Autostereotypes

BackgroundTo further understand the implementation of hyperparameters re-estimation technique in Bayesian hierarchical model, we added two more prior assumptions over the weight in BayesPI, namely Laplace prior and Cauchy prior, by using the evidence approximation method. In addition, we divided hyperparameter (regularization constants α of the model) into multiple distinct classes based on either the structure of the neural networks or the property of the weights.ResultsThe newly implemented BayesPI was tested on both synthetic and real ChIP-based high-throughput datasets to identify the corresponding protein binding energy matrices. The results obtained were encouraging: 1) there was a minor effect on the quality of predictions when prior assumptions over the weights were altered (e.g. the prior probability distributions to the weights and the number of classes to the hyperparameters) in BayesPI; 2) however, there was a significant impact on the computational speed when tuning the weight prior in the model: for example, BayesPI with a Laplace weight prior achieved the best performance with regard to both the computational speed and the prediction accuracy.ConclusionsFrom this study, we learned that it is absolutely necessary to try different prior assumptions over the weights in Bayesian hierarchical model to design an efficient learning algorithm, though the quality of the final results may not be associated with such changes. In future, the evidence approximation method can be an alternative to Monte Carlo methods for computational implementation of Bayesian hierarchical model.

The human visual system can extract information on surface reflectance (lightness) from light intensity; this, however, confounds information on reflectance and illumination. We hypothesized that the visual system, to solve this lightness problem, utilizes the internally held prior assumption that illumination falls from above. Experiment 1 showed that an upward-facing surface is perceived to be darker than a downward-facing surface, proving our hypothesis. Experiment 2 showed the same results in the absence of explicit illumination cues. The effect of the light-from-left prior assumption was not observed in Experiment 3. The upward- and downward-facing surface stimuli in Experiments 1 and 2 showed no difference in a two-dimensional configuration or three-dimensional structure, and the participants' perceived lightness appeared to be affected by the observers' prior assumption that illumination is always from above. Other studies have not accounted for this illusory effect, and this study's finding provides additional insights into the study of lightness perception.

Exploratory factor analysis derives its key ideas from many sources. From the Greek rationalists and atomists comes the idea that appearance is to be explained by something not observed. From Aristotle comes the idea of induction and seeking common features of things as explanations of them. From Francis Bacon comes the idea of an automatic algorithm for inductively discovering common causes. From Descartes come the ideas of analysis and synthesis that underlie the emphasis on analysis of variables into orthogonal or linearly independent factors and focus on reproducing (synthesizing) the correlation matrix from the factors. From empiricist statisticians like Pearson and Yule comes the idea of exploratory, descriptive statistics. Also from the empiricist heritage comes the false expectation some have that factor analysis yields unique and unambiguous knowledge without prior assumptions -- the inductivist fallacy. This expectation founders on the indeterminacy of factors, even after their loadings are defined by rotation. Indeterminacy is unavoidable in the interpretation of common factors because the process of interpretation is inductive and inductive inferences are not uniquely determined by the data on which they are based. But from Kant we learn not to discard inductive inferences but to treat them as hypotheses that must be tested against additional data to establish their objectivity. And so the conclusions of exploratory factor analyses are never complete without a subsequent confirmatory analysis with additional variables and new data.

Accurate characterization of aquifer hydraulic properties is crucial for groundwater resource management and pollution control. Most traditional inverse methods for aquifer characterization rely on unbiased prior assumptions to make robust estimation; however, real‐world applications frequently involve biased priors that can significantly compromise characterization accuracy. To mitigate this issue, this study introduces a hybrid prior strategy that combines multiple plausible prior assumptions to form a comprehensive prior. This strategy is implemented within a deep learning‐based ensemble smoother, termed , enabling robust estimation of unknown, heterogeneous aquifer hydraulic properties without relying on unbiased prior assumptions. We systematically evaluate the performance of under unbiased, biased, and hybrid priors across hydraulic conductivity fields characterized by log‐Gaussian, channelized, and three‐facies distributions. Using the Kalman‐based ensemble smoother () as a benchmark, results demonstrate that the hybrid prior strategy significantly enhances the robustness of aquifer characterization under biased prior assumptions, with consistently outperforming in terms of both accuracy and stability. Notably, maintains high effectiveness even when the hybrid prior ensemble excludes unbiased samples, highlighting its adaptability and reliability in complex groundwater systems.

The design of the built environment greatly impacts how all types of individuals and populations actively participate in their daily lives. Lack of access in the built environment for disabled populations remains a daily reality, negatively impacting engagement and life satisfaction, leading to isolation, loneliness, and depression. A university in the Northeastern United States sought to expand current constructs of the end-user and environment within a universal design (UD) perspective. On an eight-month inaugural interprofessional collaborative co-design experience, third-year occupational therapy doctoral (OTD) students were embedded in a first-year masters of industrial design (MSID) curriculum, which ran the course of the academic calendar (two consecutive semesters: Fall and Spring). Primary aims wanted to determine, via an interrupted time-series quantitative design, if embedding OTD students within the industrial design curriculum influenced the MSID students’ prior assumptions, understanding of disability and enhanced their willingness to create more inclusive final products. Quantitative findings indicated that it was difficult to capture the meaningful change that occurred in the doctoral capstone program experience with the existing psychometric tools available. Anecdotal mixed-method findings indicated that informal interprofessional learning experiences in the classroom, such as lectures and learning activities created and facilitated by the OTD students and delivered in real-time, broadened and enhanced the MSID students’ knowledge surrounding disability and accessibility in a more nuanced way than the chosen quantitative survey tools were constructed to capture. A detailed literature review and description of the program have been provided, along with suggestions to capture meaningful outcomes for longer-term interdisciplinary collaborations.

This study examines the sensitivity of the posterior mean to change in the prior assumptions. Three plausible choices of prior which include informative, relative-non informative and non-informative priors are considered. The paper considers information level for a prior to cause a notable change in the Bayesian posterior point estimate. The study develops a framework for evaluating a bound for a robust posterior point estimate. The Ellipsoid Bound theorem is employed to derive the Ellipsoid Bound for an independent normal gamma prior distribution. The proposed modification ellipsoid bound for the large prior was establised by varrying different variance co-variance sizes for the independent normal gamma prior. This bound represents the range for the posterior mean when is insensitive and when it’s sensitive in both location and spread. The result shows that; for a large prior parameter value (greater than the OLS estimate) with a positive definite prior variance covariance matrix, and prior parameter values interval which contains the OLS estimate then, the posterior estimate will be less than both the OLS and the prior estimates. Similarly, if the lower bound of the prior parameter values range is greater than the OLS estimate then: The posterior estimate will be greater than the OLS estimate but smaller than the prior estimate. Furthermore, it is observed that no matter the degrees of confidence in the prior values, data information is powerful enough to modify it.

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

Relaxation and antiplasticization measurements in trehalose–glycerol mixtures – A model formulation for protein preservation

Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects

Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for assessing parameter uncertainties in situations where there is also uncertainty in the underlying model. We apply model averaging to the estimation of the parameters associated with the primordial power spectra of curvature and tensor perturbations. We use CosmoNest and MultiNest to compute the model evidences and posteriors, using cosmic microwave data from WMAP, ACBAR, BOOMERanG, and CBI, plus large-scale structure data from the SDSS DR7. We find that the model-averaged 95% credible interval for the spectral index using all of the data is $0.940<{n}_{\mathrm{s}}<1.000$, where ${n}_{\mathrm{s}}$ is specified at a pivot scale $0.015\text{ }\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$. For the tensors model averaging can tighten the credible upper limit, depending on prior assumptions.

In a recent commentary on statistical inference, Batterham and Hopkins advocated an approach to statistical inference centered on expressions of uncertainty in parameters. After criticizing an approach to statistical inference driven by null hypothesis testing, they proposed a method of "magnitude-based" inference and then claimed that this approach is essentially Bayesian but with no prior assumption about the true value of the parameter. In this commentary, after we address the issues raised by Batterham and Hopkins, we show that their method is "approximately" Bayesian and rather than assuming no prior information their approach has a very specific, but hidden, joint prior on parameters. To correctly adopt the type of inference advocated by Batterham and Hopkins, sport scientists need to use fully Bayesian methods of analysis.

Fractures are an important fabric element in many tight gas reservoirs because they provide the necessary channels for fluid flow in rocks which usually have low matrix permeabilities. Several sandstone samples of such a reservoir type were chosen for a combined study of rock fabric elements and petrophysical properties. Geological investigations of the distribution and orientation of the fractures and sedimentary layering were performed. In addition, laboratory measurements were carried out to determine the directional dependence of the permeability and P‐wave velocities. Higher permeability values are generally in the plane of the nearly horizontal sedimentary layering with regard to the core axis. With the occurrence of subvertical fractures, however, the highest permeabilities were determined to be parallel to the core axis.Compressional wave velocities were measured on spherical samples in more than 100 directions to get the VPsymmetry without prior assumptions. Below 50 MPa confining pressure, all samples show a monoclinic symmetry of the P wave velocity distribution, caused by sedimentary layering, fractures, and crossbedding. At higher confining pressure, sedimentary layering is approximately the only effective fabric element, resulting in a more transverse isotropic VPsymmetry. Using the geological‐petrophysical model introduced here, the complex symmetry of the VPdistributions can only be explained by the rock fabric elements. Furthermore, water saturation increases the velocities and decreases the anisotropy but does not change VPsymmetry. This indicates that at this state, all fabric elements, including the fractures, have an influence on P‐wave velocity distribution.

This study presents the SHGBA framework, integrating a modified Bat Algorithm (BA) with the Second Horizontal Gradient (SHG) technique to enhance geothermal reservoir characterization through self-potential (SP) data inversion. The SHGBA approach leverages SHG's ability to suppress regional effects and BA's global search to precisely estimate subsurface parameters like amplitude coefficient (K), depth (z), location (xo), shape factor (q), and polarization angle (Ɵ). Unlike conventional SP inversion approaches, SHGBA refines SHG-derived anomalies using BA's global optimization, reducing noise and improving accuracy in geothermal modeling with minimal reliance on prior assumptions. Tested on synthetic models under noise-free and noisy (5% and 10%) conditions, as well as multi-source models with overlapping anomalies, SHGBA consistently recovered parameters, proving its robustness. When applied SP data from Hi'iaka dike surveys (1973-2012) at Kilauea Volcano, Hawaii, SHGBA successively characterized basaltic dike intrusion parameters and tracked temporal thermal changes. Unlike traditional techniques prone to local minima and reliant extensive prior assumptions, SHGBA's population-based optimization thoroughly explores parameter space, excelling in nonlinear and multi-parameters SP inversion. Requiring minimal prior constraints, SHGBA is well-suited for geothermal exploration, volcanic monitoring, and reservoir management, enhancing drilling efficiency, monitoring, and sustainable geothermal energy production.

This chapter presents a number of book reviews which touch on the subjects of antisemitism, the Holocaust, and genocide. Topics covered by the books reviewed include Israeli Jewish society after the Holocaust, the annihilation of the Jews in eastern Poland following Nazi invasion, and the two major concentration camp trials that preceded and immediately followed the International Military Tribunal (IMT) proceedings. Other books reviewed are about the role of religion in survivors' lives, Nazi racial policy in Poland, the interaction between Jews and Germans on the black market, the juridical ways in which victors and vanquished have dealt with mass violence, and American and American Jewish responses to Nazi outrages in the 1930s. Another book reviewed focuses on the trial of 61 personnel of the Mauthausen-Gusen concentration camp in Austria. Other books look at the Jewish experience after the Holocaust during the early post-war years in Europe, the revival of Yiddish culture among the displaced persons (DPs), the loosely linked diaspora of Jews from Vilna and Jewish sites of memory in the present-day city, prior assumptions about Jewish displaced persons (DPs) and Holocaust survivors, various aspects of the Yishuv during the Holocaust era, and the trial of Nazi war criminals in the Nuremberg military tribunals. The final couple of books reviewed analyze the significance of Lodz to Jewish life in Poland in the immediate aftermath of the Holocaust and American psychologist David Broder's interviews with Holocaust survivors in displaced person (DP) camps and shelters in various areas in Europe in 1946.

An unsupervised methodology is introduced for quantitative zonal rate allocation in wellbores equipped with temporary or permanent fiber optic cables for acoustic sensing. This approach aims to mitigate key limitations of conventional data-driven methods, which rely heavily on labeled training data and prior knowledge and often struggle to accommodate temporal and spatial variability in acoustic signatures originating from perforations or flow control devices distributed across the lower completion. The proposed framework employs an adaptive, unsupervised learning model capable of autonomous zonal flow allocation using distributed acoustic sensing data. At each rate change event, the algorithm leverages differential acoustic responses along the fiber, identifying how zone-specific signatures evolve relative to total flow variations. This enables the development of unique flow-acoustic relationships for individual zones without prior assumptions. Furthermore, the self-learning model continuously updates zonal contributions, adapting to changing downhole environments and completion characteristics. The model is applicable across all completion types—including open hole scenarios—provided acoustic data is available. Validation using synthetic wells demonstrates successful allocation of flow rates across multiple zones, aligning with expected injection and production profiles. This technique offers potential value for reservoir management, production optimization, and well integrity monitoring by enabling continuous, quantitative zonal flow allocation. The novelty of the method lies in its unsupervised learning framework, which leverages acoustic data during rate change events to dynamically calibrate the relationship between flow-induced noise and zonal flow rates—surpassing the limitations of empirical and supervised models. Beyond allocation, the learned acoustic-flow relationships offer diagnostic value, enabling insights into completion performance and zonal behavior over time.