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Gene-Gene and Gene-Environment Interactions in Case-Control Studies Based on Hierarchies of Dirichlet Processes
No full textIt is becoming increasingly clear that complex interactions among genes and environmental factors play crucial roles in triggering complex diseases. Thus, understanding such interactions is vital, which is possible only through statistical models that adequately account for such intricate, albeit unknown, dependence structures. In this article, we propose and develop a novel nonparametric Bayesian model for case-control genotype data using hierarchies of Dirichlet processes that offers a more realistic and nonparametric dependence structure among the genes, induced by the environmental variables. In this regard, we propose a novel and highly parallelisable MCMC algorithm that is rendered quite efficient by the combination of modern parallel computing technology, effective Gibbs sampling steps, retrospective sampling and Transformation based Markov Chain Monte Carlo (TMCMC). We devise appropriate Bayesian hypothesis testing procedures to detect the roles of genes and environment in case-control studies. Applying our ideas to 5 biologically realistic case-control genotype datasets simulated under distinct set-ups, we obtain encouraging results in each case. We finally apply our ideas to a real, myocardial infarction dataset, and obtain interesting results on gene-gene and gene environment interaction, that broadly agree with the results reported in the literature, but provide further important insights
Group-feature (Sensor) selection with controlled redundancy using neural networks
No full textIn this work, we present a novel embedded feature selection method based on a Multi-layer Perceptron (MLP) network and generalize it for group-feature or sensor selection problems, which can control the level of redundancy among the selected features or groups and it is computationally more efficient than the existing ones in the literature. Additionally, we have generalized the group lasso penalty for feature selection to encompass a mechanism for selecting valuable groups of features while simultaneously maintaining control over redundancy. We establish the monotonicity and convergence of the proposed algorithm, with a smoothed version of the penalty terms, under suitable assumptions. The effectiveness of the proposed method for both feature selection and group feature selection is validated through experimental results on various benchmark datasets. The performance of the proposed methods is compared with some state-of-the-art methods
Image registration for zooming: A statistically consistent local feature mapping approach
No full textImage registration is a widely used tool for matching two images of the same scene with one another. In the literature, several image registration techniques are available to register rigid-body and non-rigid-body transformations. One such important transformation is zooming. There are very few feature-based methods that address this particular problem. These methods fail miserably when there are only a limited number of point features available in the image. This paper proposes a feature-based approach that works with a feature that is readily available in almost all images, for registering two images of the same image object where one is a zoomed-in version of the other. In the proposed method, we first detect the possible edge points which we consider as features in both the reference and the zoomed image. Then, we map these features of the reference and the zoomed image with one another and find the relationship between them using a mathematical model. Finally, we use the relationship to register the zoomed-in image. This method outperforms some of the state-of-the-art methods in many occasions. Several numerical examples and some statistical properties justify that this method works well in many applications
InGARSS 2023 in Bangalore: Striking a Balance [Conference Reports]
No full textIn short, InGARSS 2023 was well balanced on all fronts and gave a memorable experience of a conference series success?fully transitioning from online to hybrid mode. The next edition of InGARSS is slated to be held in Goa in December 2024
Interaction intensity in strategic fitness: A quantifying yardstick of selection optimization for evolutionary game
No full textThe notion of the fitness of a strategy has been assimilated as the reproductive success in the evolutionary game. Initially, this fitness was tied to the game\u27s pay-off and the strategy\u27s relative frequency. However, density dependence becomes exigent in order to make ecologically reliable fitness. However, the contributions of each different type of interaction to the species\u27s overall growth process were surprisingly under-explored. This oversight has occasionally led to either more or less prediction of strategy selection compared to the actual possibility. Moreover, density regulation of the population has always been analysed in a general way compared to strategy selection. In this context, our study introduces the concept of mean relative death payoff, which helps in assessing interaction intensity coefficients and integrates them into strategic fitness. Based on this fitness function, we develop the frequency-density replicator dynamics, which eventually provides distinguishing criteria for directional and balancing selection. Our optimized, evolutionarily stable strategy emerges as a superior alternative to the conventional trade-off between selection forces and ecological processes. More significantly, mean relative death pay-off has both conditional and quantitative roles in getting a stable population size. As a case study, we have extensively analysed the evolution of aggression using the Hawk-Dove game. We have shown that pure Dove selection is always beneficial for species growth rather than pure Hawk selection, and the condition of selection is dependent on external mortality pressure. However, the condition of coexistence is independent of external mortality pressure, representing a strong evolutionary selection that optimizes population density governed by interaction intensity
Introducing Devsagar Sandstone Member: A revised stratigraphy of the Mesoproterozoic Chattisgarh basin, Central India
No full textAbstract: Chandarpur–Raipur sequence in Chattisgarh basin is represented as siliciclastic-dominated Chandarpur Group and carbonate-dominated Raipur Group. Here, we introduce ‘Devsagar Sandstone Member’, the only sandstone-dominated member in the carbonate-dominated Charmuria Formation of Raipur Group, that marks a period of rapid siliciclastic deposition identifying a phase of forced regression between two carbonate platforms of Charmuria–Chandi formations, thereby indicating a drastic change in palaeogeography of Raipur Group. In addition, this study revised the litho-stratigraphy of Mesoproterozoic Chattisgarh basin to clarify the confusion raised due to the existence of different stratigraphy in different basinal parts and different nomenclature for the same lithologic units. Detailed geological mapping with facies analysis in the eastern part of the basin manifests the entire basin-fill succession as part of the Chattisgarh basin itself, rather than sub-dividing some parts as Baradwar sub-basin and Singhora proto-basin. Singhora Group deposited in Singhora proto-basin has already been presented as equivalent of Chandarpur Group. Here we propose, Bamandihi–Saradih–Raigarh formations of Raipur Group in Baradwar sub-basin, as lateral equivalent of Gunderdehi–Chandi–Tarenga formations of Raipur Group and Sarnadih–Nandeli formations of Kharsiya Group in Chattisgarh basin. Inferred depositional environment and tectonic setting of Chattisgarh basin support the lithostratigraphic revision, which will help in basin analysis as well as intrabasinal–interbasinal correlation in regional and global contexts. Research highlights: Devsagar Sandstone Member introduced as the only sandstone-dominated member in carbonate-dominated Charmuria Formation of Raipur Group. Devsagar Sandstone Member represents a tidal shelf in between two carbonate ramp platforms (Charmuria and Chandi), marking a period of rapid siliciclastic deposition and the only phase of forced regression in overall sea-level rising scenario of the carbonate-dominated Raipur Group. Stratigraphy of Chattisgarh basin revised. The entire Chattisgarh succession is represented as deposits of Chattisgarh basin only, without further subdivision into sub-basin and/or proto-basin, thus resolving the stratigraphic and basinal correlation problem
Latent class analysis of multigroup heterogeneity in propensity for academic dishonesty
No full textLatent class analysis (LCA) is a cross-sectional latent variable mixture modeling (LVMM) approach. Like all LVMM approaches, LCA aims to find heterogeneity within the population by identifying homogenous subgroups of individuals, with each subgroup (called latent class) possessing a unique set of characteristics that differentiate it from other subgroups. LCA can be carried out with categorical latent and indicator variables. But, LCA is unable to examine the association between respective items and the latent variable among categories of individuals. Multiple-group LCA, in particular, is a useful extension of LCA which enables the testing of homogeneity of the class patterns between groups of the individual through a series of constraints. In this paper, we have performed a multi-group latent class analysis for measuring self reported academic dishonesty among the students of University of Jammu. From the analysis, three general behaviors of academic cheaters are identified as rare, frequent, and instant cheaters. Further, from the multi-group LCA, it is envisaged that female students of University of Jammu are more instantaneous cheaters than male students. Students who are self-reported cheaters from sciences and humanities of the University of Jammu are persistent in cheating whereas from professional courses they are more occasional
Local incentive compatibility on gross substitutes and other non-convex type-spaces
No full textWe explore the equivalence of local incentive compatibility (LIC) (Carroll 2012) and incentive compatibility (IC) in non-convex type-spaces. We provide a sufficient condition on a type-space called richness for the said equivalence. Using this result, we show that LIC and IC are equivalent on large class of non-convex type-spaces which include the gross substitutes type-space and the generalized gross substitutes and complements type-space. Finally, we provide a geometric property consisting of three conditions for the equivalence of LIC and IC, and show that all the conditions are indispensable
MAB-BMC: A Formal Verification Enhancer by Harnessing Multiple BMC Engines Together
No full textIn recent times, Bounded Model Checking (BMC) engines have gained wide prominence in formal verification. Different BMC engines exist, differing in their optimization, representations and solving mechanisms used to represent and navigate the underlying state transition of the given design to be verified. The objective of this article is to examine if combinations of BMC engines can help to combine their strengths. We propose an approach that can create a sequencing of BMC engines that can reach better depth in formal verification, as opposed to executing them alone for a specified time. Our approach uses machine learning, specifically, the Multi-Armed Bandit paradigm of reinforcement learning, to predict the best-performing BMC engine for a given unrolling depth of the underlying circuit design. We evaluate our approach on a set of benchmark designs from the Hardware Model Checking Competition (HWMCC) benchmarks and show that it outperforms the state-of-the-art BMC engines in terms of the depth reached or time taken to deduce a property violation. The synthesized BMC engine sequences reach better depths than HWMCC results and the state-of-the-art technique, super-deep, for more than 80% of the cases. It also outperforms single engine runs for more than 92% of the cases where a property violation is not found within a given time duration. For designs where property violations are found within the given time duration, the synthesized sequences found the property violation in a lesser time than HWMCC for all the designs and outperformed both super-deep and single engine runs for more than 87% of the designs
Methodological issues: socioeconomic status
No full textSocioeconomic status (SES) is a construct which influences development, health, well-being and cognition of an individual or a family or a community. Existing methods of measuring SES have methodological limitations. The paper suggests remedial measures by (i) composite approach using arithmetic aggregation of indicator scores, transformed to follow normal distributions and by (ii) composite index approach using multiplicative aggregation of ratio of indicator scores at t-th time and base periods. Desired properties satisfied by each approach and application areas are addressed. Each method avoids scaling, selection of weights, major limitations of SES using summative scores and expresses SES by a continuous variable satisfying desirable properties including plotting of progress/decline of SES across time, statistical test of hypothesis, identification of critical indicators, etc. Method-2 is preferred based on theoretical advantages like satisfaction of time-reversal test, formation of chain indices, etc. Methodological novelties include: Computation of from a single administration offering benefits like finding true score variance of test; testing of hypothesis Factorial validity and Cronbach’s alpha in terms of largest eigenvalue (. Proposed methods with wide application areas help in improved measure of socioeconomic status along with better measures of reliability, validity are recommended