Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Statistical Methods with R

INTRODUCTION MATHEMATICAL TOOLS OF ROBUSTNESS Statistical Model Illustration on Statistical Estimation Statistical Functional Fisher's Consistency Some Distances of Probability Measures Relations between Distances Differentiable Statistical Functionals Gateau Derivative Frechet Derivative Hadamard (Compact) Derivative Large Sample Distribution of Empirical Functional Computation and Software Notes Problems and Complements BASIC CHARACTERISTICS OF ROBUSTNESS Influence Function Discretized Form of Influence Function Qualitative Robustness Quantitative Characteristics of Robustness Based on Influence Function Maximum Bias Breakdown Point Tail-Behavior Measure of a Statistical Estimator Variance of Asymptotic Normal Distribution Problems and Complements ROBUST ESTIMATORS OF REAL PARAMETER Introduction M-Estimators M-Estimator of Location Parameter Finite Sample Minimax Property of M-Estimator Moment Convergence of M-Estimators Studentized M-Estimators L-Estimators Sequential M- and L-Estimators R-Estimators Numerical Illustration Computation and Software Notes Problems and Complements ROBUST ESTIMATORS IN LINEAR MODEL Introduction Least Squares Method M-Estimators GM-Estimators S-Estimators and MM-Estimators L-Estimators, Regression Quantiles Regression Rank Scores Robust Scale Statistics Estimators with High Breakdown Points One-Step Versions of Estimators Numerical Illustrations Computation and Software Notes Problems and Complements MULTIVARIATE LOCATION MODEL Introduction Multivariate M-Estimators of Location and Scatter High Breakdown Estimators of Multivariate Location and Scatter Admissibility and Shrinkage Numerical Illustrations and Software Notes Problem and Complements SOME LARGE SAMPLE PROPERTIES OF ROBUST PROCEDURES Introduction M-Estimators L-Estimators R-Estimators Interrelationships of M-, L-, and R-Estimators Minimaximally Robust Estimators Problems and Complements SOME GOODNESS-OF-FIT TESTS Introduction Tests of Normality of the Shapiro-Wilk Type with Nuisance Regression and Scale Parameters Goodness-of-Fit Tests for General Distribution with Nuisance Regression and Scale Numerical Illustration Computation and Software Notes APPENDIX A: R SYSTEM Brief R Overview REFERENCES SUBJECT INDEX AUTHOR INDEX
The Gut Microbiota Contributes to Systemic Responses and Liver Injury in Gut-Derived Sepsis

The gut microbiota, as a major source of opportunistic pathogens, poses a great threat to systemic infection, whereas the role of the gut microbiota in sepsis is underestimated. Here, we aimed to explore the effects of different gut microbiota patterns (namely, enterotypes) in cecal ligation and puncture (CLP)-induced murine sepsis. To achieve this purpose, we built four kinds of enterotypes by exposing mice to different types of antibiotics (azithromycin, amoxicillin, metronidazole, and levofloxacin). The results showed that antibiotic exposure induced different enterotypes, which, in turn, led to varying levels of systemic inflammation in septic mice, with amoxicillin-associated enterotypes exhibiting the most severe inflammation, followed by metronidazole, azithromycin, and levofloxacin. Specifically, the amoxicillin-associated enterotype was characterized by an abundance of intestinal opportunistic pathogens, including Enterobacteriaceae, Sutterellaceae, and Morganellaceae. This enterotype played a significant role in promoting the pathogenic potential of the gut microbiota, ultimately contributing to the development of severe systemic inflammation. Furthermore, the amoxicillin-associated enterotype exaggerated the sepsis-related liver injury, as evidenced by higher levels of alanine aminotransferase, aspartate transaminase, and hepatic malondialdehyde. The results of the RNA sequencing and the fecal suspension intraperitoneal injection sepsis model indicated that the amoxicillin-associated enterotype provoked acute hepatic immune responses and led to more significant metabolic compensation in the event of sepsis. Collectively, we concluded that the gut microbiota was one crucial factor for heterogeneity in sepsis, where the modulated gut microbiota likely prevented or reduced the serious consequences of sepsis, at least in gut-derived sepsis.
Ultra-Wideband Filter with Dual Notch Bands Based on Ring Resonator

ABSTRACT As the ultra-wideband system is easy to be interfered by narrow-band signals, an Ultra-wideband (UWB) bandpass filter for the rejection of wireless local area network and satellite communication signals is proposed. The main resonant structure was composed of an open stubs loaded ring resonator. The cascaded open stubs increased a resonant frequency and improved the selectivity of the filter. Two notch bands were realized by coupling W-shaped short stub loaded resonators and etching complementary split rings resonators. The notch frequencies could be obtained by adjusting the size of two band-stop resonators. The UWB bandpass filter was then simulated, fabricated, and tested. Simulation results are in sound agreement with measured results. Both show that the filter has a compact size with a wide upper stopband, and dual notch bands are centered at 5.8 and 8.0 GHz, respectively, which suppressed the interference from a wireless local area network and satellite communication signals in the UWB passband.
beyond Leading Order in the Chiral Expansion

We present a model–independent analysis of K + → π + l + l and KS → π 0 l + l decays, including K → 3π unitarity corrections and a general decomposition of the dispersive amplitude. From the existing data on K + → π + e + e we predict the ratio R = B(K + → π + � + � )/B(K + → π + e + e ) to be larger than 0.23, in slight disagreement with the recent measurement R = 0.167 ± 0.036. Consequences for the K ± → π ± e + e charge asymmetries and for the KL → π 0 e + e mode are also discussed.
Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain’s electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer’s disease (AD), and Parkinson’s disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.
Financial literacy and sustainability of rural microfinance: The mediating effect of governance

Abstract This research aims to examine the mediating role of microfinance governance in the relationship between financial literacy and microfinance sustainability. The research used the purposive sampling method to collect data from the Women Farmers Group (WFG) “Tapa Walla Badi” in Mbatakapidu village, East Sumba, Indonesia. The survey was conducted from November through December 2021 with the valid sample for analysis was 200 questionnaires. The Partial Least Squares-Structural Equation Model (PLS-SEM) was used for analysis. The bootstrapping and the Sobel test were required for a better understanding of the findings. Robust test and focus group discussion (FGD) made our methodology more reliable. The findings show that financial literacy plays an important role in the transformation of rural microfinance institutions (MFIs). Financial literacy works positively on the sustainability of rural microfinance via governance. Age, gender, education, and employment situation were taken into account in financial literacy. This research provides a comprehensive view to policymakers in developing rural MFIs, thus implies the urgency to promote financial literacy and improve governance structures within MFIs. The local government may create pro-financial inclusion policies that support sustainable MFIs by implementing financial literacy curriculum in formal and non-formal education.
second-order cone programming

This work addresses the numerical computation of the two-dimensional ow of yield stress uids (with Bingham and Herschel-Bulkley models) based on a variational approach and a nite element discretization. The main goal of this paper is to propose an alternative optimization method to existing procedures such as penalization and augmented Lagrangian techniques. It is shown that the minimum principle for Bingham and Herschel-Bulkley yield stress uid steady ows can, indeed, be formulated as a second-order cone programming (SOCP) problem, for which very ecient primal-dual interior point solvers are available. In particular, the formulation does not require any regularization of the visco-plastic model as is usually the case for existing techniques, avoiding therefore the dicult choice of the regularization parameter. Besides, it is also unnecessary to adopt a mixed stress-velocity approach or discretize explicitly auxiliary variables as frequently proposed in existing methods. Finally, the performance of dedicated SOCP solvers, like the Mosek software package, enables to solve large-scale problems on a personal computer within seconds only. The proposed method will be validated on classical benchmark examples and used to simulate the ow generated around a plate during its withdrawal from a bath of yield stress uid.
Rich afterimage illusion and inflow on-screen mechanisms for visual field constancy and visual orientation in space

It has been shown with using a complex 3-D configuration of reference points, that, in the presence of rich afterimage (rA), eye movements lead to transformation of the visual field, representing a complex rotation and scale transformation of 3-D space with a center somewhere in the region of a hypothetical “cyclopean eye”. Experiments have shown that this also results in the recalculation of the coordinates of the observer’s gaze direction, entirely based on inflow information, independent on the signals of the oculomotor efferent system. Three types of rA illusions are described under different kinds of eye movements. A model is again (after 1976) expressed that these phenomena reflect the work of the “contextually” independent Screen Constancy Mechanisms (SCM), which operate at the hypothetical Constant Visual Screen (CVS) level. This level can still be considered as “pseudo-sensory” one of the visual system, located before the formation (appearance) of the object-structured visual field. In experiments, certain conditions affecting the degree of afterimage “richness» have been also investigated.
Even Liquids Are Magnetic: Observation of the Moses Effect and the Inverse Moses Effect

In our everyday life, it seems that magnetic phenomena are restricted to ferromagnetic materials or electromagnetism caused by electric currents. From a physics point of view, we know that this everyday experience is contrary to the nature of magnetic phenomena. If one regards the magnetic properties of the elements of the periodic table in their natural appearance, there are just a few ferromagnetic (3) but a variety of diamagnetic (34) and paramagnetic (51) substances under standard conditions. Using strong neodymium magnets, it is possible to demonstrate the specific properties of diamagnetism and paramagnetism in the classroom for solids and, unexpectedly, as well for liquids because every piece of matter possesses magnetic properties.
Dynamical adaptive integral backstepping variable structure controller design for uncertain systems and experimental application

In this study, a dynamical adaptive integral backstepping variable structure control (DAIBVSC) system based on the Lyapunov stability theorem is proposed for the trajectory tracking control of a nonlinear uncertain mechatronic system with disturbances. In this control scheme, no prior knowledge is required on the uncertain parameters and disturbances because it is estimated by two types of dynamical adaptive laws. These adaptive laws are integrated into the dynamical adaptive integral backstepping control and variable structure control (VSC) parts of the DAIBVSC. The dynamical adaptive law in the dynamical adaptive integral backstepping control part updates parametric uncertainties, while the other in the VSC part adapts upper bounds of non‐parametric uncertainties and disturbances. In order to achieve a more robust output tracking and better parameter adaptation, the control system is extended by one integrator and sliding surface is augmented by an integral action. Experimental evaluation of the DAIBVSC is conducted with respect to performance and robustness to parametric uncertainties. Experimental results of the DAIBVSC are compared with those of a traditional VSC. The proposed DAIBVSC exhibits satisfactory output tracking performance, good estimation of the uncertain parameters and can reject disturbances with a chattering free control law. Copyright © 2017 John Wiley & Sons, Ltd.
Multi-task feature learning by using trace norm regularization

Abstract Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.
Adjuvanted Fusion Protein Vaccine Induces Durable Immunity to Onchocerca volvulus in Mice and Non-Human Primates

Onchocerciasis remains a debilitating neglected tropical disease. Due to the many challenges of current control methods, an effective vaccine against the causative agent Onchocerca volvulus is urgently needed. Mice and cynomolgus macaque non-human primates (NHPs) were immunized with a vaccine consisting of a fusion of two O. volvulus protein antigens, Ov-103 and Ov-RAL-2 (Ov-FUS-1), and three different adjuvants: Advax-CpG, alum, and AlT4. All vaccine formulations induced high antigen-specific IgG titers in both mice and NHPs. Challenging mice with O. volvulus L3 contained within subcutaneous diffusion chambers demonstrated that Ov-FUS-1/Advax-CpG-immunized animals developed protective immunity, durable for at least 11 weeks. Passive transfer of sera, collected at several time points, from both mice and NHPs immunized with Ov-FUS-1/Advax-CpG transferred protection to naïve mice. These results demonstrate that Ov-FUS-1 with the adjuvant Advax-CpG induces durable protective immunity against O. volvulus in mice and NHPs that is mediated by vaccine-induced humoral factors.
Input‐to‐state stability of stochastic port‐Hamiltonian systems using stochastic generalized canonical transformations

As a practically important class of nonlinear stochastic systems, this paper considers stochastic port‐Hamiltonian systems (SPHSs) and investigates the stochastic input‐to‐state stability (SISS) property of a class of SPHSs. We clarify necessary conditions for the closed‐loop system of an SPHS to be SISS. Moreover, we provide a systematic construction of both the SISS controller and Lyapunov function so that the proposed necessary conditions hold. In the main results, the stochastic generalized canonical transformation plays a key role. The stochastic generalized canonical transformation technique enables to design both coordinate transformation and feedback controller with preserving the SPHS structure of the closed‐loop system. Consequently, the main theorem guarantees that the closed‐loop system obtained by the proposed method is SISS against both deterministic disturbance and stochastic noise. Copyright © 2017 John Wiley & Sons, Ltd.
Diversity of Lichens in Northern Alabama Yields Evidence of an Exceptionally Diverse Biota

Abstract - Our recent fieldwork to collect lichens in northern Alabama, with an emphasis on the terminus of the southern Appalachian Mountains, illuminated previously undocumented lichen species in this region of exceptional biodiversity. Numerous new and significant records are presented, including species disjunct both from more northerly latitudes as well as subtropical southerly latitudes: Dibaeis sorediata, Lobaria pulmonaria, Ricasolia quercizans, and the extremely rare Rinodina chrysomeleana. Despite exceptional and noteworthy lichen biodiversity, there exist only 2 large blocks of relatively high quality, undisturbed land in the northern half of the state: Bankhead National Forest and Skyline Wildlife Management Area. This situation, in combination with discovery of exceptional lichen biodiversity, calls for urgency in conservation measures and land protection throughout northern Alabama.
Analysis of longitudinal data under nonignorable nonmonotone nonresponse

We consider identiﬁcation and estimation in a longitudinal study with nonignorable nonmonotone nonresponse in responses. To handle the identiﬁability issue, we use a baseline covariates named as nonresponse instrument that can be excluded from the nonresponse propensity conditional on other observed covariates and the variables subject to nonresponse. The generalized method of moments is applied to estimate the parameters in the nonresponse propensity. Marginal response means and the parameters deﬁned via regression models between responses and baseline covariates can be estimated by inverse probability weighting using the estimated propensity. Alternatively, we derive an augmented inverse probability weighting estimator and apply the importance sampling technique for its computation. Consistency and asymptotic normality of the proposed estimators are established under possibly misspeciﬁed models. Simulations are performed to evaluate the ﬁnite sample performance of the estimators. Also, a real data example is presented to demonstrate the proposed methodology.
Synthesis of Copper(II) and Manganese(II) complexes using 2-hydroxybenzyl-(pyridine-2-carbo) iminohydrazone and 2-Furyl (pyridine-2-carbo) Iminohydrazone: Evaluation of their antimicrobial properties

Three new coordination complexes [Cu(HPBH) 2 ]Cl 2 (1), [Cu(FPFH) 2 ]Cl 2 (2) and [Mn(HPBH) 2 ] Cl 2 (3) (where HPBH=2-Hydroxybenzyl (pyridine-2-carbo) iminohydrazone, and FPFH=2-furyl(pyridine-2-carbo) iminohydrazone) have been synthesized using two different tridentate hydrazones ligand. The ligands were prepared by condensation of Pyridine-2-acetylchloride, 2-Hydroxybenzaldehyde and Furan-2-carbaldehyde with hydrazine, respectively, in spite of varying the carbonyl functionality attached to the pyridine moiety present in the hydrazones ligand. In both the Schiff bases, we obtained three mononuclear complexes 1, 2 and 3 which were clearly characterized from physicochemical studies. Antimicrobial studies have been performed for these compounds with different antimicrobial species.
Numerical Methods for Digital Geometry Processing

Digital Geometry Processing recently appeared (in the middle of the 90's) as a promising avenue to solve the geometric modeling problems encountered when manipulating surfaces represented by discrete elements (i.e. meshes). Since a mesh may be considered to be a sampling of a surface - in other words a signal - the DGP (digital signal processing) formalism was a natural theoretic background for this discipline (see e.g. Taubin 95). In this discipline, discrete fairing Kobbelt 97 and mesh parameterization Floater 98 have been two active research topics these last few years. In parallel with the evolution of this discipline, acquisition techniques have made huge advances, and todays meshes acquired from real objects by range-laser scanners are larger and larger (30 million triangles is now common). This causes difficulties when trying to apply DGP tools to these meshes. The kernel of a DGP algorithm is a numerical method, used either to solve a linear system, or to minimize a multivariate function. The Gauss-Seidel iteration and gradient descent methods used at the early ages of DGP do not scale-up when applied to huge meshes. In this presentation, our goal is to give a survey of classic and more recent numerical methods, to show how they can be applied to DGP problems, from a theoretic point of view down to implementation. We will focus on two different classes of DGP problems (mesh fairing and mesh parameterization), show solutions for linear problems, quadratic problems, and general non-linear problems, with and without constraint. In particular, we give a general formulation of quadratic problems with reduced degrees of freedom that can be used as a general framework to solve a wide class of DGP problems. Our method is implemented in the OpenNL library, freely available on the web. The presentation will be illustrated with live demos of the methods.
Leak Detection and Localization in Multi-Grid Space Using Improved Gaussian Plume Model

Leak detection and localization of liquid or gas is of great significance to avoid potential danger and reduce the waste of resources. Leak detection and localization methods are varied and uniquely suited to specific application scenarios. The existing methods are primarily applied to conventional pressurized pipelines and open areas, and there are few methods suitable for multi-grid spaces. In this paper, a gas diffusion model applied to multi-grid space is constructed, and a method for leak detection and localization using the concentration gradient of characteristic gas is proposed according to the prediction behavior. The Gaussian plume model is selected due to its advantages of simplicity and the interpretation of gas diffusion behavior is closer to reality; the expression of the improved model is also obtained. To verify the correctness of the model and the applicability of the localization method, taking the coolant leakage in the circuit system as an example, three experiments with different source strengths were repeated. The fitting correlation coefficients between the gas concentration data of the three experiments and the model are 0.995, 0.997 and 0.997, respectively. The experimental results show that the model has a strong correlation with the real plume behavior, and it is reasonable to use the gas concentration gradient for the localization of the leak source. This study provides a reference for future research on the leak detection and localization of gas- or liquid-containing volatile substances in a complex multi-grid space.