Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

In this paper, we propose a model-based method to study conditional dependence be- tween response accuracy and response time (RT) with the diffusion IRT model. To this end, we extend the previously proposed model by introducing variability across persons and items in cognitive capacity and in the initial bias of the response processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. The first variability component in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. The second variability in the initial bias can account for the early changes in the response accuracy as a function of RTs given the person and item effects, producing the curvilinear conditional accuracy functions. We also provide a simulation study to validate the parameter recovery of the proposed model and two empirical applications to describe how to implement the model to study conditional dependency underlying data response accuracy and RTs.

Big data integration enhancement based on attributes conditional dependency and similarity index method

<abstract> <p>Big data has attracted a lot of attention in many domain sectors. The volume of data-generating today in every domain in form of digital is enormous and same time acquiring such information for various analyses and decisions is growing in every field. So, it is significant to integrate the related information based on their similarity. But the existing integration techniques are usually having processing and time complexity and even having constraints in interconnecting multiple data sources. Many of these sources of information come from a variety of sources. Due to the complex distribution of many different data sources, it is difficult to determine the relationship between the data, and it is difficult to study the same data structures for integration to effectively access or retrieve data to meet the needs of different data analysis. In this paper, proposed an integration of big data with computation of attribute conditional dependency (ACD) and similarity index (SI) methods termed as ACD-SI. The ACD-SI mechanism allows using of an improved Bayesian mechanism to analyze the distribution of attributes in a document in the form of dependence on possible attributes. It also uses attribute conversion and selection mechanisms for mapping and grouping data for integration and uses methods such as LSA (latent semantic analysis) to analyze the content of data attributes to extract relevant and accurate data. It performs a series of experiments to measure the overall purity and normalization of the data integrity, using a large dataset of bibliographic data from various publications. The obtained purity and NMI ratio confined the clustered data relevancy and the measure of precision, recall, and accurate rate justified the improvement of the proposal is compared to the existing approaches.</p> </abstract>

Post-COVID-19 Fatigue: Potential Contributing Factors

Much of the spotlight for coronavirus disease 2019 (COVID-19) is on the acute symptoms and recovery. However, many recovered patients face persistent physical, cognitive, and psychological symptoms well past the acute phase. Of these symptoms, fatigue is one of the most persistent and debilitating. In this “perspective article,” we define fatigue as the decrease in physical and/or mental performance that results from changes in central, psychological, and/or peripheral factors due to the COVID-19 disease and propose a model to explain potential factors contributing to post-COVID-19 fatigue. According to our model, fatigue is dependent on conditional and physiological factors. Conditional dependency comprises the task, environment, and physical and mental capacity of individuals, while physiological factors include central, psychological, and peripheral aspects. This model provides a framework for clinicians and researchers. However, future research is needed to validate our proposed model and elucidate all mechanisms of fatigue due to COVID-19.

Fast and accurate structure probability estimation for simultaneous alignment and folding of RNAs with Markov chains

Motivation Simultaneous alignment and folding (SA&F) of RNAs is the indispensable gold standard for inferring the structure of non-coding RNAs and their general analysis. The original algorithm, proposed by Sankoff, solves the theoretical problem exactly with a complexity of $$O(n^6)$$ O ( n 6 ) in the full energy model. Over the last two decades, several variants and improvements of the Sankoff algorithm have been proposed to reduce its extreme complexity by proposing simplified energy models or imposing restrictions on the predicted alignments. Results Here, we introduce a novel variant of Sankoff’s algorithm that reconciles the simplifications of PMcomp, namely moving from the full energy model to a simpler base pair-based model, with the accuracy of the loop-based full energy model. Instead of estimating pseudo-energies from unconditional base pair probabilities, our model calculates energies from conditional base pair probabilities that allow to accurately capture structure probabilities, which obey a conditional dependency. This model gives rise to the fast and highly accurate novel algorithm Pankov (Probabilistic Sankoff-like simultaneous alignment and folding of RNAs inspired by Markov chains). Conclusions Pankov benefits from the speed-up of excluding unreliable base-pairing without compromising the loop-based free energy model of the Sankoff’s algorithm. We show that Pankov outperforms its predecessors LocARNA and SPARSE in folding quality and is faster than LocARNA.

Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.