Bias Parameters Research Articles

An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

Intriguing Plasma Composition Pattern in a Solar Active Region: A Result of Nonresonant Alfvén Waves?

The plasma composition of the solar corona is different from that of the solar photosphere. Elements that have a low first ionization potential (FIP) are preferentially transported to the corona and therefore show enhanced abundances in the corona compared to the photosphere. The level of enhancement is measured using the FIP bias parameter. In this work, we use data from the EUV Imaging Spectrometer on Hinode to study the plasma composition in an active region following an episode of significant new flux emergence into the preexisting magnetic environment of the active region. We use two FIP bias diagnostics: Si x 258.375 Å/S x 264.233 Å (temperature of approximately 1.5 MK) and Ca xiv 193.874 Å/Ar xiv 194.396 Å (temperature of approximately 4 MK). We observe slightly higher FIP bias values with the Ca/Ar diagnostic than Si/S in the newly emerging loops, and this pattern is much stronger in the preexisting loops (those that had been formed before the flux emergence). This result can be interpreted in the context of the ponderomotive force model, which proposes that the plasma fractionation is generally driven by Alfvén waves. Model simulations predict this difference between diagnostics using simple assumptions about the wave properties, particularly that the fractionation is driven by resonant/nonresonant waves in the emerging/preexisting loops. We propose that this results in the different fractionation patterns observed in these two sets of loops.

The genomic footprint of social stratification in admixing American populations.

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European, and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification, shaped by socially constructed racial and gender hierarchies, has constrained the admixture processes in the Americas since the European colonization and the subsequent Atlantic slave trade.

A Bayesian method to mitigate the effects of unmodelled time-varying systematics for 21-cm cosmology experiments

ABSTRACT Radio observations of the neutral hydrogen signal from the Cosmic Dawn and Epoch of Reionization have helped to provide constraints on the properties of the first stars and galaxies. Since this global 21-cm cosmological signal from the Cosmic Dawn is effectively constant on observing time-scales and since effects resulting from systematics will vary with time, the effects of these systematics can be mitigated without the need for a model of the systematic. We present a method to account for unmodelled time-varying systematics in 21-cm radio cosmology experiments using a squared exponential Gaussian process kernel to account for correlations between time bins in a fully Bayesian way. We find by varying the model parameters of a simulated systematic that the Gaussian process method improves our ability to recover the signal parameters by widening the posterior in the presence of a systematic and reducing the bias in the mean fit parameters. When varying the amplitude of a model sinusoidal systematic between 0.25 and 2.00 times the 21-cm signal amplitude and the period between 0.5 and 4.0 times the signal width, we find on average a 5 per cent improvement in the root mean squared error of the fitted signal. We can use the fitted Gaussian process hyperparameters to identify the presence of a systematic in the data, demonstrating the method’s utility as a diagnostic tool. Furthermore, we can use Gaussian process regression to calculate a mean fit to the residuals over time, providing a basis for producing a model of the time-varying systematic.

The impact of halo concentration on the Sunyaev Zel’dovich effect signal from massive galaxy clusters

ABSTRACT The Sunyaev Zel’dovich (SZ) effect is sensitive to the pressure of ionized gas inside galaxy clusters, which is in turn controlled largely by the gravitational potential of the cluster. Changing the concentration parameter describing the cluster mass distribution impacts the gravitational potential and thus the cluster SZ signal, with implications for cosmological and other analyses of SZ-selected clusters. We investigate the concentration–SZ relation in theory and simulations. We find that the impact of concentration on the inner SZ profile (R ≲ 0.75R200c) can be captured with standard polytropic gas models. However, we find that such models do a poor job of reproducing the outer SZ profiles (R ≳ 0.75R200c) and the relation between the integrated SZ signal, Y, and concentration. This disagreement results from a sharp truncation of the gas pressure profile near the splashback radius, likely caused by virial shocks. We develop a simple description of the truncation that leads to a good match with the simulated SZ profiles out to several R200c for clusters of varying mass and concentration, and that also accurately predicts the concentration–Y relationship. Finally, we determine how inference of the linear bias parameter and splashback radius for SZ-selected clusters can be biased by ignoring the concentration dependence of the SZ signal, finding that bias to the former is essentially negligible, while bias to the latter can be as much as 2 per cent.

Feature-wise scaling and shifting: Improving the generalization capability of neural networks through capturing independent information of features

From the perspective of input features, information can be divided into independent information and correlation information. Current neural networks mainly concentrate on the capturing of correlation information through connection weight parameters supplemented by bias parameters. This paper introduces feature-wise scaling and shifting (FwSS) into neural networks for capturing independent information of features, and proposes a new neural network FwSSNet. In the network, a pair of scale and shift parameters is added before each input of each network layer, and bias is removed. The parameters are initialized as 1 and 0, respectively, and trained at separate learning rates, to guarantee the fully capturing of independence and correlation information. The learning rates of FwSS parameters depend on input data and the training speed ratios of adjacent FwSS and connection sublayers, meanwhile those of weight parameters remain unchanged as plain networks. Further, FwSS unifies the scaling and shifting operations in batch normalization (BN), and FwSSNet with BN is established through introducing a preprocessing layer. FwSS parameters except those in the last layer of the network can be simply trained at the same learning rate as weight parameters. Experiments show that FwSS is generally helpful in improving the generalization capability of both fully connected neural networks and deep convolutional neural networks, and FWSSNets achieve higher accuracies on UCI repository and CIFAR-10.

Nested Pseudo likelihood estimation of continuous-time dynamic discrete games

We introduce a sequential estimator for continuous time dynamic discrete choice models (single-agent models and games) by adapting the nested pseudo likelihood (NPL) estimator of Aguirregabiria and Mira (2002, 2007), developed for discrete time models with discrete time data, to the continuous time case with data sampled either discretely (i.e., uniformly-spaced snapshot data) or continuously. We establish conditions for consistency and asymptotic normality of the estimator, a local convergence condition, and, for single agent models, a zero Jacobian property assuring local convergence. We carry out a series of Monte Carlo experiments using an entry-exit game with five heterogeneous firms to confirm the large-sample properties and demonstrate finite-sample bias reduction via iteration. In our simulations we show that the convergence issues documented for the NPL estimator in discrete time models are less likely to affect comparable continuous-time models. We also show that there can be large bias in economically-relevant parameters, such as the competitive effect and entry cost, from estimating a misspecified discrete time model when in fact the data generating process is a continuous time model.

Impact of tidal environment on galaxy clustering in GAMA

ABSTRACT We constrain models of the galaxy distribution in the cosmic web using data from the Galaxy and Mass Assembly (GAMA) survey. We model the redshift-space behaviour of the 2-point correlation function (2pcf) and the recently proposed Voronoi volume function (VVF) – which includes information beyond two-point statistics. We extend the standard halo model using extra satellite degrees of freedom and two assembly bias parameters: αcen and αsat, which correlate the occupation numbers of central and satellite galaxies with their host halo’s tidal environment, respectively. We measure $\alpha _{\rm sat}=1.44^{+0.25}_{-0.43}$ and $\alpha _{\rm cen}=-0.79^{+0.29}_{-0.11}$ using a combination of 2pcf and VVF measurements, representing a detection of assembly bias at the 3.3σ (2.4σ) significance level for satellite (central) galaxies. This result remains robust to possible anisotropies in the halocentric distribution of satellites as well as technicalities of estimating the data covariance. We show that the growth rate (fσ8) deduced using models with assembly bias is about 7 per cent (i.e. 1.5σ) lower than if assembly bias is ignored. When projected on to the Ωm–σ8 plane, the model constraints without assembly bias overlap with Planck expectations, while allowing assembly bias introduces significant tension with Planck, preferring either a lower Ωm or a lower σ8. Finally, we find that the all-galaxy weak-lensing signal is unaffected by assembly bias, but the central and satellite sub-populations individually show significantly different signals in the presence of assembly bias. Our results illustrate the importance of accurately modelling galaxy formation for cosmological inference from future surveys.

Balancing the Objectives of Statistical Efficiency and Allocation Randomness in Randomized Controlled Trials

Various restricted randomization procedures are available to achieve equal (1:1) allocation in a randomized clinical trial. However, for some procedures, there is a nonnegligible probability of imbalance in the final numbers which may result in an underpowered study. It is important to assess such probability at the study planning stage and make adjustments in the design if needed. In this article, we perform a quantitative assessment of the tradeoff between randomness, balance, and power of restricted randomization designs targeting equal allocation. First, we study the small-sample performance of biased coin designs with known asymptotic properties and identify a design with an excellent balance–randomness tradeoff. Second, we investigate the issue of randomization-induced treatment imbalance and the corresponding risk of an underpowered study. We propose two risk mitigation strategies: increasing the total sample size or fine-tuning the biased coin parameter to obtain the least restrictive randomization procedure that attains the target power with a high, user-defined probability for the given sample size. Additionally, we investigate an approach for finding the most balanced design that satisfies a constraint on the chosen measure of randomness. Our proposed methodology is simple and yet generalizable to more complex settings, such as trials with stratified randomization and multi-arm trials with possibly unequal randomization ratios.

Estimating item parameters in multistage designs with the tmt package in R

Various likelihood-based methods are available for the parameter estimation of item response theory models (IRT), leading to comparable parameter estimates. Considering multistage testing (MST) designs, Glas (1988; https://doi.org/10.2307/1164950) stated that the conditional maximum likelihood (CML) method in its original formulation leads to severely biased parameter estimates. A modified CML estimation method for MST designs proposed by Zwitser and Maris (2015; https://doi.org/10.1007/s11336-013-9369-6) finally provides asymptotically unbiased item parameter estimates. Steinfeld and Robitzsch (2021b; https://doi.org/10.31234/osf.io/ew27f) complemented this method to MST designs with probabilistic routing strategies. For both proposed modifications additional software solutions are required since design-specific information must be incorporated into the estimation process. An R package that has implemented both modifications is "tmt". In this article, first, the proposed solutions of the CML estimation in MST designs are illustrated, followed by the main part, the demonstration of the CML item parameter estimation with the R package "tmt". The demonstration includes the process of model specification, data simulation, and item parameter estimation, considering two different routing types of deterministic and probabilistic MST designs.

Flow rectification in a micro-nanochannel junction: Interplay of channel geometry and interfacial properties for directional transport

The linear correspondence between flow rate and pressure drop for low Reynolds number hydrodynamics is often a constraint for the realization of miniaturized fluid circuits with selective junctions. Here we devise a micro-nanochannel junction with the directional flow in the nanochannel achieved by a combination of junction geometry and wall functionalization. The system can be compared to a fluid diode with parity-breaking between pressure drop and flow rate in the nanochannel junction with increasing flow rates in a particular configuration and negligible throughput in the reverse direction. This directional junction transport is tuned with slip length, characterizing channel wall functionalization as the bias parameter. Tuning the transport or leakage at a junction is key to engineering various applications. Such junctions are ubiquitous in systems like capillary blood flow, desalination systems, energy storage devices, etc. The resulting flow physics, thus unraveled, is an amalgamation of various factors which leads to a flow rectification system. We have delineated the effect of the confinement of the channel, the slip, and the geometry of the nanochannel towards the selective nature of the resulting configuration. We have successfully characterized a system to generate flow anisotropy while transporting fluid in different directions using numerical techniques. Additionally, our work demonstrates, for the first time, that a common definition of slip coefficient may be associated with three different flow regimes, which correspond to the order of magnitude of slip length relative to the channel dimensions thus providing a unifying key to engineer substrates and geometries towards designing fluid circuits.

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today's workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker's performance in a human-robot collaborative environment. In this study, factory workers' mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

Measurement and calculation for high frequency magnetic losses of Terfenol-D alloy rod under coupled stress and DC bias fields

Compressive stress, a DC bias magnetic field, and an AC excitation field significantly affect hysteresis characteristics and magnetic energy losses of magnetostrictive materials. Therefore, studying high frequency magnetic losses of magnetostrictive materials under coupling of these three conditions is of great importance for improving the output performance of high power magnetostrictive transducers. In this paper, a magnetic property testing system for magnetostrictive materials has been constructed. It enables the magnetic property testing of Terfenol-D alloy rods under the simultaneous application of stress and DC bias at high frequency excitation (a maximum AC magnetic flux density of 0.5 T at an excitation frequency of 9 kHz). Based on Bertotti separation theory and experimental data, considering the coupling effect of stress and DC bias, a computational model for the high frequency magnetic losses of Terfenol-D materials is proposed. The model incorporates relevant parameters of stress and DC bias to modify loss coefficients, and the expressions for loss coefficients are identified using the symbolic regression method. A comparative analysis between experimental data and model calculation shows good agreement, with the maximum and average errors of 4.68% and 1.74%, respectively.

3-D muographic inversion in the exploration of cavities and low-density fractured zones

SUMMARY Muography is an imaging tool based on the attenuation of cosmic muons for observing density anomalies associated with large objects, such as underground caves or fractured zones. Tomography based on muography measurements, that is, 3-D reconstruction of density distribution from 2-D muon flux maps, brings along special challenges. The detector field of view covering must be as balanced as possible, considering the muon flux drop at high zenith angles and the detector placement possibilities. The inversion from directional muon fluxes to a 3-D density map is usually underdetermined (more voxels than measurements). Therefore, the solution of the inversion can be unstable due to partial coverage. The instability can be solved by geologically relevant Bayesian constraints. However, the Bayesian principle results in parameter bias and artefacts. In this work, linearized (density-length based) inversion is applied by formulating the constraints associated with inversion to ensure the stability of parameter fitting. After testing the procedure on synthetic examples, an actual high-quality muography measurement data set from seven positions is used as input for the inversion. The resulting tomographic imaging provides details on the complicated internal structures of karstic fracture zone. The existence of low density zones in the imaged space was verified by samples from core drills, which consist of altered dolomite powder within the intact high density dolomite.

Partially Nested Designs in Social Work Research: Principles and Practices

Purpose: Group-administered and shared facilitator treatments can induce nested data in a treatment arm that is not present in the control arm. Failure to accommodate these partially nested data structures produces study design inefficiencies, biased parameter estimates, and inaccurate inferences. This work introduces partially nested data structures. Method: We began by describing the features of partially nested data then discuss best practices and guidelines for study planning and analysis through examples commonly found in social work research. Results: The totality of this work provides social work researchers with the knowledge and tools to accommodate partially nested data in study planning and analysis including integration of comprehensive effects (i.e., mediation and moderation). Discussion: Improved understanding of partially nested data structures help researchers avoid the detrimental effects associated with disregarding them. Broadly, these methodological advances increase the capacity and quality of research in the field of social work.

Modelling the composition of household portfolios: A latent class approach

AbstractWe explore portfolio allocation in Great Britain by introducing a latent class modelling approach using household panel data based on a nationally representative sample of the population, namely the Wealth and Assets Survey. The latent class aspect of the model splits households into four groups, from lowest‐wealth and least‐diversified through to highest‐wealth and most‐diversified, which serves to unveil a more detailed picture of the determinants of portfolio diversification than existing econometric approaches. A pattern of class heterogeneity is revealed that conventional econometric models are unable to identify because the statistical significance and the direction of the effect of some explanatory variables vary across the groups. For example, the effect of labour income on the number of financial assets held influences the level of diversification for the two middle classes, whereas no effect is found for households with the lowest or the highest levels of diversification. Noticeable differences in the magnitude of the effects of pension wealth and occupation are also revealed across the four classes. Such findings demonstrate the importance of accounting for latent heterogeneity when modelling financial behaviour. Ultimately, treating the population as a single homogeneous group may lead to biased parameter estimates, whereby policy based on such models could be inappropriate or erroneous.

A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest

Process-based models are widely used to predict the agroecosystem dynamics, but such modeled results often contain considerable uncertainty due to the imperfect model structure, biased model parameters, and inaccurate or inaccessible model inputs. Data assimilation (DA) techniques are widely adopted to reduce prediction uncertainty by calibrating model parameters or dynamically updating the model state variables using observations. However, high computational cost, difficulties in mitigating model structural error, and low flexibility in framework development hinder its applications in large-scale agroecosystem predictions. In this study, we addressed these challenges by proposing a novel DA framework that integrates a Knowledge-Guided Machine Learning (KGML)-based surrogate with tensorized ensemble Kalman filter (EnKF) and parallelized particle swarm optimization (PSO) to effectively assimilate historical and in-season multi-source remote sensing data. Specifically, we incorporate knowledge from a process-based model, ecosys, into a Gated Recurrent Unit (GRU)-based hierarchical neural network. The hierarchical architecture of KGML-DA mimics key processes of ecosys and builds a causal relationship between target variables. Using carbon budget quantification in the US Corn-Belt as a context, we evaluated KGML-DA's performance in predicting key processes of the carbon cycle at three agricultural sites (US-Ne1, US-Ne2, US-Ne3), along with county-level (627 counties) and 30-m pixel-level (Champaign County, IL) grain yield. The site experiments show that updating the upstream variable, e.g., gross primary production (GPP), improved the prediction of downstream variables such as ecosystem respiration, net ecosystem exchange, biomass, and leaf area index (LAI), with RMSE reductions ranging from 9.2% to 30.5% for corn and 4.8% to 24.6% for soybean. Uncertainty in downstream variables was automatically constrained after correcting the upstream variables, demonstrating the effectiveness of the causality linkages in the hierarchical surrogate. We found joint use of in-season GPP and evapotranspiration (ET) products along with historical GPP and surveyed yields achieved the best prediction for county-level yields, while assimilating in-season LAI observations benefitted the prediction in extreme years. Uncertainty and error analysis of regional yield estimation demonstrated that KGML-DA could reduce prediction error by 26.5% for corn and 36.2% for soybean. Remarkably, the GPU-based tensor operation design makes this DA framework more than 7000 times faster than the PB model with a High-Performance Computing system, indicating the high potential of the proposed framework for in-season, high-resolution agroecosystem predictions.

Cancer Disease Treatment Classification Using Extended Rough based Intuitionistic Fuzzy C-means Learning Methodology

Because of the sensitivity of magnetic resonance to noise for the diagnosis and study of cancer, classifying magnetic resonance disease treatments is a medically complex and vitally important process. To sum up, these are the most pressing problems with the current system for describing how to treat cancer. Mathematical methods like rough sets, fuzzy sets, and rough sets are utilized to assess and cope with the ambiguity and uncertainty present in medical cancer disease treatments. Rough sets and rough sets-based techniques have both been offered in past, but depending on the parameters, each has its own special set of issues. The Extended Rough based Intuitionistic Fuzzy C-means Learning Technique (ERIFCM) with estimation of weight bias parameter for Cancer Disease Treatment Classification presented in this work is a novel method for computing the disease treatment classification. Using the non-membership and membership values in intuitionistic Rough based fuzzy sets, a generalised kind of fuzzy, rough sets and their representative components are evaluated. The approach that is proposed in this research uses existing clustering's standard features to lower the intensity and noise associated with cancer treatments without the need for spatial weight context data. In addition, the cluster centroid is initialized using the max-dist evaluation method, which is based on the weight measure, before the proposed algorithm is executed. This helps to reduce the number of iterations required for clustering. In contrast to existing segmented approaches developed for Cancer Disease Treatment and similar Disease Treatments, experimental results of the proposed approach yielded effective Disease Treatment Classification results. The main component of the proposed method is a more thorough analysis of experimental results.

Demogenomic inference from spatially and temporally heterogeneous samples.

Modern and ancient genomes are not necessarily drawn from homogeneous populations, as they may have been collected from different places and at different times. This heterogeneous sampling can be an issue for demographic inferences and results in biased demographic parameters and incorrect model choice if not properly considered. When explicitly accounted for, it can result in very complex models and high data dimensionality that are difficult to analyse. In this paper, we formally study the impact of such spatial and temporal sampling heterogeneity on demographic inference, and we introduce a way to circumvent this problem. To deal with structured samples without increasing the dimensionality of the site frequency spectrum (SFS), we introduce a new structured approach to the existing program fastsimcoal2. We assess the efficiency and relevance of this methodological update with simulated and modern human genomic data. We particularly focus on spatial and temporal heterogeneities to evidence the interest of this new SFS-based approach, which can be especially useful when handling scattered and ancient DNA samples, as in conservation genetics or archaeogenetics.

Modified maximum likelihood space registration method for shipborne multi-radar signal processing

Aiming at the spatial registration problem for networking of multi-platform shipborne radars, a modified maximum likelihood (MML) absolute registration algorithm based on singular value decomposition (SVD) is proposed. Firstly, the four major biases (radar detection biases, platform positioning biases, attitude biases, and baseline conversion biases) which affect shipborne radar detection are analyzed, and an equivalent model of shipborne radar detection biases is established to make the bias parameters more efficiently. Secondly, the maximum likelihood registration method is improved. According to the singular value decomposition, the conditional index is used to evaluate the observability in the maximum likelihood registration method in realtime, and the abnormal conditional index is corrected to effectively reduce the fluctuation of the bias estimation. Finally, the simulation results of two different scenarios are conducted, and the results show that the new method has significant improvements in estimation accuracy and algorithm stability compared with the existing algorithms.