### Recent Submissions

• #### RETINOBLASTOMA RELATED (RBR) interaction with key factors of the RNA-directed DNA methylation (RdDM) pathway

(Cold Spring Harbor Laboratory, 2022-01-07) [Preprint]
• #### Asymptotic Derivation of Multicomponent Compressible Flows with Heat Conduction and Mass Diffusion

(arXiv, 2021-12-27) [Preprint]
A Type-I model of a multicomponent system of fluids with non-constant temperature is derived as the high-friction limit of a Type-II model via a Chapman-Enskog expansion. The asymptotic model is shown to fit into the general theory of hyperbolic-parabolic systems, by exploiting the entropy structure inherited through the asymptotic procedure. The exact computations are specified in the case of a two-component system. Finally, two convergence results for smooth solutions are presented, from the system with mass-diffusion and heat conduction to the corresponding system without mass-diffusion but including heat conduction and to its hyperbolic counterpart.
• #### Unbiased Parameter Inference for a Class of Partially Observed Lévy-Process Models

(arXiv, 2021-12-27) [Preprint]
We consider the problem of static Bayesian inference for partially observed Lévy-process models. We develop a methodology which allows one to infer static parameters and some states of the process, without a bias from the time-discretization of the afore-mentioned Lévy process. The unbiased method is exceptionally amenable to parallel implementation and can be computationally efficient relative to competing approaches. We implement the method on S &P 500 log-return daily data and compare it to some Markov chain Monte Carlo (MCMC) algorithms.
• #### Optimization and uncertainty quantification model for time-continuous geothermal energy extraction undergoing re-injection

(arXiv, 2021-12-10) [Preprint]
Geothermal field modeling is often associated with uncertainties related to the subsurface static properties and the dynamics of fluid flow and heat transfer. Uncertainty quantification using simulations is a useful tool to design optimum field-development and to guide decision-making. The optimization process includes assessments of multiple time-dependent flow mechanisms, which are functions of operational parameters subject to subsurface uncertainties. This process requires careful determination of the parameter ranges, dependencies, and their probabilistic distribution functions. This study presents a new approach to assess time-dependent predictions of thermal recovery and produced-enthalpy rates, including uncertainty quantification and optimization. We use time-continuous and multi-objective uncertainty quantification for geothermal recovery, undergoing a water re-injection scheme. The ranges of operational and uncertainty parameters are determined from a collected database, including 135 geothermal fields worldwide. The uncertainty calculation is conducted non-intrusively, based on a workflow that couples low-fidelity models with Monte Carlo analysis. Full-physics reservoir simulations are used to construct and verify the low-fidelity models. The sampling process is performed with Design of Experiments, enhanced with space-filling, and combined with analysis of covariance to capture parameter dependencies. The predicted thermal recovery and produced-enthalpy rates are then evaluated as functions of the significant uncertainty parameters based on dimensionless groups. The workflow is applied for various geothermal fields to assess their optimum well-spacing in their well configuration. This approach offers an efficient and robust workflow for time-continuous uncertainty quantification and global sensitivity analysis applied for geothermal field modeling and optimization.
• #### Local Mortality Impacts Due to Future Air Pollution Under Climate Change Scenarios

(Submitted to Journal of Science of Total Environment, 2021-12-10) [Preprint]
The health impacts of global climate change mitigation will affect local populations differently. We aimed to quantify the local health impacts due to fine particles (PM 2.5 ) under the governance arrangements embedded in the Shared Socioeconomic Pathways (SSPs1-5) under two greenhouse gas concentration scenarios (Representative Concentration Pathways (RCPs) 2.6 and 8.5) in local populations of Mozambique, India, and Spain.MethodsWe simulated the SSP-RCP scenarios using the Global Change Analysis Model, which was linked to the TM5-FASST model to estimate PM 2.5 levels. PM 2.5 levels were calibrated with local measurements. We used comparative risk assessment methods to estimate attributable premature deaths due to PM 2.5 linking local population and mortality data with PM 2.5 –mortality relationships from the literature. We incorporated population projections under the SSPs in sensitivity analysis.ResultsPM 2.5 attributable burdens in 2050 differed across SSP-RCP scenarios, and scenario-sensitivity varied across populations. Future attributable mortality burden of PM 2.5 was highly sensitive to assumptions about how populations will change according to SSP. SSPs reflecting high challenges for adaptation (SSPs 3 and 4) consistently resulted in the highest PM 2.5 attributable burdens mid-century.DiscussionOur analysis of local PM 2.5 attributable premature deaths under SSP-RCP scenarios in three local populations highlights the importance of both socioeconomic development and climate policy in reducing the health burden from air pollution. Sensitivity of future PM 2.5 mortality burden to SSPs was particularly evident in low- and midlle- income country settings due either to high air pollution levels or dynamic populations.
• #### CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions

(arXiv, 2021-12-09) [Preprint]
The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images. However, such editing operations are either trained with semantic supervision or described using human guidance. In another development, the CLIP architecture has been trained with internet-scale image and text pairings and has been shown to be useful in several zero-shot learning settings. In this work, we investigate how to effectively link the pretrained latent spaces of StyleGAN and CLIP, which in turn allows us to automatically extract semantically labeled edit directions from StyleGAN, finding and naming meaningful edit operations without any additional human guidance. Technically, we propose two novel building blocks; one for finding interesting CLIP directions and one for labeling arbitrary directions in CLIP latent space. The setup does not assume any pre-determined labels and hence we do not require any additional supervised text/attributes to build the editing framework. We evaluate the effectiveness of the proposed method and demonstrate that extraction of disentangled labeled StyleGAN edit directions is indeed possible, and reveals interesting and non-trivial edit directions.
• #### On the Stability of Positive Semigroups

(arXiv, 2021-12-07) [Preprint]
The stability and contraction properties of positive integral semigroups on locally compact Polish spaces are investigated. We provide a novel analysis based on an extension of V-norm, Dobrushin-type, contraction techniques on functionally weighted Banach spaces for Markov operators. These are applied to a general class of positive and possibly time-inhomogeneous bounded integral semigroups and their normalised versions. Under mild regularity conditions, the Lipschitz-type contraction analysis presented in this article simplifies and extends several exponential estimates developed in the literature. The spectraltype theorems that we develop can also be seen as an extension of Perron-Frobenius and Krein-Rutman theorems for positive operators to time-varying positive semigroups. We review and illustrate in detail the impact of these results in the context of positive semigroups arising in transport theory, physics, mathematical biology and advanced signal processing.
• #### Neural Networks for Infectious Diseases Detection: Prospects and Challenges

(arXiv, 2021-12-07) [Preprint]
Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN's advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.
• #### Joint Posterior Inference for Latent Gaussian Models with R-INLA

(arXiv, 2021-12-06) [Preprint]
Efficient Bayesian inference remains a computational challenge in hierarchical models. Simulation-based approaches such as Markov Chain Monte Carlo methods are still popular but have a large computational cost. When dealing with the large class of Latent Gaussian Models, the INLA methodology embedded in the R-INLA software provides accurate Bayesian inference by computing deterministic mixture representation to approximate the joint posterior, from which marginals are computed. The INLA approach has from the beginning been targeting to approximate univariate posteriors. In this paper we lay out the development foundation of the tools for also providing joint approximations for subsets of the latent field. These approximations inherit Gaussian copula structure and additionally provide corrections for skewness. The same idea is carried forward also to sampling from the mixture representation, which we now can adjust for skewness.
• #### A review of diaphragmless shock tubes for interdisciplinary applications

(arXiv, 2021-12-05) [Preprint]
Shock tubes have emerged as an effective tool for applications in various fields of research and technology. The conventional mode of shock tube operation employs a frangible diaphragm to generate shockwaves. The last half-century has witnessed significant efforts to replace this diaphragm-bursting method with fast-acting valves. These diaphragmless methods have good repeatability, quick turnaround time between experiments, and produce a clean flow, free of diaphragm fragments in contrast to the conventional diaphragm-type operation. The constantly evolving valve designs are targeting shorter opening times for improved performance and efficiency. The present review is a compilation of the different diaphragmless shock tubes that have been conceptualized, developed, and implemented for various research endeavors. The discussions focus on essential factors, including the type of actuation mechanism, driver-driven configurations, valve opening time, shock formation distance, and operating pressure range, that ultimately influence the shockwave parameters obtained in the shock tube. A generalized mathematical model to study the behavior of these valves is developed. The advantages, limitations, and challenges in improving the performance of the valves are described. Finally, the present-day applications of diaphragmless shock tubes have been discussed, and their potential scope in expanding the frontiers of shockwave research and technology are presented.
• #### Snapshot HDR Video Construction Using Coded Mask

(arXiv, 2021-12-05) [Preprint]
This paper study the reconstruction of High Dynamic Range (HDR) video from snapshot-coded LDR video. Constructing an HDR video requires restoring the HDR values for each frame and maintaining the consistency between successive frames. HDR image acquisition from single image capture, also known as snapshot HDR imaging, can be achieved in several ways. For example, the reconfigurable snapshot HDR camera is realized by introducing an optical element into the optical stack of the camera; by placing a coded mask at a small standoff distance in front of the sensor. High-quality HDR image can be recovered from the captured coded image using deep learning methods. This study utilizes 3D-CNNs to perform a joint demosaicking, denoising, and HDR video reconstruction from coded LDR video. We enforce more temporally consistent HDR video reconstruction by introducing a temporal loss function that considers the short-term and long-term consistency. The obtained results are promising and could lead to affordable HDR video capture using conventional cameras.
• #### MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions

(arXiv, 2021-12-01) [Preprint]
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun to discover significant limitations in these datasets, suggesting that state-of-the-art techniques commonly overfit to hidden dataset biases. In this work, we present MAD (Movie Audio Descriptions), a novel benchmark that departs from the paradigm of augmenting existing video datasets with text annotations and focuses on crawling and aligning available audio descriptions of mainstream movies. MAD contains over 384,000 natural language sentences grounded in over 1,200 hours of video and exhibits a significant reduction in the currently diagnosed biases for video-language grounding datasets. MAD's collection strategy enables a novel and more challenging version of video-language grounding, where short temporal moments (typically seconds long) must be accurately grounded in diverse long-form videos that can last up to three hours.