Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Self-Calibration for Visual-Inertial Navigation: Models, Analysis, and Degeneracy

As sensor calibration plays an important role in visual-inertial sensor fusion, this article performs an in-depth investigation of online self-calibration for robust and accurate visual-inertial state estimation. To this end, we first conduct complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including inertial measurement unit (IMU)/camera intrinsics and IMU-camera spatial-temporal extrinsic calibration, along with readout time of rolling shutter (RS) cameras (if used). We study different inertial model variants containing intrinsic parameters that encompass most commonly used models for low-cost inertial sensors. With these models, the observability analysis of linearized VINS with full sensor calibration is performed. Our analysis theoretically proves the intuition commonly assumed in the literature—that is, VINS with full sensor calibration has four unobservable directions, corresponding to the system's global yaw and position, while all sensor calibration parameters are observable given fully excited motions. Moreover, we, for the first time, identify degenerate motion primitives for IMU and camera intrinsic calibration, which, when combined, may produce complex degenerate motions. We compare the proposed online self-calibration on commonly used IMUs against the state-of-art offline calibration toolbox Kalibr, showing that the proposed system achieves better consistency and repeatability. Based on our analysis and experimental evaluations, we also offer practical guidelines to effectively perform online IMU-camera self-calibration in practice.
Flexural Vibration Analysis of Nonuniform Double-Beam System with General Boundary and Coupling Conditions

In this paper, an analytical modeling approach for the flexural vibration analysis of the nonuniform double-beam system is proposed via an improved Fourier series method, in which both types of translational and rotational springs are introduced to account for the mechanical coupling on the interface as well as boundary restraints. Energy formulation is employed for the dynamic description of the coupling system. With the aim to treat the varying thickness across the beam in a unified pattern, the relevant variables are all expanded into Fourier series. Supplementary terms with the smoothed characteristics are introduced to the standard Fourier series for the construction of displacement admissible function for each beam. In conjunction with the Rayleigh–Ritz procedure, the transverse modal characteristics of nonuniform double-beam system can be obtained by solving a standard eigenvalue problem. Instead of solving the certain value of nonideal boundary conditions, the continuous spring stiffnesses of the boundary conditions are considered, and the rotational restrains are introduced in the coupling beam interface. Numerical results are then presented to demonstrate the reliability of the current model and study the influence of various parameters, such as taper ratio, boundary, and coupling strength on the free vibration characteristics, with the emphasis put on the rotational restraining coefficients on the beam interface. This work can provide an efficient modeling framework for the vibration characteristics study of the complex double-beam system, especially with arbitrary varying thickness and coupling stiffness.
A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Missing data, caused by many factors such as equipment short circuits or data cleaning, affect the accuracy of condition monitoring for rotating machinery. To improve the precision of missing data recovery, a compressed sensing-based vibration data repair method is developed. First, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then, a sparse representation of the vibration signal, through a discrete cosine transform, is utilized to make the noisy vibration signal sparse. Finally, the orthogonal matching pursuit algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is shown that the OMM has a higher repair precision at different loss rates.
Low threshold of a continuous-wave mode-locked and azimuthally polarized Nd:YVO4 laser with a semiconductor saturable absorber mirror

On the basis of the birefringence of laser crystals, a continuous-wave mode-locked Nd:YVO4 laser with azimuthal polarization was fabricated using a semiconductor saturable absorber mirror. When compared to the method based on thermally induced birefringence, the threshold was sufficiently lower than that in previous studies with about the one-tenth ratio of threshold. The repetition rate and pulse width for the continuous-wave mode-locked pulse were 125 MHz and 77 ps, respectively, at a pump power of 11 W. The degree of polarization was 90.0% ± 1.9%.
Water-table-driven greenhouse gas emission estimates guide peatland restoration at national scale

Abstract. The substantial climate change mitigation potential of restoring peatlands through rewetting and intensifying agriculture to reduce greenhouse gas (GHG) emissions is largely recognized. The green deal in Denmark aims at restoring 100 000 ha of peatlands by 2030. This area corresponds to more than half of the Danish peatland, with an expected reduction in GHG emissions of almost half of the entire land use, land use change and forestry (LULUFC) emissions. Recent advances established the functional relationship between hydrological regimes, i.e., water table depth (WTD), and CO2 and CH4 emissions. This builds the basis for science-based tools to evaluate and prioritize peatland restoration projects. With this article, we lay the foundation of such a development by developing a high-resolution WTD map for Danish peatlands. Further, we define WTD response functions (CO2 and CH4) fitted to Danish flux data to derive a national GHG emission estimate for peat soils. We estimate the annual GHG emissions to be 2.6 Mt CO2-eq, which is around 15 % lower than previous estimates. Lastly, we investigate alternative restoration scenarios and identify substantial differences in the GHG reduction potential depending on the prioritization of fields in the rewetting strategy. If wet fields are prioritized, which is not unlikely in a context of a voluntary bottom-up approach, the GHG reduction potential is just 30 % for the first 10 000 ha with respect to a scenario that prioritizes drained fields. This underpins the importance of the proposed framework linking WTD and GHG emissions to guide a spatially differentiated peatland restoration. The choice of model type used to fit the CO2 WTD response function, the applied global warming potentials and uncertainties related to the WTD map are investigated by means of a scenario analysis, which suggests that the estimated GHG emissions and the reduction potential are associated with coefficients of variation of 13 % and 22 %, respectively.
Developments in Survey Research over the Past 60 Years: A Personal Perspective

Many developments have occurred in the practice of survey sampling and survey methodology in the past 60 years or so. These developments have been partly driven by the emergence of computers and the continuous growth in computer power over the years and partly by the increasingly sophisticated demands from the users of survey data. The paper reviews these developments with a main emphasis on survey sampling issues for the design and analysis of social surveys. Design‐based inference based on probability samples was the predominant approach in the early years, but over time, that predominance has been eroded by the need to employ model‐dependent methods to deal with missing data and to satisfy analysts' demands for survey estimates that cannot be met with design‐based methods. With the continuous decline in response rates that has occurred in recent years, much current research has focused on the use of non‐probability samples and data collected from administrative records and web surveys.
Bicoherence analysis of nonstationary and nonlinear processes.

Bicoherence analysis is a well established method for identifying the quadratic nonlinearity of stationary processes. However, it is often applied without checking the basic assumptions of stationarity and convergence. The classic bicoherence, unfortunately, tends to give false positives -- high bicoherence values without actual nonlinear coupling of different frequency components -- for signals exhibiting rapidly changing amplitudes and limited length. The effect of false positive values can lead to misinterpretation of results, therefore a more prudent analysis is necessary in such cases. This paper analyses the properties of bispectrum and bicoherence in detail, generalizing these quantities to nonstationary processes. A step-by-step method is proposed to filter out false positives at a given confidence level for the case of nonstationary signals. We present a number of test cases, where the method is demonstrated on simple physics-based numerical systems. The approach and methodology introduced in the paper can be generalized to lower and higher order coherence calculations.
Outlier-resistant high-dimensional regression modelling based on distribution-free outlier detection and tuning parameter selection

ABSTRACT The -type regularization is a useful tool for high-dimensional regression modelling. Although the -type approaches perform well regression modelling, the methods suffer from outliers, since the -type approaches are based on non-robust methods (e.g. least squares loss function). In order to resolve the drawback, we propose a robust -type regularization method based on distribution-free outlier detection measure. We consider outlier detection in principal component spaces (PCSs) to overcome dimensionality problem of high-dimensional data, and propose a novel cut-off value based on a non-parametric test. By using the distribution-free outlier detection measure, we can effectively detect outliers in PCS without distribution assumption of the Mahalanobis distance. We then propose a robust -type regularization method via a weighted elastic net. The tuning parameter selection is a vital matter in -type regularized regression modelling, since choosing the tuning parameters can be seen as variable selection and model estimation. We derive an information criterion to select the tuning parameters of the proposed robust -type regularization method. Monte Carlo simulations and NCI60 data analysis show that the proposed robust regression modelling strategies effectively perform for high-dimensional regression modelling, even in the presence of outliers.
Actin Cytoskeleton Polymerization and Focal Adhesion as Important Factors in the Pathomechanism and Potential Targets of Mucopolysaccharidosis Treatment

The main approach used in the current therapy of mucopolysaccharidosis (MPS) is to reduce the levels of glycosaminoglycans (GAGs) in cells, the deposits considered to be the main cause of the disease. Previous studies have revealed significant differences in the expression of genes encoding proteins involved in many processes, like those related to actin filaments, in MPS cells. Since the regulation of actin filaments is essential for the intracellular transport of specific molecules, the process which may affect the course of MPSs, the aim of this study was to evaluate the changes that occur in the actin cytoskeleton and focal adhesion in cells derived from patients with this disease, as well as in the MPS I mouse model, and to assess whether they could be potential therapeutic targets for different MPS types. Western-blotting, flow cytometry and transcriptomic analyses were employed to address these issues. The levels of the key proteins involved in the studied processes, before and after specific treatment, were assessed. We have also analyzed transcripts whose levels were significantly altered in MPS cells. We identified genes whose expressions were changed in the majority of MPS types and those with particularly highly altered expression. For the first time, significant changes in the expression of genes involved in the actin cytoskeleton structure/functions were revealed which may be considered as an additional element in the pathogenesis of MPSs. Our results suggest the possibility of using the actin cytoskeleton as a potential target in therapeutic approaches for this disease.
A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors

Pedestrian dead reckoning (PDR) using inertial and magnetic measurement unit (IMMU) is a current research hotspot that can be used to provide location-based services. The main challenges of the self-contained sensor-based PDR method are mainly sensor errors and orientation estimation errors. In this paper, a novel orientation estimation algorithm and gait phase detection algorithm with strong adaptability are proposed. The variance and magnitude of the angular rate are adopted to detect the gait. The experimental results show that the gait phase detection method can accurately distinguish the swing phase and the stance phase of the foot even under running conditions. By reasonable assumptions, the attitude angles calculated in the swing phase can be calibrated while the attitude angles are in the stance phase. The calibration of velocity not only uses the zero velocity updates method but also makes reasonable assumptions about the bias error of the accelerometer, so that the velocity calculated during the whole swing phase can be calibrated. Experimental results demonstrate that the proposed self-contained IMMU is capable of providing consistent beacon-free PDR in different scenarios, achieving less than 1.2% average distance error and average end-to-end position error.
Multiresolution analysis signal in a three beam path Mach-Zehnder interferometer based on a discrete wavelet transform

In this work, we present an analytical methodology to evaluate the response of a three-beam path Mach-Zehnder interferometer (MZI) based on a multiresolution signal analysis using a discrete Wavelet transform (DWT). Here, the MZI was implemented by fusion splicing of a segment of ytterbium-doped double cladding photonic crystal fiber (Yb-d-DCPCF) between two pieces of single mode fibers. Moreover, Yb-d-DCPCF core and claddings were used as the arms of the MZI, while the hole collapsed regions acted as optical fiber couplers. So, the MZI interference pattern occurs of two main modal interferences, between the core and cladding. Hence, as a result, an interference spectrum with aperiodic sinusoidal waveform was observed. In this way, in our proposed methodology we decompose the interference spectrum into eight bands of spatial frequency (SF) using the DWT with a wavelet mother Daubechies order twenty (db20) for detecting the two main modal interferences. The spectral analysis of the decomposed signal reveals that the level 7 and 8 contain the two main modal interference between the core and the cladding 1 and cladding 2, respectively. Additionally, the fundamental mode is obtained in the level 8. In the same way, the decomposed signal provides information about the wavelength and spatial frequency simultaneously that can be used for knowing the energy spectral density. Finally, we claim that the proposed methodology provides the best accuracy in SF of MZI intermodal and DWT indicates the wavelength region in which the propagation modes occur, improving the description of the refractive index difference.
A novel scatterplot-based method to detect copy number variation (CNV)

Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters. Moreover, SNPs within a deletion are not heterozygous, whereas heterozygous SNPs within a duplication show typical 1:2 signal distribution between the alleles. Scatterplot-based CNV calls were compared with standard results of PennCNV analysis. All discordant calls as well as a random selection of 100 concordant calls were individually analyzed by visual inspection after noise-reduction. Results: An algorithm for the automated scatterplot visualization of CNVs was developed and used to analyze six known CNV regions. Use of scatterplots and PennCNV yielded 1019 concordant and 108 discordant CNV calls. All concordant calls were evaluated as true CNV-findings. Among the 108 discordant calls, 7 were false positive findings by the scatterplot method, 80 were PennCNV false positives, and 21 were true CNVs detected by the scatterplot method, but missed by PennCNV (i.e., false negative findings). Conclusion: CNV visualization by scatterplots allows for a reliable and rapid detection of CNVs in large studies. This novel method may thus be used both to confirm the results of genome-wide CNV detection software and to identify known CNVs in hitherto untyped samples.
A perspective of human development theory and its applications in Ethiopia: A systematic review

The pre-existing, profit-driven theories of international development gave rise to the movement for human-centered development. In this review, an extensive analysis of Ethiopian perspectives on the theory of human development and its implementation there was carried out for the first time. There has not previously been a review of the theories, techniques, and practices related to Ethiopian human development. The goal of this review was to examine Ethiopian human development concepts, theories, techniques, and practices in order to make recommendations on how to enhance both the current situation of human development and its potential applications in Ethiopia. Ethiopia therefore had a lower human development index (HDI) than sub-Saharan African nations and those with low HDI. When compared to other emerging nations, it also has lower averages for predicted school years, mean school years, and birth weight. Also, on average, its gross national product per person is lower. Except for pension programs for government and military employees, there is no comprehensive, publicly funded welfare system. Most people run the risk of going hungry because they do not have access to enough food to survive. The government will therefore be urged to adopt policies that will increase society’s output and productivity, as well as to implement human-centered development projects, a capability-based approach to development, a strategy for improving sustainable livelihoods, and stable environmental management.
Entropy as a measure of dependency for categorized data

Summary Data arranged in a two-way contingency table can be obtained as a result of many experiments in the life sciences. In some cases the categorized trait is in fact conditioned by an unobservable continuous variable, called liability. It may be interesting to know the relationship between the Pearson correlation coefficient of these two continuous variables and the entropy function measuring the corresponding relation for categorized data. After many simulation trials, a linear regression was estimated between the Pearson correlation coefficient and the normalized mutual information (both on a logarithmic scale). It was observed that the regression coefficients obtained do not depend either on the number of observations classified on a categorical scale or on the continuous random distribution used for the latent variable, but they are influenced by the number of columns in the contingency table. In this paper a known measure of dependency for such data, based on the entropy concept, is applied.
Completeness estimation model: Fault perspective

Reliability is an important software quality factor that is more effective at an in the development software. It is more effective in the case of object oriented design approaches. Completeness is a significant key attribute to reliability analysis. Its evaluations provided the maintainable and highly reliable software. Completeness is a reason of important impact to software developers. This paper evaluated on the need and importance of completeness for reliable software. A model has been proposed for completeness assessment with fault issues at design level by establishing multiple linear regressions. Finally the completeness model has been validated using experimental examine.
New Techniques for Learning and Inference in Bayesian Models

A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the best fit graphical model from raw data, while inference is the task of answering probabilistic queries for a known graphical model (for example what is the marginal distribution of one of the variables or what is the distribution of a subset of variables, after conditioning on the values of some other subset of variables). Learning can be thought of as finding a graphical model that explains the raw data, while the inference queries extract the knowledge the graphical model contains. This thesis introduces new provable techniques for performing both of these tasks, in the context of both latent-variable models – in which a portion of the variables in the graphical model are not observed, as well as fully-observable undirected graphical models (Markov Random Fields). Chapters 2 and 3 will focus on learning latent-variable models, while Chapter 4 will focus on inference in Markov Random Fields. In Chapter 2, I will contribute the first provable results for analyzing variational Bayes: a family of alternatingminimization style algorithms which is very popular in practice for learning latent-variable models. Despite it’s popularity with practitioners, the only theoretical guarantees prior to this work concerned convergence to local minima. We will prove that under reasonable assumptions, in the context of topic models, these algorithms will converge to the global minimum. Subsequently, in Chapter 3, we will use the method-of-moments along with new techniques in tensor decomposition and constrained matrix factorization to derive algorithms for learning noisy-OR networks – the textbook example of a probabilistic model for causal relationships. Importantly, these techniques were only applicable to linear latentvariable models – which noisy-OR is not. In Chapter 4, I will contribute a new understanding of a class of variational methods for calculating partition functions in Markov Random Fields. The key technical ingredient is a connection to convex programming hierarchies – a recent area of interest in combinatorial optimization, along with approximations of the entropy of a distribution based on low-order moment information.