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The emerging paradigm of the modern city demands big data analytics in urban studies. Data mining techniques for big data have the potential to ascertain strategic insights for current and future c...

This paper explains the effects of nonideal circuit elements upon the performance of analog and sampled data adaptive filters. It is shown that the effects in case of analog implementations differ significantly from that encountered in ideal case. Moreover, it is shown that effects of nonzero mean errors, such as offset voltages and nonlinearities of the input multipliers contribute n excess mean square error which is inversely convergence of the updating algorithm. To overcome most of these errors, solutions are presented and discussed.

The microseism is the strongest component of background seismic noise that masks seismic signals recorded by ocean-bottom seismographs (OBSs). Such undesired noise hampers the identification of critical seismic phases and sometimes even the entire waveform. Here, we introduce the data adaptive polarization filter (DAPF), an approach that suppresses random signals from the background seismic-noise significantly to overcome such difficulties. To automate this task, we have developed a self-contained software suite—DAPF-v1, supported by a MATLAB graphical user interface. The polarization filter is constructed from the data spectral density matrices of seismogram segments employing multitaper spectral analysis approach. We demonstrate a successful application of this technique to the OBSs deployed in the Indian Ocean. Our results confirm substantially enhanced signal-to-noise ratio after application of DAPF. The application of this technique has extensive implications for seismological studies particularly those aimed at understanding deep mantle dynamics, in which phase identification and qualitative waveform recovery are crucial yet challenging.

A method to predict time-series using multiple deep learners and a Bayesian network is proposed. In this study, the input explanatory variables are Bayesian network nodes that are associated with learners. Training data are divided using K-means clustering, and multiple deep learners are trained depending on the cluster. A Bayesian network is used to determine which deep learner is in charge of predicting a time-series. We determine a threshold value and select learners with a posterior probability equal to or greater than the threshold value, which could facilitate more robust prediction. The proposed method is applied to financial time-series data, and the predicted results for the Nikkei 225 index are demonstrated., Comment: 14 pages, 12 tables and 4 figures, Submitted to Neural Computing and Applications

When fitting a model to a data set, the goal is to create a model that captures the trends present in the data. However, data often contains regions where the underlying model changes or exhibits shifts in certain parameters due to economic events. These locations in the data are known as changepoints, and ignoring them can result in high error and incorrect forecasts. By developing a specific cost function and optimizing using the genetic algorithm, we are able to locate and account for the changepoints in a given data set. We specifically apply this process to the retail sales of electricity in the United States by examining data sets from each state's residential, commercial, and industrial sectors. We demonstrate that, when changepoints are accounted for, model trends can be computed more accurately. We specifically explore this in the case of data sets that exhibit changepoints due to the 2020 (and ongoing) pandemic.

Microwave imaging (MI) is suited to monitoring breast health due to its safety and low cost. A microwave imaging transmission system (MITS) is being developed at the University of Calgary to measure and monitor the permittivity of tissue over time. This preliminary study presents an adaptive filtering technique to remove higher frequency artifacts, likely caused by incomplete contact between the skin and the antennas. This work investigates the impact of the filtering method on scans of two volunteers taken at different time points. As good contact between antennas and tissue is achieved for these scans, small changes are anticipated after application of the filtering strategy. Results show that the consistency of signals, permittivity estimates, and images over time is high, before and after filtering, for both volunteers. This preliminary study suggests that the filtering approach has potential for removing artifacts at higher frequencies, while preserving the integrity of clean signals.

In this paper, a sampled-data adaptive output feedback controller is proposed for a class of uncertain nonlinear systems with unmeasured states, unknown dynamics and unknown time-varying external d...

Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from the power system operators. This paper proposes a novel data-adaptive robust optimization method for the economic dispatch of active distribution network with renewables. The scenario-generation method and the two-stage robust optimization are combined in the proposed method. To reduce the conservativeness, a few extreme scenarios selected from the historical data are used to replace the conventional uncertainty set. The proposed extreme-scenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all the possible scenarios if they hold in the selected extreme scenarios, which guarantees the robustness of the decision. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm with the selected uncertainty set is less conservative but equally as robust as the existing two-stage robust optimization approaches. This leads to the improved economy of the decision with uncompromised security.

Understanding the limitation of solely relying on statistical significance, researchers have proposed methods to draw biomedical conclusions based on clinical significance. The minimal clinically important significance is one of the most fundamental concepts to study clinical significance. Based on an anchor question usually available in the patients' reported outcome, Hedayat et al. presented a method to estimate minimal clinically important significance using the classification technique. However, their method implicitly requires that the binary outcome of the anchor question is equally likely, i.e. the balanced outcome assumption. This assumption cannot be guaranteed a priori when one designs the study; hence, it cannot be satisfied in general. In this paper, we propose a data adaptive method, which can overcome this limitation. Compared to Hedayat et al., our method uses a faster gradient based algorithm and adopts a more flexible structure of the minimal clinically important significance at the individual level. We conduct comprehensive simulation studies and apply our method to the chondral lesions and meniscus procedure study to demonstrate its usefulness and also its outperformance.

Many changepoint detection procedures rely on the estimation of nuisance parameters (like long-run variance). If a change has occurred, estimators might be biased and data adaptive rules for the choice of tuning parameters might not work as expected. If the data are not stationary, this becomes more challenging. The aim of this paper is to present two changepoint tests, which involve neither nuisance nor tuning parameters. This is achieved by combing self-normalization and wild bootstrap. We investigate the asymptotic behavior and show the consistency of the bootstrap under the hypothesis as well as under the alternative, assuming mild conditions on the weak dependence of the time series. As a by-product, a changepoint estimator is introduced and its consistency is proved. The results are illustrated through a simulation study. The new completely data-driven tests are applied to real data examples from finance and hydrology.

Geometric seismic attributes such as coherence are routinely used for highlighting geologic features such as faults and channels. Traditionally, we use a single user-defined analysis window of fixed size to calculate attributes for the entire seismic volume. In general, smaller windows produce sharper geologic edges, but they are more sensitive to noise. In contrast, larger windows reduce the effect of random noise, but they might laterally smear faults and channel edges and vertically mix the stratigraphy. The vertical and lateral resolutions of a 3D seismic survey change with depth due to attenuation losses and velocity increase, such that a window size that provides optimal images in the shallower section is often too small for the deeper section. A common workaround to address this problem is to compute the seismic attributes using a suite of fixed windows and then splice the results at the risk of reducing the vertical continuity of the final volume. Our proposed solution is to define laterally and vertical smoothly varying analysis windows. The construction of such tapered windows requires a simple modification of the covariance matrix for eigenstructure-based coherence and a less obvious, but also simple, modification of semblance-based coherence. We determine the values of our algorithm by applying it to a vintage 3D seismic survey acquired offshore Louisiana, USA.

This article introduces a data-adaptive nonparametric approach for the estimation of time-varying spectral densities from nonstationary time series. Time-varying spectral densities are commonly estimated by local kernel smoothing. The performance of these nonparametric estimators, however, depends crucially on the smoothing bandwidths that need to be specified in both time and frequency direction. As an alternative and extension to traditional bandwidth selection methods, we propose an iterative algorithm for constructing localized smoothing kernels data-adaptively. The main idea, inspired by the concept of propagation-separation, is to determine for a point in the time-frequency plane the largest local vicinity over which smoothing is justified by the data. By shaping the smoothing kernels nonparametrically, our method not only avoids the problem of bandwidth selection in the strict sense but also becomes more flexible. It not only adapts to changing curvature in smoothly varying spectra but also adjusts for structural breaks in the time-varying spectrum. Supplementary materials, including the R package tvspecAdapt containing an implementation of the routine, are available online.

We develop a novel Adaptive Elastic Net (AEN) modelling using a new covariance-regularization approach via the Hybridized Smoothed Covariance Estimators (HSCEs) to identify and select the best subs...

The robustification parameter, which balances bias and robustness, has played a critical role in the construction of sub-Gaussian estimators for heavy-tailed and/or skewed data. Although it can be tuned by cross-validation in traditional practice, in large scale statistical problems such as high dimensional covariance matrix estimation and large scale multiple testing, the number of robustification parameters scales with the dimensionality so that cross-validation can be computationally prohibitive. In this paper, we propose a new data-driven principle to choose the robustification parameter for Huber-type sub-Gaussian estimators in three fundamental problems: mean estimation, linear regression, and sparse regression in high dimensions. Our proposal is guided by the non-asymptotic deviation analysis, and is conceptually different from cross-validation which relies on the mean squared error to assess the fit. Extensive numerical experiments and real data analysis further illustrate the efficacy of the proposed methods

Data Augmented Design (DAD) can be applied to the redevelopment-oriented urban design, which helps find problems via an existing condition analysis based on multidimensional data, and quantitatively evaluate the implementation of designs after reconstruction. Based on this framework, a data adaptive urban design solution for redevelopment-oriented urban space is proposed in this chapter. It integrates space measurement and feedback into the process of urban design, which will convert the long-term design evaluation into short-term spatial intervention. Through establishing ICT infrastructures on a site, the feedback from precise and customized big data can be applied to promote the interaction between urban space and social activities. This chapter displays a series of quantitative methods including the workflow and tools to analyze opportunities and challenges of the design site. To better describe the framework, we take the design in Shanghai Hengfu historical district as a case study to depict the application of DAD in redevelopment-oriented urban design.

Pain is one of the most complicated and subjective sensation. As of now, doctors need to verbally communicate with the subjects for assessment of pain. In many cases, proper assessment of pain is not possible, especially for infants, deaf and dumb subjects, unconscious subjects etc. It is reported that pain stimulus excites the sympathetic nervous system (SNS) that changes the activity of few bio-signals that are directly related to SNS activities and the facial expression as well. In this present work, EMG of zygomaticus muscle which is related to change in facial expression is analyzed. Considering non-stationary nature of the EMG signal stimulated through pain, Empirical Mode Decomposition method is applied on EMG for its data adaptive nature. Standard Logistic Regression classifier is used on selected IMFs to classify between ‘no pain’ and different pain condition. Gradual increase in classification accuracy is noticed with the increase of pain intensity.

This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local estimation and the combining of explicit modeling with purely data adaptive inference. Then, we discuss stepwise approaches with different ways of integrating machine learning. Furthermore, for the modeling of prior knowledge, we introduce classes of distribution families for financial data. The proposed procedures are illustrated with data on stock returns for five companies of the Spanish value-weighted index IBEX35. The authors thank the Institute and Faculty of Actuaries in the U.K. for funding their research through the grant “Minimizing Longevity and Investment Risk while Optimizing Future Pension Plans” and the Spanish Ministerio de Economía y Competitividad, Project ECO2016-76203-C2-1-P, for partial support of this work.

Summarization: Nowadays the use of Ground Penetrating Radar (GPR) in archaeology has spread widely, indicating this method to be an effective means of detection and mapping of buried remains. However, some limitations have been reported by researchers and users, mainly related to the low resolution or penetration depth of the electromagnetic (EM) signal, especially in highly dispersive soils. In such a context, a robust and data adaptive processing scheme may increase the reliability of the interpretation of the GPR data. This work reports on a case study concerning a preliminary geophysical survey at an archaeological site with interest in the presence of a buried roman necropolis. The tests have been carried out by means of a ground-coupled dual-frequency GPR system, over a rectangular area defined by a regular grid with 1 m spacing interval between each scan line. A specific processing protocol has been applied to the collected dataset in order to maximize the interpretability of both the radargrams and the extracted depth slices. As a result, the analysis allowed to recognize two areas dominated by regular flooring whereas, at shallower levels, it permitted to detect a pattern of walls. Presented on

SUMMARYMeasurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter.In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability.We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements.Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.

With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition-driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and the geologic target of interest. However, such an approach may overlook unsuspected or subtle features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of candidate attributes that best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on their response from the unsupervised learning algorithm and the interpreter’s knowledge. In other words, we expect the weights to represent “which attribute is ‘favored’ by an interpreter as input for unsupervised learning” from an interpretation perspective and “which attribute is ‘favored’ by the learning algorithm” from a data-driven perspective. Therefore, we claim the weights are user guided and data adaptive, as the derivation of weight for each input attribute is embedded into the learning algorithm, providing a specific measurement tailored to the selected learning algorithm, while still taking the interpreter’s knowledge into account. We develop our workflow using Barnett Shale surveys and an unsupervised self-organizing map seismic facies analysis algorithm. We found that the proposed weighting-based attribute selection method better differentiates features of interest than using equally weighted input attributes. Furthermore, the weight values provide insights into dependency among input attributes.

This paper investigates the sampled-data adaptive control problem for a class of nonlinear systems with prescribed performance. The radial basis function neural networks are employed to approximate the unknown function in controller design procedure. The sampled-data controller and adaptive laws are designed by backstepping design technique, and an explicit formula for the allowable sampling period is derived. The proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the system output satisfies the prescribed performance. Finally, two examples are given to illustrate the effectiveness of the proposed approach.

The properties of directional distributions in ocean wave spectra are studied, with an emphasis on sea states with bimodal directional distributions in the high-frequency tails of single-peaked wave systems. A peak-splitting tendency has been a challenge in the interpretation of results from some data-adaptive estimation methods. After a survey of the theory, mathematical and numerical explanations are presented regarding domains of uni- and bimodality for symmetric Burg and Shannon maximum entropy methods. The study finds that both the Burg and Shannon maximum entropy methods have a tendency to split peaks, and that the domains of uni- and bimodality for these two methods depend on the Fourier coefficients input into the algorithms. Comparisons of data-adaptive methods based on data collected near the Ekofisk oil field in the North Sea and from nonlinear wave simulations are presented. The maximum likelihood (ML) method, the iterative maximum likelihood (IML) method, and the Burg and Shannon maximum entropy methods are applied. A large fraction of the directional wave spectra from Ekofisk shows bimodal features for distributions above the spectral peak for all of the abovementioned methods. In particular, strong similarity in bimodal features between the iterative maximum likelihood and the Burg maximum entropy methods are found. In general, the bimodality is consistent with previous observations, and it seems to be associated with wave and spectral development owing to nonlinear wave–wave interactions rather than being associated with the peak-splitting tendency in the estimates from any of the algorithms. The bimodal directional distributions were sometimes persistent and sometimes formed or decayed within the order of hours. This research was originally published in Journal of Atmospheric and Oceanic Technology. © 2018 American Meteorological Society

Rainfall exhibits nonlinear and non-stationary characteristic such that analysis done using linear methods are inconclusive. It is, therefore, necessary to understand the multi-scale variations of the rainfall. In this study, a comparison is done of two different regions which receive two distinct rainfall types. Northeastern to southwestern South Africa receives austral winter rainfall whilst the rest of the country receives austral summer rainfall. The multi-scale rainfall variability is done using data adaptive method, ensemble empirical mode decomposition. Changes of rainfall in both regions exhibited inter-annual and quasi-decadal oscillation. We found that there were distinctive spatial differences in the patterns.

Motivation Batch effects are one of the major source of technical variations that affect the measurements in high-throughput studies such as RNA sequencing. It has been well established that batch effects can be caused by different experimental platforms, laboratory conditions, different sources of samples and personnel differences. These differences can confound the outcomes of interest and lead to spurious results. A critical input for batch correction algorithms is the knowledge of batch factors, which in many cases are unknown or inaccurate. Hence, the primary motivation of our paper is to detect hidden batch factors that can be used in standard techniques to accurately capture the relationship between gene expression and other modeled variables of interest. Results We introduce a new algorithm based on data-adaptive shrinkage and semi-Non-negative Matrix Factorization for the detection of unknown batch effects. We test our algorithm on three different datasets: (i) Sequencing Quality Control, (ii) Topotecan RNA-Seq and (iii) Single-cell RNA sequencing (scRNA-Seq) on Glioblastoma Multiforme. We have demonstrated a superior performance in identifying hidden batch effects as compared to existing algorithms for batch detection in all three datasets. In the Topotecan study, we were able to identify a new batch factor that has been missed by the original study, leading to under-representation of differentially expressed genes. For scRNA-Seq, we demonstrated the power of our method in detecting subtle batch effects. Availability and implementation DASC R package is available via Bioconductor or at https://github.com/zhanglabNKU/DASC. Supplementary information Supplementary data are available at Bioinformatics online.

This article studies a problem of predicting seasonal precipitation over East Asia from real observations and multi-model ensembles. Classical model output statistics approach based on principal component analysis (PCA) has been widely used for climate prediction. However, it may not be efficient in predicting precipitation since PCA assumes that information of data should be retained by the second moment of them, which is too stringent to climate data that can be skewed or asymmetric. This article presents a method based on data-adaptive PCA (DPCA) by Lim and Oh (2016) that can adapt to non-Gaussian distributed data. In addition to investigate the utility of DPCA for climate study, we propose a data-adaptive principal component regression for seasonal precipitation prediction, which consists of DPCA and a regularized regression technique that is able to handle high-dimensional data. We apply the proposed method to nine general circulation models for prediction of precipitations on the summer season (June, July, and August). The prediction ability of the proposed method is evaluated in comparison with observations and model outputs (prediction) of each constituent model.

This paper presents an advanced approach to HDR/WCG video coding developed at FastVDO called FVHDR, and built on top of the Versatile Video Coding (VVC) VTM-5.0 test model of the Joint Video Exploration Team, a joint committee of ITU|ISO/IEC. A fully automatic adaptive video process that differs from a known HDR video processing chain (analogous to HDR10, and herein called “anchor”), is used. FVHDR works entirely within the framework of the VVC software model but adds additional tools. These tools can become an integral part of a future video coding standard or be extracted as additional pre- and post-processing chains. Reconstructed video sequences using FVHDR show an improved subjective visual quality to the output of the anchor. Moreover, the resultant SDR content generated by the data adaptive grading process is backward compatible.

This paper proposes a data-adaptive packing method for compression of dynamic point cloud sequences. Due to the increasing popularity in emerging immersive applications, the interest in representing the virtual world with dynamic point cloud sequences has never been higher. However, it remains a challenging work to compress these sequences with high efficiency. The proposed method divides the input sequences into some groups adaptively, in which patches in all frames are packed in a consistent way. Thus, the occupancy maps as well as the corresponding depth and texture images, are highly consistent both in temporal and spatial, which benefits the subsequent 2D video encoding greatly. Experimental results show that the proposed method achieves significant improvement in coding efficiency over the reference method of MPEG Point Cloud Compression (PCC).

Genome-wide sequencing enables evaluation of associations between traits and combinations of variants in genes and pathways. But such evaluation requires multi-locus association tests with good power, regardless of the variant and trait characteristics. And since analyzing families may yield more power than analyzing unrelated individuals, we need multi-locus tests applicable to both related and unrelated individuals. Here we describe such tests, and we introduce SKAT-X, a new test statistic that uses genome-wide data obtained from related or unrelated subjects to optimize power for the specific data at hand. Simulations show that: a) SKAT-X performs well regardless of variant and trait characteristics; and b) for binary traits, analyzing affected relatives brings more power than analyzing unrelated individuals, consistent with previous findings for single-locus tests. We illustrate the methods by application to rare unclassified missense variants in the tumor suppressor gene BRCA2, as applied to combined data from prostate cancer families and unrelated prostate cancer cases and controls in the Multi-ethnic Cohort (MEC). The methods can be implemented using open-source code for public use as the R-package GATARS (Genetic Association Tests for Arbitrarily Related Subjects) .

The OCT imaging produces speckle noise due to multiple forward and backward scattered waves. The most important step of OCT preprocessing is noise reduction, which is helpful for more sophisticated processes like segmentation. Three OCT despeckling and enhancement methods are presented in this chapter, which are based on statistical modeling, data adaptive and non data adaptive transform based models, respectively.

This paper proposes a model-free and data-adaptive feature screening method for ultra-high dimensional datasets. The proposed method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model and applies to the data in the presence of heavy-tailed errors and multivariate response. It enjoys both sure screening and rank consistency properties under weak assumptions. Further, a two-step approach is proposed to control the false discovery rate (FDR) in feature screening with the help of knockoff features. It can be shown that the proposed two-step approach enjoys both sure screening and FDR control if the pre-specified FDR level $\alpha$ is greater or equal to $1/s$, where $s$ is the number of active features. The superior empirical performance of the proposed methods is justified by various numerical experiments and real data applications.

In recent years, 3D sensing and capture technologies have made constant progress, leading to point clouds with higher resolution and fidelity. Since most applications demand compact storage and fast transmission, the issue of how to compress point clouds efficiently becomes an intractable problem. While previous GFT-based solutions use the transform tool to decorrelate attributes directly, ignoring the overall attribute’s data spatial redundancy, Graph Fourier Transform (GFT) has shown good performance on point cloud attribute compression. So, motivated by coding tools in traditional image and video coding, we propose a block-based data-adaptive intra prediction tool before graph transform processing to further reduce the redundancy. We adopt uniform quantizing and context-based arithmetic coding to get the final bitstream. Experimental results on different datasets demonstrate that our method improves the compression efficiency of other GFT-based schemes and has much better BD-BR performance than the state-of-the-art Region-Adaptive Hierarchical Transform (RAHT) approach on most specified point cloud contents.

With the increasing number of various types of high-resolution gravity observations, earth gravity models can be regionally refined. We use Abel-Poisson kernel to represent the gravity as the linear summation of finite radial basis functions and combine the multiple gravity data to build a regional gravity model with high resolution. The minimum root mean square criterion based on the data adaptive algorithm is proposed to calculate the base function, which promote the speed of computation significantly. Taking the central South China Sea as an example, two different types of gravity data, namely geoid undulations with resolution of 6'×6' and gravity anomaly with resolution of 2'×2', are used to construct the high-resolution regional gravity model. The model has a resolution of 2'×2', and has a great agreement with original gravity anomaly, reaching to ±0.8×10-5m/s2.Our results show that using radial basis functions to construct the regional gravity field can avoid the problem of slow convergence of spherical harmonic functions, and can improve the resolution remarkably.

Recent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency. These inferential targets are sometimes data-adaptive, in that they are dependent on the empirical distribution of the data. In this short note, we show that if researchers are willing to adapt the inferential target on the basis of efficiency, then extraordinary gains in precision can potentially be obtained. Specifically, when causal effects are heterogeneous, any asymptotically normal and root- n $n$ consistent estimator of the population average causal effect is superefficient for a data-adaptive local average causal effect.

The sea surface temperature (SST) of the marginal seas off the East coast of China is known for its large warming trend during the twentieth century. The station data in Taiwan is of particular importance as a reliable complementing measurement for SST. This paper analyzes the temperature from 1911 - 2010 at six stations using the data adaptive method Ensemble Empirical Mode Decomposition (EEMD). Multidecadal modes (period ＞ 40 years) in winter with the warm (1931 - 1950) and cold (1971 - 1980) phases are identified. In addition to the warm and cold phases, the 1951 - 1970 period is identified as the cooling phase, a transition phase from a warm to a cold regime, while the 1911 - 1930 and 1991 - 2010 periods are identified as the warming phase. The multidecadal variability in the Taiwan winter temperature is robust because it is rooted in the major multidecadal variability modes of the global SST, the Atlantic multidecadal oscillation (AMO), and the Pacific Decadal Oscillation (PDO). The centennial secular trend (ST) shows much larger warming rate than the linearly regressed. Because the multidecadal mode has been in a warming phase since 1991, the surpassed warming rate is likely due to a compensation effect to the decadal or sub-decadal scale cooling variations, while the possibility of anthropogenic influence cannot be ruled out.

This paper presents an advanced approach to HDR/WCG video coding developed at FastVDO called FVHDR, and built on top of the Versatile Video Coding (VVC) VTM1.0 test model of the Joint Video Exploration Team, a joint committee of ITU|ISO/IEC. A fully automatic adaptive video process that differs from a known HDR video processing chain (analogous to HDR10, and herein called “anchor”) developed recently in the standards committee JCTVC, is used. FVHDR works entirely within the framework of the VTM software model, but adds additional tools. These tools can become an integral part of a future video coding standard, or be extracted as additional pre- and post-processing chains. Reconstructed video sequences using FVHDR show an improved subjective visual quality to the output of the anchor. Moreover, the resultant SDR content generated by the data adaptive grading process is backward compatible.