupling parameter ([Formula: see text]) increases, the system goes from periodic cascades along the coarse-grain region to chaos. We use the number of domain walls as an order parameter for locating this transition. The domain walls undergo random walk with drift and annihilate each other. At critical parameter value, we observe the power-law decay of this parameter. The decay exponents fall within the directed Ising universality class in general. We also find the escape time distribution, i.e. time required for reaching frozen antiferromagnetic regime. It diverges as it approaches the critical point. The divergence of average laminar length shows features indicating crisis induced intermittency.

Hyperuniformity and absorbing phase transition in continuous media with a conserved field

The nonequilibrium absorbing phase transition of interacting particles in two-dimensional continuous media is investigated via Monte Carlo simulations. Starting from random and natural homogeneous distributions of equi-sized spherical particles (disks) in an off-lattice continuous medium, particles that overlap with other particles are considered to be active and isolated particles are considered inactive. Active particles tend to repel overlapping particles. In a steady state, the particle distribution is found to be hyperuniform with a larger hyperuniformity exponent than the lattice Manna model. The critical exponents that characterize the power laws of the steady-state densities and the critical active-particle densities are calculated using both the sequential and parallel updates; they are found to be consistent, within the statistical margin of error, with those of the lattice Manna model but are distinctly different from those of the directed percolation universality class. The influence of impurities is also investigated by adding quenched point-impurity particles, and it is found that the impurity has a negligible effect on the critical behavior, supporting the universality between the continuum model and lattice Manna model. The results are in disagreement with the recent observations.

Information Fusion for Industrial Mobile Platform Safety via Track-Before-Detect Labeled Multi-Bernoulli Filter.

This paper presents a novel Track-Before-Detect (TBD) Labeled Multi-Bernoulli (LMB) filter tailored for industrial mobile platform safety applications. At the core of the developed solution is two techniques for fusion of color and edge information in visual tracking. We derive an application specific separable likelihood function that captures the geometric shape of the human targets wearing safety vests. We use a novel geometric shape likelihood along with a color likelihood to devise two Bayesian updates steps which fuse shape and color related information. One approach is sequential and the other is based on weighted Kullback–Leibler average (KLA). Experimental results show that the KLA based fusion variant of the proposed algorithm outperforms both the sequential update based variant and a state-of-art method in terms of the performance metrics commonly used in computer vision literature.

Correction to: A sequential update algorithm for computing the stationary distribution vector in upper block-Hessenberg Markov chains

The original version of this article unfortunately contained the following errors: In Introduction, below Eq. (1.3), the sentence should read “From (1.2) and (1.3), we have” instead of “From (1.2) and (1.6), we have”.

Sequential hypothesis tests for streaming data via symbolic time-series analysis

This paper addresses sequential hypothesis testing for Markov models of time-series data by using the concepts of symbolic dynamics. These models are inferred by discretizing the measurement space of a dynamical system, where the system dynamics are approximated as a finite-memory Markov chain on the discrete state space. The study is motivated by time-critical detection problems in physical processes, where a temporal model is trained to make fast and reliable decisions with streaming data. Sequential update rules have been constructed for log-posterior ratio statistic of Markov models in the setting of binary hypothesis testing and the stochastic evolution of this statistic is analyzed. The proposed technique allows selection of a lower bound on the performance of the detector and guarantees that the test will terminate in finite time. The underlying algorithms are first illustrated through an example by numerical simulation, and are subsequently validated on time-series data of pressure oscillations from a laboratory-scale swirl-stabilized combustor apparatus to detect the onset of thermo-acoustic instability. The performance of the proposed sequential hypothesis tests for Markov models has been compared with that of a maximum-likelihood classifier with fixed sample size (i.e., sequence length). It is shown that the proposed method yields reliable detection of combustion instabilities with fewer observations in comparison to a fixed-sample-size test.

Automated Scheduling for Optimal Parallelization to Reduce the Duration of Vehicle Software Updates

The needs-oriented expansion and maintenance of vehicle functions through software updates will increase drastically due to current megatrends such as autonomous driving or connected car services. Both, shorter innovation cycles and increasing software volumes resulting from these megatrends, increase update frequency and duration equally. Consequently, a reduction of update duration will become even more important in the future. Popular literature has shown that in a vehicle-internal network of up to 100 electronic control units (ECUs), the parallelization of individual ECU updates has great potential to reduce the total update duration. However, the methods presented so far do not show an optimal parallel scheduling algorithm that uses the full potential of the vehicle-internal network. In addition, some of the approaches are also limited to certain bus systems or do not have an automated parallelization strategy based on ECU requirements. The approach introduced here uses a high abstraction model for optimal parallel ECU software update scheduling. For this purpose, we developed two scheduling algorithms, one as a mixed-integer linear program and the other as a hybrid algorithm. We verified our approach in a real testing environment with up to 20 ECUs regarding update duration and computation time. Evaluation shows that the reprogramming time can be reduced by up to 77% compared to sequential update processing through optimal parallelization. Furthermore, the model is very easy to adapt to different and new vehicle generations due to its high abstraction.

A sequential update algorithm for computing the stationary distribution vector in upper block-Hessenberg Markov chains

This paper proposes a new algorithm for computing the stationary distribution vector in continuous-time upper block-Hessenberg Markov chains. To this end, we consider the last-block-column-linearly-augmented (LBCL-augmented) truncation of the (infinitesimal) generator of the upper block-Hessenberg Markov chain. The LBCL-augmented truncation is a linearly-augmented truncation such that the augmentation distribution has its probability mass only on the last block column. We first derive an upper bound for the total variation distance between the respective stationary distribution vectors of the original generator and its LBCL-augmented truncation. Based on the upper bound, we then establish a series of linear fractional programming (LFP) problems to obtain augmentation distribution vectors such that the bound converges to zero. Using the optimal solutions of the LFP problems, we construct a matrix-infinite-product (MIP) form of the original (i.e., not approximate) stationary distribution vector and develop a sequential update algorithm for computing the MIP form. Finally, we demonstrate the applicability of our algorithm to BMAP/M/$\infty$ queues and M/M/$s$ retrial queues.

Online estimation for parameters and state-of-charge of LiMn2O2 batteries with a modified adaptive Kalman filter

The accurate estimation for parameters and state-of-charge (SOC) is very important for the state monitoring and optimization in battery management system. This paper introduces an online estimation method with modified Kalman filter, where two contributions are added comparing related battery-application literature: (1) In order to improve the robustness to systematic noise brought by varying current loads and condition, an adaptive law is introduced to calibrate the noise covariance recursively. (2) To enhance the numerical stability over covariance propagating, a modified square-root procedure is derived by using sequential measurement update and the Potter algorithm. Besides, a test bench with a series-connected battery pack of 10 LiMn2O2 cells is used to evaluate the algorithm. The experimental results indicate both a better precision comparing the offline result, and the maximum error for the overall SOC is less than 2%.

Attractor Stability in Finite Asynchronous Biological System Models.

We present mathematical techniques for exhaustive studies of long-term dynamics of asynchronous biological system models. Specifically, we extend the notion of [Formula: see text]-equivalence developed for graph dynamical systems to support systematic analysis of all possible attractor configurations that can be generated when varying the asynchronous update order (Macauley and Mortveit in Nonlinearity 22(2):421, 2009). We extend earlier work by Veliz-Cuba and Stigler (J Comput Biol 18(6):783-794, 2011), Goles et al. (Bull Math Biol 75(6):939-966, 2013), and others by comparing long-term dynamics up to topological conjugation: rather than comparing the exact states and their transitions on attractors, we only compare the attractor structures. In general, obtaining this information is computationally intractable. Here, we adapt and apply combinatorial theory for dynamical systems from Macauley and Mortveit (Proc Am Math Soc 136(12):4157-4165, 2008. https://doi.org/10.1090/S0002-9939-09-09884-0 ; 2009; Electron J Comb 18:197, 2011a; Discret Contin Dyn Syst 4(6):1533-1541, 2011b. https://doi.org/10.3934/dcdss.2011.4.1533 ; Theor Comput Sci 504:26-37, 2013. https://doi.org/10.1016/j.tcs.2012.09.015 ; in: Isokawa T, Imai K, Matsui N, Peper F, Umeo H (eds) Cellular automata and discrete complex systems, 2014. https://doi.org/10.1007/978-3-319-18812-6_6 ) to develop computational methods that greatly reduce this computational cost. We give a detailed algorithm and apply it to(i) thelac operon model forEscherichia coli proposed by Veliz-Cuba and Stigler (2011), and(ii) the regulatory network involved in the control of the cell cycle and cell differentiation in theCaenorhabditis elegans vulva precursor cells proposed by Weinstein et al.(BMC Bioinform 16(1):1, 2015). In both cases, we uncover all possible limit cycle structures for these networks under sequential updates. Specifically, for the lac operon model, rather than examining all[Formula: see text] sequential update orders, we demonstrate that it is sufficient to consider344 representative update orders, and, more notably, that these344 representatives give rise to 4 distinct attractor structures. A similar analysis performed for the C.elegans model demonstrates that it has precisely125 distinct attractor structures. We conclude with observations on the variety and distribution of the models' attractor structures and use the results to discuss their robustness.

Sequential Bayesian updating as a model for human perception.

Sequential Bayesian updating has been proposed as model for explaining various systematic biases in human perception, such as the central tendency, range effects, and serial dependence. The present chapter introduces to the principal ideas behind Bayesian updating for the random-change model introduced previously and shows how to implement sequential updating using the exact method via probability distributions, the Kalman filter for Gaussian distributions, and a particle filter for approximate sequential updating. Finally, it is demonstrated how to couple perception to action by selecting an appropriate action based on the posterior distribution that results from sequential updating.

Sex-based differences in right dorsolateral prefrontal cortex roles in fairness norm compliance

Social norms are a common motivator or de-motivator of behavior through the need of humans to comply with acceptable behaviors of their social groups. Converging evidence from functional neuroimaging and noninvasive brain stimulation studies suggest that the right dorsolateral prefrontal cortex (rDLPFC) is involved in social norm compliance. Extending this view, we suggest that rDLPFC may not act uniformly when men and women face different types of fairness norm compliance situations, and that there may be a sequential update of norm compliance after experiencing punishment. Using High-Definition Transcranial Direct Current Stimulation (HD-tDCS) on 100 participants playing a sequence of economic game trials, the current study showed that (1) the right dorsolateral prefrontal cortex (rDLPFC) is involved in both voluntary and sanction-induced fairness norm compliance, (2) it has an opposite role in the two types of compliance, (3) there are sex-based differences in rDLPFC roles in fairness norm compliance, and (4) post-punishment fairness compliance is reduced compared to baseline.

ICON‐A, the Atmosphere Component of the ICON Earth System Model: I. Model Description

AbstractICON‐A is the new icosahedral nonhydrostatic (ICON) atmospheric general circulation model in a configuration using the Max Planck Institute physics package, which originates from the ECHAM6 general circulation model, and has been adapted to account for the changed dynamical core framework. The coupling scheme between dynamics and physics employs a sequential updating by dynamics and physics, and a fixed sequence of the physical processes similar to ECHAM6. To allow a meaningful initial comparison between ICON‐A and the established ECHAM6‐LR model, a setup with similar, low resolution in terms of number of grid points and levels is chosen. The ICON‐A model is tuned on the base of the Atmospheric Model Intercomparison Project (AMIP) experiment aiming primarily at a well balanced top‐of atmosphere energy budget to make the model suitable for coupled climate and Earth system modeling. The tuning addresses first the moisture and cloud distribution to achieve the top‐of‐atmosphere energy balance, followed by the tuning of the parameterized dynamic drag aiming at reduced wind errors in the troposphere. The resulting version of ICON‐A has overall biases, which are comparable to those of ECHAM6. Problematic specific biases remain in the vertical distribution of clouds and in the stratospheric circulation, where the winter vortices are too weak. Biases in precipitable water and tropospheric temperature are, however, reduced compared to the ECHAM6. ICON‐A will serve as the basis of further development and as the atmosphere component to the coupled model, ICON‐Earth system model (ESM).

Sequential Audio-Visual Correspondence With Alternating Diffusion Kernels

A fundamental problem in multimodal signal processing is to quantify relations between two different signals with respect to a certain phenomenon. In this paper, we address this problem from a kernel-based perspective and propose a measure that is based on affinity kernels constructed separately in each modality. This measure is motivated from both a kernel density estimation point of view of predicting the signal in one modality based on the other, as well as from a statistical model, which implies that high values of the proposed measure are expected when signals highly correspond to each other. Considering an online setting, we propose an efficient algorithm for the sequential update of the proposed measure, and demonstrate its application to eye-fixation prediction in audio-visual recordings. The goal is to predict locations within a video recording at which people gaze when watching the video. As studies in psychology imply, people tend to gaze at the location of the audio source, so that their prediction becomes equivalent to locating the audio source within the video. Therefore, we propose to predict eye-fixations as regions within the video with the highest correspondence to the audio signal, thereby demonstrating the improved performance of the proposed method.