Accurate Parameter Estimates Research Articles

Biophysical modeling and experimental analysis of the dynamics of C. elegans body-wall muscle cells.

This study combines experimental techniques and mathematical modeling to investigate the dynamics of C. elegans body-wall muscle cells. Specifically, by conducting voltage clamp and mutant experiments, we identify key ion channels, particularly the L-type voltage-gated calcium channel (EGL-19) and potassium channels (SHK-1, SLO-2), which are crucial for generating action potentials. We develop Hodgkin-Huxley-based models for these channels and integrate them to capture the cells' electrical activity. To ensure the model accurately reflects cellular responses under depolarizing currents, we develop a parallel simulation-based inference method for determining the model's free parameters. This method performs rapid parallel sampling across high-dimensional parameter spaces, fitting the model to the responses of muscle cells to specific stimuli and yielding accurate parameter estimates. We validate our model by comparing its predictions against cellular responses to various current stimuli in experiments and show that our approach effectively determines suitable parameters for accurately modeling the dynamics in mutant cases. Additionally, we discover an optimal response frequency in body-wall muscle cells, which corresponds to a burst firing mode rather than regular firing mode. Our work provides the first experimentally constrained and biophysically detailed muscle cell model of C. elegans, and our analytical framework combined with robust and efficient parametric estimation method can be extended to model construction in other species.

A phylogenetic approach to delimitate species in a probabilistic way.

Different species concepts and their associated criteria have been used to delimit species boundaries, such as the absence of gene flow for the biological species concept and the presence of morphological distinction for the morphological species concept. The need for different delimitation criteria largely reflects the fact that species are generated under various speciation mechanisms. A key question is how to make species delimitation consistent in a species group, especially when we want to delimit the species boundaries over many newly discovered evolutionary lineages and add these new lineages into a comparative analysis. Instead of forcing a single definition of 'species', we can acknowledge different delimitation criteria by modelling how fast lineages in a species group evolve to meet these criteria along a phylogenetic tree. This study presents such a new model and a new delimitation approach that calculates the probability of each possible species identity of a lineage. We use simulations to show that our likelihood function gives accurate estimates of parameters in the model and our approach have high power to correctly identify species identities. We apply the approach to lineages in two real species groups that already have genomic and morphological evidence for their species identities. Our approach gives consistent inference of species identities with these existing pieces of evidence. We also demonstrate how to use our model to test a popular hypothesis about speciation process across all lineages in a species group and discuss further extension of the model to study speciation.

Transformer Architecture for Micromotion Target Detection Based on Multi-Scale Subaperture Coherent Integration

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network.

SA-Net: Leveraging Spatial Correlations Spatial-Aware Net for Multi-Perspective Robust Estimation Algorithm

Robust estimation aims to provide accurate and reliable parameter estimations, particularly when data are affected by noise or outliers. Traditional methods like random sample consensus (RANSAC) struggle with handling outliers because they treat all observations as equally important. A series of advanced deep learning methods have recently emerged, which use deep learning techniques to estimate the probability of each sample being selected, prioritizing higher confidence for observations that are closer to the ground truth model. However, optimizing solely based on proximity to the ground truth model does not guarantee higher-quality estimations. Meanwhile spatial relationships between the data points in the minimum sampled set also influence the accuracy of the final estimated model. To address these issues, we propose Spatial-Aware Net (SA-Net), a dual-branch neural network that integrates both confidence and spatial encodings. SA-Net employs a confidence distribution encoder to learn the confidence distribution and a spatial distribution encoder to capture spatial correlations between point features. By incorporating multi-perspective sampling, the minimum sample set can be selected based on different spatial distributions in the output of the neural network, and applying Chamfer Loss constraints, our approach improves model optimization and effectively mitigates suboptimal solutions. Extensive experiments demonstrate that SA-Net outperforms the state of the art across various real-world robust estimation tasks.

Estimation of gamma distribution parameters

Statements of problems of statistical analysis of data with a gamma distribution are related to classical mathematical statistics. Oddly enough, not all alone were solved within the framework of parametric statistics, which was at the forefront of the development of statistical science in the first third of the 20th century. As with the beta distribution, gaps need to be filled. This is necessary because the gamma distribution is currently widely used in theoretical and applied work. An example is GOST 11.011–83 «Applied statistics. Rules for determining estimates and confidence limits for gamma distribution parameters». The standard gamma distribution is determined by the shape parameter. When switching to a scale-shift family, scale and translation parameters are added. Seven formulations of parameter estimation problems are considered, since each of the three parameters can be either unknown or known. For each of the formulations, the estimates of the method of moments and their asymptotic variances are found. For a known shift parameter, maximum likelihood estimates are obtained. One-step estimates, asymptotically equivalent to maximum likelihood estimates, are used for an unknown shift parameter. The presence of measurement errors affects the accuracy of parameter estimates when applying certain calculation algorithms. In GOST 11.011–83, based on the interval data model, rules are given for choosing an estimation method for unknown shape and scale parameters and a known shift parameter. During the development of GOST 11.011–83, problems were identified, for the solution of which new methods from a scientific point of view were proposed. Further development of new scientific results obtained in the course of solving a practical problem (development of GOST 11.011–83) led to the creation of new scientific directions. We are talking about the statistics of interval data, as well as one-step estimates. To date, the statistics of interval data as a branch of mathematical statistics is quite developed and covers all the main areas of statistical methods. It is an important part of systemic fuzzy interval mathematics.

Calibration and optimal transport approaches for harmonizing survey weights

Abstract Originally, the construction of weighting systems to estimate parameters of interest in survey data does not depend on the variables of interest. However, recent research raises questions about the practice of using specific weighting systems for each variable of interest, thus deviating from the universal nature of the weights proposed by survey data processing methods. This article examines the challenge of harmonizing weights for variables with different weighting systems in survey data processing, by presenting and comparing two methods: one that creates a common weight for both variables using the calibration method, and one that uses optimal transport to match the variables. A simulation study is carried out to evaluate the performance of these methods in different sampling scenarios and with continuous and categorical variables. The results of the simulation study show that the optimal transport method for weight harmonization can provide accurate parameter estimates in different scenarios, particularly in situations where disparities between sample and population distributions are large. It therefore appears to be a more versatile solution.

The Study of COVID-19 Virus Infection Based on the SEIR Model

From the Black Death to SARS to influenza A (H1N1), communicable diseases have always been among the uppermost problem endangering human health and have caused crises all over the world countless times. Practical relevance of a thorough knowledge of the transmission pathways and related mechanisms of infectious diseases are complex. Investigating the laws of infectious illness transmission mostly depends on the building of dynamic models. Designing corresponding models based on specific transmission mechanisms can more realistically depict the transmission dynamics of infectious diseases and provide higher accuracy for parameter estimation. This study suggested an enhanced SEIR model and performed a comprehensive investigation of its equilibrium point and stability, acknowledging that individuals infected with the novel coronavirus are contagious in time of their incubation period. Consequently, an empirical investigation was undertaken utilizing this model to analyze the overall trend of the epidemic in China and the outbreak aboard the Diamond Princess cruise ship. Key model parameters, such as the infection rate coefficient, control coefficient, basic reproduction number, and the particular time at which the effective reproduction number declines to 1, were ascertained by the maximum likelihood estimation approach. The cumulative confirmed case fitting curves for the national pandemic in China and the Diamond Princess outbreak were presented concurrently to further validate the model's efficacy

Distinguishing Compact Objects in Extreme-Mass-Ratio Inspirals by Gravitational Waves

Extreme-mass-ratio inspirals (EMRIs) are promising gravitational-wave (GW) sources for space-based GW detectors. EMRI signals typically have long durations, ranging from several months to several years, necessitating highly accurate GW signal templates for detection. In most waveform models, compact objects in EMRIs are treated as test particles without accounting for their spin, mass quadrupole, or tidal deformation. In this study, we simulate GW signals from EMRIs by incorporating the spin and mass quadrupole moments of the compact objects. We evaluate the accuracy of parameter estimation for these simulated waveforms using the Fisher Information Matrix (FIM) and find that the spin, tidal-induced quadruple, and spin-induced quadruple can all be measured with precision ranging from 10−2 to 10−1, particularly for a mass ratio of ∼10−4. Assuming the “true” GW signals originate from an extended body inspiraling into a supermassive black hole, we compute the signal-to-noise ratio (SNR) and Bayes factors between a test-particle waveform template and our model, which includes the spin and quadrupole of the compact object. Our results show that the spin of compact objects can produce detectable deviations in the waveforms across all object types, while tidal-induced quadrupoles are only significant for white dwarfs, especially in cases approaching an intermediate-mass ratio. Spin-induced quadrupoles, however, have negligible effects on the waveforms. Therefore, our findings suggest that it is possible to distinguish primordial black holes from white dwarfs, and, under certain conditions, neutron stars can also be differentiated from primordial black holes.

Frequency and damping factor estimation of damped sinusoid by using DFT and DTFT

An algorithm for estimating the frequency and damping factor of damped complex sinusoidal signal is presented. After the rough estimation of frequency by discrete Fourier transform (DFT), the fine estimation of frequency and damping factor is achieved by utilizing the DFT bin with the maximum magnitude and two arbitrarily spaced discrete time Fourier transform (DTFT) spectral lines located on the same side of the maximum DFT spectrum bin. Then the theoretical mean square error (MSE) in additive white noise is analyzed. Simulation results show that the proposed algorithm tracks the Cramer-Rao lower bound (CRLB), and the parameters estimation accuracy is higher than the competing algorithms in most situations. The computational requirements are almost the same as several existing methods.

A Neural-Metaheuristic Kalman Filter for Moving Microburst Wind Shear Identification

Microburst wind shears (MB) are powerful localized three dimensional (3D) columns of wind that occur when cooled air drops from the base of thunderstorms at high speeds. They eventually hit the ground and spread out in all directions. As such MBs are considered a major atmospheric hazard for aircrafts (ACs), especially in terminal flight phases. Though pilots train regularly to survive it, yet the most recommended practice within the aviation community is to avoid its encounter upon detection. Some modern aircrafts have predictive wind shear warning systems, but they have limited short range capability and do not provide quantitative data for engineering application. In this sense, accurate onboard detection and identification of MBs and its characteristics is of importance for flight safety, whose success can lead to design of automatic flight control systems (FCSs) that reduce the risk of aircraft crash landings and accidents. Even though there are some studies on FCS designs for MB encounter, research on MB identification is rare but ongoing. To this aim, the present study is among the firsts that focuses on online identification of multiple and moving MB parameters using onboard aircraft air data and inertial sensors. The identification task is initially performed using the aircraft six degrees of freedom (6DoF) equations of motion (EOM) integrated with a 3D MB model via the utility of nonlinear Kalman filtering (KF) algorithms. Subsequently, an enhanced neural metaheuristic Kalman filter (NMKF) is proposed that improves the estimation accuracy of the MB model parameters. The results demonstrate the efficacy of the proposed NMKF in comparison with previous studies.

A Robust VMD-PCA Integrated Approach for Accurate Respiration Parameter Estimation from PPG Signals

A Robust VMD-PCA Integrated Approach for Accurate Respiration Parameter Estimation from PPG Signals

Composite Learning Based Adaptive Control of Linear 2×2 Hyperbolic PDE Systems.

This article considers the adaptive stability control of a class of 2×2 linear hyperbolic PDE systems. The PDE model is subject to constant but in-domain and boundary unknown parameters. A novel adaptive controller is developed by leveraging the swapping design technique and composite parameter learning law. With swapping design, several linear and static combinations, including carefully designed filters, unknown parameters, and error terms, are constructed to express the system states. From the static combinations, a composite learning based forgetting-factor least squares law is introduced to guarantee exponential parameter convergence without the persistent excitation (PE). Although inaccurate parameter estimation in the adaptive backstepping control results in asymptotic stability of the system, accurate parameter estimation ensures the exponential convergence of closed-loop system and concomitantly improves the transient performance. Finally, a comparative numerical simulation is performed to validate the effectiveness and advantage of the developed adaptive control scheme.

Dynamic Boundary Estimation of Suspended Sediment Plume Benefit by the Autonomous Underwater Vehicle Sensing.

The suspended sediment plume generated in the deep-sea mining process significantly impacts the marine environment and seabed ecosystem. Accurate boundary estimation can effectively monitor the scope of environmental impact, guiding mining operations to prevent ecological damage. In this paper, we propose a dynamic boundary estimation approach for the suspended sediment plume, leveraging the sensing capability of the Autonomous Underwater Vehicles (AUVs). Based on the plume model and the point-by-point sensor measurements, a Luenberger-type observer is established for designing the AUV control algorithm. To address the challenge of unknown and time-varying environmental parameters, the estimation errors are reduced by using the projection modification unit. Rigorous convergence and stability analyses of the proposed control algorithm are provided by the Lyapunov method. Numerical simulations demonstrate that the improved algorithm enhances the estimation accuracy of unknown parameters and enables the AUV to patrol along the dynamic boundary in a shorter time, thereby verifying the effectiveness of the boundary estimation algorithm based on AUV sensing.

Estimating two-dimensional physical parameters of digital rocks using deep learning

Abstract This research focused on estimating the physical parameters of porous
rocks crucial in hydrocarbon exploration using deep learning algorithms. Laboratory
measurements have limitations such as time, cost, and core sample limitations, so
digital rock models have emerged as a powerful alternative. Digital rock technology
involves creating high-resolution images of rock samples using techniques such as
micro-CT scanning for the detailed analysis of rock structures and calculation
of physical parameters through image processing and numerical simulations. In
this work, the CNN architectures included custom-developed models, and transfer
learning was applied using pre-trained models DenseNet201, ResNet152, MobileNetV2,
InceptionV3, and Xception to estimate physical parameters such as permeability,
absolute porosity, effective porosity, tortuosity, and average grain size. Both CNN
A and CNN B were good models for estimating permeability with CNN B being the
best model for estimating tortuosity, Xception the best model for estimating absolute
porosity and effective porosity, and DenseNet201 the best model for estimating average
grain size. These results underscore the potential of deep learning in enhancing the
efficiency and accuracy of physical parameter estimation in digital rock analysis

Parameter estimation using time-dependent Weibull proportional hazards model for survival analysis with partly interval censored data

OBJECTIVE: To assess the validity and effectiveness of parameter estimation using a time-dependent Weibull proportional hazards model for survival analysis containing partly interval censored data and explore the impact of different covariates on the results of analysis. METHODS: We established a time-dependent Weibull proportional hazards model using the Weibull distribution as the baseline hazard function of the model which incorporated time-varying covariates. Maximum likelihood estimation was employed to estimate the model parameters, which were obtained by optimization of the likelihood function. RESULTS AND CONCLUSION: Numerical simulation results showed that with higher proportions of precise observations across different sample sizes and parameter settings, the proposed model resulted in improved accuracy of parameter estimation with coverage probabilities approximating the theoretical expectation of 95%. As the sample sizes increased, the parameter biases of the model tended to decrease. Experiments with empirical data further validated the effectiveness of the model. Compared with the failure time data for each precisely observed individual, additional interval-censored data helped to obtain more effective estimates of the regression parameters. Comparison with the Cox model that included time-varying covariates further demonstrated the effectiveness of the time-dependent Weibull proportional hazards model for parameter estimation in survival analysis with partly interval censored data.

A Two-Parameter Ridge Estimator for Handling Extreme Multicollinearity Problems in Logistic Regression

This paper introduces a robust two-parameter ridge estimator that is customized for logistic regression models, which tend to be sensitive to extreme multicollinearity problems. Inflated standard errors and unreliability in the results stem from the problem of multicollinearity characterized by high correlations among predictor variables in logistic regression model. Traditional approaches like the Maximum Likelihood Estimator (MLE) and one-parameter ridge-type estimators often perform poorly under these settings, thus calling for the development of more robust approaches. This new proposal is called New Biased Two Parameter (NBTP), which extends the ridge regression framework by introducing additional biasing parameters customized for an extreme multicollinearity problem. The paper combines a theoretical analysis with extensive Monte Carlo simulations and real application to Pena data. It demonstrates that the new estimator, New Biased Two Parameter (NBTP), provides much more stable and accurate parameter estimates than previous methods. The results underscore the importance of using robust estimation methods within logistic regression, especially when multicollinearity may be widespread in fields such as medical research, finance, and the social sciences.

Coherence revival under the Unruh effect and its metrological advantage

Abstract In this paper, we investigate the quantum coherence extraction between two accelerating Unruh-DeWitt detectors, coupling to a scalar field in (3+1)-dimensional Minkowski spacetime. We find that quantum coherence as a nonclassical correlation can be generated through the Markovian evolution of the detector system, just like quantum entanglement. However, with growing Unruh temperature, in contrast to monotonously degrading entanglement, we find that quantum coherence exhibits a striking revival phenomenon. For certain detectors' initial state choices, the coherence measure will reduce to zero at first and then grow to an asymptotic value. We verify such coherence revival by inspecting its metrological advantage on the quantum Fisher information (QFI) enhancement. Since the maximal QFI bounds the accuracy of quantum parameter estimation, we conclude that the extracted coherence can be utilized as a physical resource in quantum metrology.

Community detection in stochastic block models via penalized variational estimation

Stochastic Block Models (SBMs) have emerged as a powerful framework for modelling community structures in networks. However, accurately determining the number of communities in SBMs remains challenging. In this paper, we propose a novel approach for community detection in stochastic block models through penalized variational estimation. Our method introduces two penalties on the variational objective function to ensure elimination of redundant blocks. We present the variational computing procedure, and demonstrate through simulation and empirical analysis that our approach can effectively identifies community structures and achieves accurate parameter estimation simultaneously.

The power of effect size stabilization.

Determining an appropriate sample size in psychological experiments is a common challenge, requiring a balance between maximizing the chance of detecting a true effect (minimizing false negatives) and minimizing the risk of observing an effect where none exists (minimizing false positives). A recent study proposes using effect size stabilization, a form of optional stopping, to define sample size without increasing the risk of false positives. In effect size stabilization, researchers monitor the effect size of their samples throughout the sampling process and stop sampling when the effect no longer varies beyond predefined thresholds. This study aims to improve our understanding of effect size stabilization properties. Simulations involving effect size stabilization are presented, with parametric modulation of the true effect in the population and the strictness of the stabilization rule. As previously demonstrated, the results indicate that optional stopping based on effect-size stabilization consistently yields unbiased samples over the long run. However, simulations also reveal that effect size stabilization does not guarantee the detection of a true effect in the population. Consequently, researchers adopting effect size stabilization put themselves at risk of increasing type 2 error probability. Instead of using effect-size stabilization procedures for testing, researchers should use them to reach accurate parameter estimates.

Optimizing Millimeter Wave MIMO Channel Estimation Through GPU-Based Edge Artificial Intelligence

In the context of upcoming sixth-generation (6G) wireless communication systems, the use of millimeter wave (mmWave) frequencies is a key technology for achieving high-throughput communications. Accurate parametric estimation of mmWave channels is critical for effective beamforming design and configuration, requiring sophisticated models to capture the directional characteristics of these channels. This work considers an innovative artificial intelligence (AI) approach for accurate estimation of angle-of-arrival (AoA) and angle-of-departure (AoD) parameters from frequency-domain channel observations. Our approach is based on the implementation of two convolutional neural networks (CNNs): a residual CNN (ResNet) and a U-Net CNN. Specifically, this work focuses on the efficient implementation of both schemes in an embedded system suitable for edge AI. We performed the experiments in a low-power NVIDIA Jetson Orin Nano platform and evaluated the effect of modifying the frequencies of its CPU and GPU on the performance of the inference process, both in terms of execution time and energy consumption. Experimental results showed that the U-Net model is more power consuming, but as it is faster, it consumes less energy per channel.