Autofocusing is a critical step for high-quality microscopic imaging of specimens, especially for measurements that extend over time covering large fields of view Autofocusing is generally practic

/pdf/single-shot-autofocusing-of-microscopy-images-using-deep-1vqa1xa8su.pdf

Single-Shot Autofocusing of Microscopy Images Using Deep Learning

We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane. We illustrate the efficacy of Deep-R using various tissue sections that were imaged using fluorescence and brightfield microscopy modalities and demonstrate snapshot autofocusing under different scenarios, such as a uniform axial defocus as well as a sample tilt within the field-of-view. Our results reveal that Deep-R is significantly faster when compared with standard online algorithmic autofocusing methods. This deep learning-based blind autofocusing framework opens up new opportunities for rapid microscopic imaging of large sample areas, also reducing the photon dose on the sample.

Single-shot autofocusing of microscopy images using deep learning

Experience sampling studies often aim to capture social interactions. A central methodological question in such studies is whether to use event- or signal contingent sampling. The few existing studies on this issue have not taken into account that social interactions occur with specific interaction partners who have a unique relationship with the participant. We analyze data on social interactions of students to investigate the differences between the two sampling designs in (1) the number of captured interaction partners, (2) the characteristics of captured interaction partners, and (3) the quality of interactions taking into account that they take place with specific interaction partners. Apart from a larger quantity ofinteractions and interaction partners captured with event-contingent sampling, our analyses indicate subtle differences between the designs. They further highlight that the social role of an interaction partner explains a considerable proportion of variance (up to 20.5%) in perceived social support and interaction quality. 

Considering the ’With Whom’: Differences between event- and signal-contingent ESM data of person-specific social interactions.

There has been a considerable amount of literature on binomial regression models that utilize well-known link functions, such as logistic, probit, and complementary log-log functions. The conventional binomial model is focused only on a single parameter representing one probability of success. However, we often encounter data for which two different success probabilities are of interest simultaneously. For instance, there are several offensive measures in baseball to predict the future performance of batters. Under these circumstances, it would be meaningful to consider more than one success probability. In this article, we employ a bivariate binomial distribution that possesses two success probabilities to conduct a regression analysis with random effects being incorporated under a Bayesian framework. Major League Baseball data are analyzed to demonstrate our methodologies. Extensive simulation studies are conducted to investigate model performances.

/pdf/logistic-regression-model-for-a-bivariate-binomial-2fjhx55h.pdf

Logistic Regression Model for a Bivariate Binomial Distribution with Applications in Baseball Data Analysis

Experience sampling studies often aim to capture social interactions. A central methodological question in such studies is whether to use event- or signal-contingent sampling. The little existing research on this issue has not taken into account that social interactions occur with unique interaction partners (e.g., Anna or Tom). We analyze one week of social interaction data of 286 students from the University of Pittsburgh (60.8% male, mean age 19.2 years), taking into account the unique interaction partners of each student. Specifically, we investigate the differences between event- and signal contingent sampling in (1) the total number of unique interaction partners captured, as well as (2) the kinds of relationships, and (3) the quality of social interactions with these captured interaction partners. Apart from a larger quantity of interactions and unique interaction partners in the event-contingent sampling design, our analyses indicate subtle differences between the two designs when aiming to assess social interactions with more distant interaction partners, such as coworkers or strangers. Most importantly, in our analyses, specific interaction partners and social roles explained a considerable amount of variance in the quality of social interactions (up to 20.5%), suggesting that future research would benefit greatly from considering "with whom" individuals interact.

Considering the 'With Whom': Differences Between Event- and Signal-Contingent ESM Data of Person-Specific Social Interactions.

The aim of this paper is Comparison between models with and without intercept and Statement the beast one, and applying the method leverage point when we added the new point to the original data. W e are testing the significant intercept by using (t) test.

/pdf/comparison-between-models-with-and-without-intercept-324ep818xy.pdf

Comparison between Models With and Without Intercept

The aim of this paper is to select an appropriate ARIMA model for the time series after transforming the original responses. Box-Cox and Yeo-Johnson power transformation models were used on the response variables of two time series datasets of average temperatures and then diagnosed and built the appropriate ARIMA models for each time-series. The authors treat the results of the model fitting as a package in an attempt to decide and choose the best model by diagnosing the effect of the data transformation on the response normality, significant of estimated model parameters, forecastability and the behavior of the residuals. The authors conclude that the Yeo-Johnson model was more flexible in smoothing the data and contributedto accessing a simple model with good forecastability.

/pdf/on-the-use-of-the-power-transformation-models-to-improve-the-1leoh78c.pdf

On the Use of the Power Transformation Models to Improve the Temperature Time Series

Estimating Kappa Distribution Parameters: A Comparative Study of Maximum Likelihood and LQ-Moment Approaches

Multivariate Time Series Analysis of the USD/IQD Exchange Rate Using VAR, SVAR, and SVECM Models

In the realm of economic (financial) time series analysis, accurate prediction holds paramount importance. However, these data often suffer from the presence of noise, particularly in highly random and non-stationary datasets like stock market data. Dealing with noisy data makes predicting noise-free economic models exceedingly challenging. This research paper introduces an innovative shrinkage (thresholding) function designed to improve the efficiency of wavelet shrinkage denoising in the context of financial time series data. The proposed function is constructed based on an arctangent model with adjustable parameters meticulously chosen to ensure the function maintains continuous differentiability. The application of this novel shrinkage function effectively reduces noise in stock data. Employing R program for data analysis and figure plotting, the performance of this approach is rigorously validated using closing price data from the Shanghai Composite Index, spanning the period from January 4, 2000 to August 28, 2023. The experimental results demonstrate that the proposed thresholding function outperforms classical shrinkage functions (hard, soft, and nonnegative garrote) in both continuous derivative property and denoising efficacy.

An enhanced shrinkage function for denoising economic time series data using wavelet analysis

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