Unknown Form Research Articles

Dependence‐robust inference using resampled statistics

SummaryWe develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well‐known forms of weak dependence and justifies the claim of dependence robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.

Non-normal errors or nonlinearity? performance of unit root tests

ABSTRACT This paper investigates how unit root tests that are designed for non-normal errors perform in the presence of unknown forms of nonlinearity. This allows us to examine whether any neglected nonlinearity in an estimation procedure could be reflected, at least partly, in the form of non-normality. Our simulation study shows that in general, univariate tests that exploit the information in non-normal errors remain relatively powerful compared to well-known nonlinear unit root tests under various forms of nonlinear alternatives. We also investigate the unit root properties of the real effective exchange rates and real interest rates for 60 countries. The results support the findings in the simulation that the neglected non-normality or nonlinearity in the existing tests is captured and used in the linearized testing procedures as a source of power improvement.

The Estimation of Parameters of Pulse Signals Having an Unknown Form That Are Observed against the Background of the Additive Mixture of the White Gaussian Noise and a Linear Component with Unknown Parameters

The Estimation of Parameters of Pulse Signals Having an Unknown Form That Are Observed against the Background of the Additive Mixture of the White Gaussian Noise and a Linear Component with Unknown Parameters

Semiparametric transition models

A new semiparametric time series model is introduced – the semiparametric transition (SETR) model – that generalizes the threshold and smooth transition models by letting the transition function to be of an unknown form. Estimation is based on a combination of the (local) least squares estimations of the transition function and regression parameters. The asymptotic behavior for the regression coefficient estimator of the SETR model is established, including its oracle property. Monte Carlo simulations demonstrate that the proposed estimator is more robust to the form of the transition function than parametric threshold and smooth transition methods and more precise than varying coefficient estimators.

Implications of Land Use and Climate Change on Water Balance Components in the Sigi Catchment, Tanzania

Whereas normal water flow in a catchment is necessary for all forms of life, the hydrologic systems in the Sigi catchment is susceptible to land use and climate changes. Over the past three decades, water balance of the Sigi Catchment has indicated changes with unknown forms and magnitudes. To uncover the dynamics, this study used SWAT model to simulate water balance to a separate and combined impact of land-use and climate change. SWAT simulation showed good performance with NSE=0.58 and R2=0.67 for calibration, and NSE=0.56 and R2=0.64 for validation periods. Land use change scenarios indicated increase in surface runoff by 16.1mm, while base flow and water yield decreased by 23.1mm and 7.2mm, respectively. Climate change scenarios indicated an increase in surface runoff by 29.9mm, while base flow and water yield decreased by 36.1mm and 14.2mm, respectively. The combined land use and climate change scenarios indicated increase of surface runoff by 19.0mm, and decrease in base flow and water yield by 29.7mm and 10.7mm, respectively. From the study, it is clearly that the impacts of climate change on water balance components of the Sigi catchment are larger than land use change. Owing to the dilemma facing water resources in this era of climate change, long-term planning that balance households’ livelihood options and water resources management option is needed.

Искусственный интеллект в системе подготовки будущих правоприменителей

In this article, taking into account the latest scientific achievements in the development of artificial neural networks, the problem concerning the process of obtaining knowledge by future law enforcement officers in the context of the transition to other previously unknown methods and forms of artificial intelligence implementation, including in the educational process, is identified. Based on the analysis of the epistemological essence of knowledge and asking questions: how and what should be taught to future lawyers, what knowledge and skills they will need in the future, the authors offer their own interactive educational methods for obtaining knowledge at a higher level. To solve the identified tasks and achieve the goal, using general scientific and private methods of cognition, the article analyzes the opinions of scientists who support the positive dynamics of education with the use of artificial intelligence technologies, and opponents of this concept. Based on the results of scientific research and their own pedagogical experience, the authors criticize the process of education in which it is proposed to maximize the use of artificial intelligence, replacing the teacher and teacher, since students receive information without using the capabilities and abilities of the human brain, ignoring many effective methods of cognition, which negatively affects the assimilation of information, obtaining new knowledge and developing independent skills. The authors defend the position that in the process of education, a synergy of artificial and natural intelligences is necessary, otherwise many of a person’s cognitive abilities may be lost. Based on the conducted research, it is concluded that in the era of modern times, completely different standards and principles of education are needed. And, despite all the effectiveness of using artificial intelligence technologies, the task of teachers, first of all, is to teach future law enforcement officers to develop their own intelligence, their personal cognitive abilities.

InfPolyn, a Nonparametric Bayesian Characterization for Composition-Dependent Interdiffusion Coefficients.

Composition-dependent interdiffusion coefficients are key parameters in many physical processes. However, finding such coefficients for a system with few components is challenging due to the underdetermination of the governing diffusion equations, the lack of data in practice, and the unknown parametric form of the interdiffusion coefficients. In this work, we propose InfPolyn, Infinite Polynomial, a novel statistical framework to characterize the component-dependent interdiffusion coefficients. Our model is a generalization of the commonly used polynomial fitting method with extended model capacity and flexibility and it is combined with the numerical inversion-based Boltzmann–Matano method for the interdiffusion coefficient estimations. We assess InfPolyn on ternary and quaternary systems with predefined polynomial, exponential, and sinusoidal interdiffusion coefficients. The experiments show that InfPolyn outperforms the competitors, the SOTA numerical inversion-based Boltzmann–Matano methods, with a large margin in terms of relative error (10× more accurate). Its performance is also consistent and stable, whereas the number of samples required remains small.

Efficient long-range conduction in cable bacteria through nickel protein wires

Filamentous cable bacteria display long-range electron transport, generating electrical currents over centimeter distances through a highly ordered network of fibers embedded in their cell envelope. The conductivity of these periplasmic wires is exceptionally high for a biological material, but their chemical structure and underlying electron transport mechanism remain unresolved. Here, we combine high-resolution microscopy, spectroscopy, and chemical imaging on individual cable bacterium filaments to demonstrate that the periplasmic wires consist of a conductive protein core surrounded by an insulating protein shell layer. The core proteins contain a sulfur-ligated nickel cofactor, and conductivity decreases when nickel is oxidized or selectively removed. The involvement of nickel as the active metal in biological conduction is remarkable, and suggests a hitherto unknown form of electron transport that enables efficient conduction in centimeter-long protein structures.

New cosmological solutions in hybrid metric-Palatini gravity from dynamical symmetries

We investigate the existence of Liouville integrable cosmological models in hybrid metric-Palatini theory. Specifically, we use the symmetry conditions for the existence of quadratic in the momentum conservation laws for the field equations as constraint conditions for the determination of the unknown functional form of the theory. The exact and analytic solutions of the integrable systems found in this study are presented in terms of quadratics and Laurent expansions.

Adjustment models for multivariate geodetic time series with vector-autoregressive errors

Abstract In this contribution, a vector-autoregressive (VAR) process with multivariate t-distributed random deviations is incorporated into the Gauss-Helmert model (GHM), resulting in an innovative adjustment model. This model is versatile since it allows for a wide range of functional models, unknown forms of auto- and cross-correlations, and outlier patterns. Subsequently, a computationally convenient iteratively reweighted least squares method based on an expectation maximization algorithm is derived in order to estimate the parameters of the functional model, the unknown coefficients of the VAR process, the cofactor matrix, and the degree of freedom of the t-distribution. The proposed method is validated in terms of its estimation bias and convergence behavior by means of a Monte Carlo simulation based on a GHM of a circle in two dimensions. The methodology is applied in two different fields of application within engineering geodesy: In the first scenario, the offset and linear drift of a noisy accelerometer are estimated based on a Gauss-Markov model with VAR and multivariate t-distributed errors, as a special case of the proposed GHM. In the second scenario real laser tracker measurements with outliers are adjusted to estimate the parameters of a sphere employing the proposed GHM with VAR and multivariate t-distributed errors. For both scenarios the estimated parameters of the fitted VAR model and multivariate t-distribution are analyzed for evidence of auto- or cross-correlations and deviation from a normal distribution regarding the measurement noise.

Evolution: Bend it like basal synapsids

Mammals can amplify their strides through unique up-and-down spinal movements. This ability was long considered to have evolved from lizard-like ancestors with spines moving sideways. A new study now suggests that, instead, it derived from an extinct, previously unknown spinal form.

Critique of Archived Life: Toward a Hesitation of Sikh Immigrant Accumulation

AbstractIn 2016, the Pioneering Punjabi Digital Archive (PPDA) went online. Attempting to reveal how the Punjabi community struggled and then thrived in California, the PPDA accumulates narratives of Punjabi American life. Against such models of archival intimacy and recovery, which look to cultivate limitless public access to a knowable and transparent subject while reducing structural precarity to the failure of an exceptional Punjabi, this article hesitates in a vexed archival space without guarantees. Within this hesitation, it explores the traces of untimely lives displaced in creating archival legibility—traces that reveal a different form of being that challenge the additive logic of the PPDA. This hesitation is cultivated through a comparative approach that couples archival and ethnographic research based on articles about Punjabi American life in both the archive and public sphere alongside ethnographic work conducted with Sikh immigrants who work in canneries and the fields. The aim is to pause in the present impasse to consider the nonbecoming of unknown forms—an ethnographic “reaching and ungrasping” in which the future is not fixed as a requirement for thinking, refusing the accumulating demands of narrative sequence that archiving presents.

Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART).

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval-censored survival data under a paradigm of Bayesian ensemble learning, called soft Bayesian additive regression trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left-censored, right-censored, and interval-censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high-dimensional data with complex underlyingassociations.

First Person Profile: Cheryl Willman, MD: As director and CEO of the University of New Mexico Comprehensive Cancer Center, Dr. Willman has led major health equity initiatives while making pioneering discoveries in precision medicine.

First Person Profile: Cheryl Willman, MD: As director and CEO of the University of New Mexico Comprehensive Cancer Center, Dr. Willman has led major health equity initiatives while making pioneering discoveries in precision medicine.

Tuning Transport Phenomena in Agarose Gels for the Control of Protein Nucleation Density and Crystal Form

Agarose gels provide the ideal environment for studying the nucleation step of complex biomacromolecules under diffusion-controlled conditions. In the present paper, we characterized the influence of agarose on the nucleation of three model proteins, i.e., lysozyme, insulin, and proteinase K, as a function of the agarose concentration using a batch method set-up inside flat capillaries. By using this set-up, we were able to directly count the number of crystals in a given volume and correlate it with the amount of agarose and with the average crystal size. We also studied the crystallization behavior of proteinase K with free-interface diffusion so that batch conditions were achieved through slow diffusion of the precipitant. Thanks to the control over the protein mass transport imposed by the network, a previously unknown crystal form, P212121, was obtained, and the three-dimensional structure was determined at a 1.6 Å resolution. Overall, the versatility of agarose gels makes them ideal candidates for the preparation of microcrystalline suspensions of biopharmaceuticals with precise and reproducible crystal attributes or for the exploration of the existence of different polymorphs.

The R-matrix bootstrap for the 2d O(N) bosonic model with a boundary

The S-matrix bootstrap is extended to a 1+1d theory with O(N) symmetry and a boundary in what we call the R-matrix bootstrap since the quantity of interest is the reflection matrix (R-matrix). Given a bulk S-matrix, the space of allowed R-matrices is an infinite dimensional convex space from which we plot a two dimensional section given by a convex domain on a 2d plane. In certain cases, at the boundary of the domain, we find vertices corresponding to integrable R-matrices with no free parameters. In other cases, when there is a one-parameter family of integrable R-matrices, the whole boundary represents integrable theories. We also consider R-matrices which are analytic in an extended region beyond the physical cuts, thus forbidding poles (resonances) in that region. In certain models, this drastically reduces the allowed space of R-matrices leading to new vertices that again correspond to integrable theories. We also work out the dual problem, in particular in the case of extended analyticity, the dual function has cuts on the physical line whenever unitarity is saturated. For the periodic Yang-Baxter solution that has zero transmission, we computed the R-matrix initially using the bootstrap and then derived its previously unknown analytic form.

Stock Market Inclusion and İts Connection with Economic Activity in Turkey

This study explores the relationship between stock market inclusion and economic activity (liveliness) in Turkey by taking advantage of the recent contributions in causality theory. Stock market inclusion is represented by the seasonally adjusted real stock market trade volume per capita (TV) and economic activity by the seasonally adjusted real gross domestic product per capita (GDP). We use quarterly series covering the period 2003:1-2020:2 and employ asymmetric bootstrap and asymmetric Fourier bootstrap causality testing procedures to obtain robust parameter estimates. Both procedures adopt a nonlinear methodology but the latter is distinguished from the first in the sense that it follows a Fourier series approximation which allows for structural breaks of unknown number, form, and point. Empirical findings suggest that the Fourier-type asymmetric bootstrap causality procedure, thanks to its trigonometric components, captures two unidirectional (one-way) causalities; one running from the positive components of TV to those of GDP and the other running from the negative components of TV to those of GDP, but not vice versa. These findings verified a strong influence on GDP of the alterations i.e. positive and negative shocks in stock market conditions.

Generation of the induced pluripotent stem cell line (NCKDi002-A) from a 22-year-old patient with Focal Segmental Glomerular Sclerosis carrying a heterozygous mutation in WT1 gene

Focal Segmental Glomerular Sclerosis (FSGS) is a glomerular disease which can be classified into primary, secondary, genetic, and unknown forms. WT1 mutation has been shown to be associated with this disorder. Recently, we identified a mutation in the Zinc finger C2H2 domain of WT1 gene in a patient with FSGS who also carried a family history of end-stage renal disease (ESRD). The Peripheral Blood Mononuclear Cells (PBMCs) of the patient were obtained and a line of induced pluripotent stem cells (iPSCs) was successfully generated. The iPSC line will be useful for further study of the pathogenesis and drug screening for FSGS.

Improved generalized raking estimators to address dependent covariate and failure-time outcome error.

Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typically complex, consisting of errors of unknown functional form in covariates and the outcome, which can be dependent. To address the bias resulting from such errors, generalized raking has recently been proposed as a robust method that yields consistent estimates without the need to model the error structure. We provide rationale for why these previously proposed raking estimators can be expected to be inefficient in failure-time outcome settings involving misclassification of the event indicator. We propose raking estimators that utilize multiple imputation, to impute either the target variables or auxiliary variables, to improve the efficiency. We also consider outcome-dependent sampling designs and investigate their impact on the efficiency of the raking estimators, either with or without multiple imputation. We present an extensive numerical study to examine the performance of the proposed estimators across various measurement error settings. We then apply the proposed methods to our motivating setting, in which we seek to analyze HIV outcomes in an observational cohort with EHR data from the Vanderbilt Comprehensive CareClinic.

Arsenic transfer along the soil-sclerotium-stroma chain in Chinese cordyceps and the related health risk assessment.

BackgroundChinese cordyceps (Lepidoptera: Ophiocordyceps sinensis)is a larval-fungus complex. The concentration and distribution of arsenic (As) may vary during the stroma (ST) germination process and between the sclerotium (SC) and the ST. The soil-to-Chinese cordyceps system is an environmental arsenic exposure pathway for humans. We studied the As concentration in the soil, the SC, and the ST of Chinese cordyceps, and performed a risk assessment.MethodsSoil and Chinese cordyceps samples were collected from the Tibetan Plateau in China. The samples were analyzed for the total As concentration and As species determination, which were conducted by inductively coupled plasma mass spectrometry (ICP-MS) and HPLC-ICP-MS, respectively.ResultsThe concentration of total As in the soil was much higher than in SC and ST. The major As species in the soil was inorganic AsV. In SC and ST, organic As was predominant, and the majority of As was an unknown organic form. There are significant differences in the As distribution and composition in soil, SC, and ST. Our risk assessment indicated that chronic daily ingestion was higher than inhalation and dermal exposure in children and adults. The hazard index (HI) of the non-carcinogenic and cancer risks (CR) for human health were HI ≤ 1 and CR < 1 × 10−4, respectively.ConclusionThe Chinese cordyceps possesses highly-efficient detoxifying characteristics and has a significant role in As transformation during its life cycle. We found that the levels of As in soils from the habitat of Chinese cordyceps were higher than the soil background values in China, but the probability for incurring health risks remained within the acceptable levels for humans.