Single Image LDR to HDR Conversion using Conditional Diffusion

Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes, resulting in under-/overexposed images. This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights while reconstructing High Dynamic Range (HDR) images. We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning. Moreover, we introduce a new loss function for LDR-HDR translation tasks, termed Exposure Loss. This loss helps direct gradients in the opposite direction of the saturation, further improving the results' quality. By conducting comprehensive quantitative and qualitative experiments, we have effectively demonstrated the proficiency of our proposed method. The results indicate that a simple conditional diffusion-based method can replace the complex camera pipeline-based architectures

Role of modified biophysical profile in the management of post term pregnancy

Background: Prolonged gestation complicates 5% to 10% of all pregnancies and confers increased risk to both the fetus and mother. In the west about 18% of all singleton pregnancies persist beyond 41 weeks, 10% (range, 3% to 14%) continue beyond 42 weeks and 4% (range, 2% to 7%) continue beyond 43 completed weeks in the absence of an obstetric intervention. The risks for prolonged and post-term pregnancy include obesity, nulliparity, maternal age >30 years. Apart from these racial and ethnic differences have also been cited to be the reasons for higher risk of prolonged and post-term pregnancy. Post term pregnancies are associated with various maternal and neonatal complications.Methods: A prospective study was carried out at Department of Obstetrics and Gynaecology, Command Hospital, Central Command, Lucknow. 100 patients were selected and divided into two groups and were followed up till the delivery.  Data so collected was subjected to analysis using Statistical Package for Social Sciences version 15.0.Results: Majority of women enrolled in the study were aged above 25 years. Majority of women enrolled in the study were primigravida (67%). The Mean BMI of women enrolled in the study was 24.2±3.43 kg/m2 and the expectant and control groups were matched demographically and anthropometrically. The compromised modified biophysical profile was recorded in 33 (66%) of women in expectant group. Rate of caesarean delivery was 30% in expectant and 46% in control group. In the expectant group, AFD was the most common indication for caesarean section while control group had NPOL as the most common indication for caesarean section. In the expectant group, mean AFI showed a declining trend with increasing gestational age.Conclusions: It was concluded that expectant management using modified biophysical profile (MBPP) does not provide an additional value over prophylactically managed pregnancies. Although cesarean rate and NICU admission rates were lower in expectant group as compared to control group yet the utility of MBPP in expectant management could not be proven and needs further assessment in larger studies or pooled clinical trials

A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence

We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph neural network that processes an augmented graph to estimate the correspondence. We have designed a novel conditional refinement scheme to find the exact correspondence when certain conditions are satisfied. We further develop a graph neural network that takes the aligned meshes and the time value as input and fuses this information to process further and generate the desired result. Using motion capture datasets and human mesh designing software, we create a large-scale synthetic dataset consisting of temporal sequences of human meshes in motion. Our results demonstrate that our approach generates realistic deformation of body parts given complex inputs

Learning Robust Deep Visual Representations from EEG Brain Recordings

Decoding the human brain has been a hallmark of neuroscientists and Artificial Intelligence researchers alike. Reconstruction of visual images from brain Electroencephalography (EEG) signals has garnered a lot of interest due to its applications in brain-computer interfacing. This study proposes a two-stage method where the first step is to obtain EEG-derived features for robust learning of deep representations and subsequently utilize the learned representation for image generation and classification. We demonstrate the generalizability of our feature extraction pipeline across three different datasets using deep-learning architectures with supervised and contrastive learning methods. We have performed the zero-shot EEG classification task to support the generalizability claim further. We observed that a subject invariant linearly separable visual representation was learned using EEG data alone in an unimodal setting that gives better k-means accuracy as compared to a joint representation learning between EEG and images. Finally, we propose a novel framework to transform unseen images into the EEG space and reconstruct them with approximation, showcasing the potential for image reconstruction from EEG signals. Our proposed image synthesis method from EEG shows 62.9% and 36.13% inception score improvement on the EEGCVPR40 and the Thoughtviz datasets, which is better than state-of-the-art performance in GAN.Comment: Accepted in WACV 202

Search Me Knot, Render Me Knot: Embedding Search and Differentiable Rendering of Knots in 3D

We introduce the problem of knot-based inverse perceptual art. Given multiple target images and their corresponding viewing configurations, the objective is to find a 3D knot-based tubular structure whose appearance resembles the target images when viewed from the specified viewing configurations. To solve this problem, we first design a differentiable rendering algorithm for rendering tubular knots embedded in 3D for arbitrary perspective camera configurations. Utilizing this differentiable rendering algorithm, we search over the space of knot configurations to find the ideal knot embedding. We represent the knot embeddings via homeomorphisms of the desired template knot, where the homeomorphisms are parametrized by the weights of an invertible neural network. Our approach is fully differentiable, making it possible to find the ideal 3D tubular structure for the desired perceptual art using gradient-based optimization. We propose several loss functions that impose additional physical constraints, ensuring that the tube is free of self-intersection, lies within a predefined region in space, satisfies the physical bending limits of the tube material and the material cost is within a specified budget. We demonstrate through results that our knot representation is highly expressive and gives impressive results even for challenging target images in both single view as well as multiple view constraints. Through extensive ablation study we show that each of the proposed loss function is effective in ensuring physical realizability. To the best of our knowledge, we are the first to propose a fully differentiable optimization framework for knot-based inverse perceptual art. Both the code and data will be made publicly available.Comment: Work in progres

Organogel: A Propitious Carman in Drug Delivery System

A gel is a semi-solid formulation having an external solvent phase that is either apolar (organogels) or polar (hydrogels) that is immobilized inside the voids contained in a three-dimensional networked structure. Organogels are bi-continuous systems composed of apolar solvents and gelators. When used at a concentration of around 15%, the gelators form self-assembled fibrous structures that become entangled with one another, resulting in the formation of a three-dimensional networked structure. The resulting three-dimensional networked structure blocks the flow of the external apolar phase. Sterol, sorbitan monostearate, lecithin, and cholesteryl anthraquinone derivatives are examples of gelators. The unique characteristics such as thermo-reversibility, viscoelasticity, and versatility impart a longer shelf-life, prolonged drug release, and patient compliance. These characteristics can easily be adjusted by simple formulation modifications, resulting in highly-structured architectures. Organogels are more likely to be used in various types of delivery systems because of their ability to entrap both hydrophilic and hydrophobic molecules inside their structure. Their combination with other materials allows for tailoring their potential as dosage forms. Organogels have potential applicability in numerous ways; hence this article discusses the various aspects of it

Role of modified biophysical profile in the management of post term pregnancy

Background: Prolonged gestation complicates 5% to 10% of all pregnancies and confers increased risk to both the fetus and mother. In the west about 18% of all singleton pregnancies persist beyond 41 weeks, 10% (range, 3% to 14%) continue beyond 42 weeks and 4% (range, 2% to 7%) continue beyond 43 completed weeks in the absence of an obstetric intervention. The risks for prolonged and post-term pregnancy include obesity, nulliparity, maternal age >30 years. Apart from these racial and ethnic differences have also been cited to be the reasons for higher risk of prolonged and post-term pregnancy. Post term pregnancies are associated with various maternal and neonatal complications.Methods: A prospective study was carried out at Department of Obstetrics and Gynaecology, Command Hospital, Central Command, Lucknow. 100 patients were selected and divided into two groups and were followed up till the delivery.  Data so collected was subjected to analysis using Statistical Package for Social Sciences version 15.0.Results: Majority of women enrolled in the study were aged above 25 years. Majority of women enrolled in the study were primigravida (67%). The Mean BMI of women enrolled in the study was 24.2±3.43 kg/m2 and the expectant and control groups were matched demographically and anthropometrically. The compromised modified biophysical profile was recorded in 33 (66%) of women in expectant group. Rate of caesarean delivery was 30% in expectant and 46% in control group. In the expectant group, AFD was the most common indication for caesarean section while control group had NPOL as the most common indication for caesarean section. In the expectant group, mean AFI showed a declining trend with increasing gestational age.Conclusions: It was concluded that expectant management using modified biophysical profile (MBPP) does not provide an additional value over prophylactically managed pregnancies. Although cesarean rate and NICU admission rates were lower in expectant group as compared to control group yet the utility of MBPP in expectant management could not be proven and needs further assessment in larger studies or pooled clinical trials

Under What Conditions Does Transverse Macrodispersion Exist in Groundwater Flow?

In recent years there has been vigorous debate whether asymptotic transverse macrodispersion exists in steady three-dimensional (3D) groundwater flows in the purely advective limit. This question is tied to the topology of 3D flow paths (termed the Lagrangian kinematics), specifically whether streamlines can undergo braiding motions or can wander freely in the transverse direction. In this study we determine which Darcy flows do admit asymptotic transverse macrodispersion for purely advective transport on the basis of the conductivity structure. We prove that porous media with smooth, locally isotropic hydraulic conductivity exhibit zero transverse macrodispersion under pure advection due to constraints on the Lagrangian kinematics of these flows, whereas either non-smooth or locally anisotropic conductivity fields can generate transverse macrodispersion. This has implications for upscaling locally isotropic porous media to the block scale as this can result in a locally anisotropic conductivity, leading to non-zero macrodispersion at the block scale that is spurious in that it does not arise for the fully resolved Darcy scale flow. We also show that conventional numerical methods for computation of particle trajectories do not explicitly preserve the kinematic constraints associated with locally isotropic Darcy flow, and propose a novel psuedo-symplectic method that preserves these constraints. These results provide insights into the mechanisms that govern transverse macrodispersion in groundwater flow, and unify seemingly contradictory results in the literature in a consistent framework. These insights call into question the ability of smooth, locally isotropic conductivity fields to represent flow and transport in real heterogeneous porous media.The authors thank the reviewers for their constructive feedback which has improved the manuscript. M.D. acknowledges the support of the Spanish Research Agency (https://doi.org/10.13039/501100011033), Spanish Ministry of Science and Innovation and European Regional Development Fund “A way of making Europe” through Grants CEX2018-000794-S and HydroPore PID2019-106887GB-C31. Open access publishing facilitated by RMIT University, as part of the Wiley - RMIT University agreement via the Council of Australian University Librarians.Peer reviewe

Signature of coalescence during scalar mixing in heterogeneous flow fields

International audienceStretching of fluid elements by a heterogeneous flow field, such as the flow through a porous media or geophysical flows such as atmospheric or oceanic vortices, is known to enhance mixing rates of scalar fields[1]. While the mechanisms leading to the elongation of material lines are well understood, predicting mixing rates still remains a challenge particularly when there is a reconnection (or aggregation) between several parts of the mixing interface, leading, at large mixing time, to a so-called coalescence regime[1][2]. In this presentation, we numerically study this coalescence dynamics through scalar transport in two different flow fields, the Rankine vortex and Stokes flow through a periodic bead pack[3]. The former is typical of large-scale turbulent flows [4] whereas the second is generic of small-scale laminar flows in porous media [5]. Both flows show a net elongation of the mixing interfaces, although at very different rates. To solve the transport problem in these flows, we use a Lagrangian method (the diffusive strip method[6]). This method allows us to reconstruct, at high resolution, the scalar concentration fields and to compute the evolution of the distribution of concentrations levels, scalar dissipation rate and scalar power spectrum in time. The signature of coalescence is clearly observed in both flows and we assess the influence of coalescence on the difference in mixing behaviour for the two flows. We finally discuss how coalescence may affect the reaction kinetics of mixing-limited reactive flows. The analysis proposed sheds light on fundamental aspects of transport and mixing in earth surface and subsurface flows.[1] Emmanuel Villermaux. Mixing versus stirring. Annual Review of Fluid Mechanics, 51:245–273, 2019.[2] Tanguy Le Borgne, Marco Dentz, and Emmanuel Villermaux. The lamellar description of mixing in porous media. Journal of Fluid Mechanics, 770:458–498, 2015.[3] Régis Turuban, David R Lester, Tanguy Le Borgne, and Yves Méheust. Space-group symmetries generate chaotic fluid advection in crystalline granular media. Physical review letters, 120(2):024501, 2018.[4] RT Pierrehumbert. Large-scale horizontal mixing in planetary atmospheres. Physics of Fluids A: Fluid Dynamics, 3(5):1250–1260, 1991.[5] Brian Berkowitz, Andrea Cortis, Marco Dentz, and Harvey Scher. Modeling non-fickian transport in geological formations as a continuous time random walk. Reviews of Geophysics, 44(2), 2006.[6] Patrice Meunier and Emmanuel Villermaux. The diffusive strip method for scalar mixing in two dimensions. Journal of fluid mechanics, 662:134–172, 2010