One Style is All you Need to Generate a Video

In this paper, we propose a style-based conditional video generative model. We introduce a novel temporal generator based on a set of learned sinusoidal bases. Our method learns dynamic representations of various actions that are independent of image content and can be transferred between different actors. Beyond the significant enhancement of video quality compared to prevalent methods, we demonstrate that the disentangled dynamic and content permit their independent manipulation, as well as temporal GAN-inversion to retrieve and transfer a video motion from one content or identity to another without further preprocessing such as landmark points

3D Flow Field Estimation and Assessment for Live Cell Fluorescence Microscopy

International audienceMotivation: The revolution in light sheet microscopy enables the concurrent observation of thousands of dynamic processes, from single molecules to cellular organelles, with high spatiotemporal resolution. However, challenges in the interpretation of multidimensional data requires the fully automaticmeasurement of those motions to link local processes to cellular functions. This includes the design and the implementation of image processing pipelines able to deal with diverse motion types, and 3D visualization tools adapted to the human visual system.Results: Here, we describe a new method for 3D motion estimation that addresses the aforementioned issues. We integrate 3D matching and variational approach to handle a diverse range of motion without any prior on the shape of moving objects. We compare dierent similarity measures to cope with intensity ambiguities and demonstrate the eectiveness of the Census signature for both stages. Additionally, wepresent two intuitive visualization approaches to adapt complex 3D measures into an interpretable 2D view, and a novel way to assess the quality of flow estimates in absence of ground truth

A low-cost hierarchical nanostructured beta-titanium alloy with high strength

Lightweighting of automobiles by use of novel low-cost, high strength-to-weight ratio structural materials can reduce the consumption of fossil fuels and in turn CO(2) emission. Working towards this goal we achieved high strength in a low cost β-titanium alloy, Ti–1Al–8V–5Fe (Ti185), by hierarchical nanostructure consisting of homogenous distribution of micron-scale and nanoscale α-phase precipitates within the β-phase matrix. The sequence of phase transformation leading to this hierarchical nanostructure is explored using electron microscopy and atom probe tomography. Our results suggest that the high number density of nanoscale α-phase precipitates in the β-phase matrix is due to ω assisted nucleation of α resulting in high tensile strength, greater than any current commercial titanium alloy. Thus hierarchical nanostructured Ti185 serves as an excellent candidate for replacing costlier titanium alloys and other structural alloys for cost-effective lightweighting applications

A sparse-to-dense method for 3D optical flow estimation in 3D light microscopy image sequences

International audienceWe present a two-stage 3D optical flow estimation method for light microscopy image volumes. The method takes a pair of light microscopy image volumes as input, segments the 2D slices of the source volume in superpixels and sparsely estimates the 3D displacement vectors in the volume pair. A weighted interpolation is then introduced to get a dense 3D flow field. Edges and motion boundaries are considered during the interpolation. Our experimental results show good gain in execution speed, and accuracy evaluated in computer generated 3D data. Promising results on real 3D image sequences are reported

A 3D+t Laplace operator for temporal mesh sequences

International audienceThe Laplace operator plays a fundamental role in geometry processing. Several discrete versions have been proposed for 3D meshes and point clouds, among others. We define here a discrete Laplace operator for temporally coherent mesh sequences, which allows to process mesh animations in a simple yet efficient way. This operator is a discretization of the Laplace-Beltrami operator using Discrete Exterior Calculus on CW complexes embedded in a four-dimensional space. A parameter is introduced to tune the influence of the motion with respect to the geometry. This enables straightforward generalization of existing Laplacian static mesh processing works to mesh sequences. An application to spacetime editing is provided as example

Survey and evaluation of hypertension machine learning research

Background: Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results: The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions: Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption

Estimation de mouvement 3D et évaluation dans des séquences volumiques de microscopie de fluorescence

The thesis work deals with the computation and the assessment of 3D motion fields in 3D fluorescence microscopy image sequences. We have investigated 3D matching and variational methods for 3D flow field estimation between two consecutive volumes. For matching, we have developed two original 3D extensions of PatchMatch both involving the discrete Census similarity measure: a super-pixel based method that proceeds slice by slice, and a coarse-to-fine method directly applied to the volumes. We have also designed a protrusion segmentation method on the cell surface along with a matching stage relying on a triangular mesh-based representation. Regarding the dense estimation of 3D flow fields, we have adopted a variational approach, while exploiting the continuous Census signature of voxels in the data term. We have tested three regularization terms: L2, L1, and TV-based regularization. We have also combined the 3D PatchMatch method with the variational method to be able to handle simultaneously large and small motion magnitude. For visual assessment, we have proposed three different color-coded visualization techniques of 3D flow fields. They offer 2D summaries of the 3D flow field, respectively, slice-by-slice, with tri-planar projections, and after maximum intensity point projection. In addition, we have defined a new quantitative error measure for assessing the accuracy of the estimated flow field when no ground truth is available. It involves the angular difference between the local principal orientation of the original source volume and the corresponding one in the volume backward-warped with the 3D computed flow field. We have tested our methods on real microscopy image sequences containing MV3 melanoma cells in collagen environment. When comparing with the state-of-the-art method of Amat et al., and our 3D extension of the classical Horn-and-Schunck method, we found our proposed methods to be the best performing ones.Ce travail de thèse porte sur l’estimation et l'évaluation de champs de vitesse 3D dans des séquences d'images 3D de microscopie à fluorescence. Nous nous sommes intéressés aux méthodes de mise en correspondance et aux méthodes variationnelles pour l'estimation du mouvement entre volumes 3D de la séquence. Pour l'appariement, nous avons développé deux extensions 3D originales de PatchMatch, les deux incorporant une mesure de similarité exploitant la signature de Census discrète : une méthode exploitant les super-pixels et qui procède couche par couche dans le volume, et une méthode multi-résolution s’appliquant directement aux volumes. Nous avons par ailleurs conçu une méthode de segmentation des protubérances sur la surface de la cellule et une étape d'appariement des éléments segmentés reposant sur une représentation en mailles triangulaires. En ce qui concerne l'estimation dense des flots 3D, nous avons élaboré plusieurs méthodes variationnelles. Si toutes exploitent la signature Census continue des voxels dans le terme d’attache aux données, elles se distinguent par la nature du terme de régularisation : L2, L1 ou TV. Nous avons également combiné la méthode 3D PatchMatch avec la méthode variationnelle pour appréhender à la fois des mouvements de grande et de petite amplitude. Pour l'évaluation visuelle, nous avons proposé trois techniques différentes de visualisation des flots 3D par code couleur. Elles offrent des vues synthétiques 2D pertinentes du flot 3D calculé, respectivement couche par couche dans le volume, selon des projections tri-planaires, ou par affichage sur l’image obtenue par projection d'intensité maximale. De plus, nous avons défini une nouvelle mesure d’erreur quantitative pour évaluer la précision du flot 3D estimé, lorsqu'aucune vérité-terrain n’est disponible. Elle s'exprime comme la différence angulaire entre l'orientation principale locale dans le volume source et celle correspondante dans le volume rétro-reconstruit à partir du flot 3D calculé. Nous avons testé nos méthodes sur des séquences d'images microscopiques réelles contenant des cellules de mélanome MV3 dans un environnement de collagène. En comparant avec la méthode d'Amat et al. et notre extension 3D de la méthode classique de Horn-et-Schunck, nous avons pu en déduire que nos méthodes sont les plus performantes

Bioresorbable Polymer Blend Scaffold for Tissue Engineering

Tissue engineering merges the disciplines of study like cell biology, materials science, engineering and surgery to enable growth of new living tissues on scaffolding constructed from implanted polymeric materials. One of the most important aspects of tissue engineering related to material science is design of the polymer scaffolds. The polymer scaffolds needs to have some specific mechanical strength over certain period of time. In this work bioresorbable aliphatic polymers (PCL and PLLA) were blended using extrusion and solution methods. These blends were then extruded and electrospun into fibers. The fibers were then subjected to FDA standard in vitro immersion degradation tests where its mechanical strength, water absorption, weight loss were observed during the eight weeks. The results indicate that the mechanical strength and rate of degradation can be tailored by changing the ratio of PCL and PLLA in the blend. Processing influences these parameters, with the loss of mechanical strength and rate of degradation being higher in electrospun fibers compared to those extruded. A second effort in this thesis addressed the potential separation of the scaffold from the tissue (loss of apposition) due to the differences in their low strain responses. This hypothesis that using knit with low tension will have better compliance was tested and confirmed

Titanium Incorporated Gallium Oxide (Ga-Ti-O): Structure Property Relationship and Performance Evaluation for Extreme Environment Applications

The existing power generation systems, which utilize fossil fuels, are in dire need of efficient, reliable chemical sensors that can operate safely at higher temperatures. These sensors control the combustion environment and the emissions during combustion. Several sensing materials such as SnO 2, ZnO, TiO2, WO3, and Ga2O3 exhibit high sensitivity to certain type of chemical molecules and in a certain range of temperatures. Among these candidate materials, β-Ga2O 3 is stable at very high temperatures and has shown functionality for oxygen sensing at higher temperatures (\u3e 700 °C). However, the response time and sensitivity must be significantly improved in order to derive their full potential and utilize them in practical applications. In this work, we focus on the fabrication, characterization and performance evaluation of titanium (Ti) doped gallium oxide (Ga2O3) thin films (referred to GTO) for application in oxygen sensors. An in-depth study was performed on GTO sensors to improve response time and sensitivity of β-Ga 2O3. The real environment condition for sensor (\u3e 700 °C) application were simulated to understand the effect of temperature on the crystal structure, mechanical properties, electronic properties and oxidation states of Ti doped β-Ga2O3. Additionally, for utilizing Ti doped β-Ga2O3 films in practical oxygen sensor applications, attempts were also made to predict the thermodynamic stability and performance of a model, doped Ga2O3 system under extreme environments. A detailed thermal study to understand the effect of extreme environment on titanium (Ti) doped β-Ga2O3 is performed. The real environment condition for sensor (\u3e 700 °C) application was simulated to understand the effect of temperature on the crystal structure, mechanical properties, electronic properties and oxidation states of Ti doped β-Ga2O3. In the entire work, a wide variety of analytical techniques were employed to derive conclusions on the structure, morphology, chemical states, optical properties, thermo-chemical and thermo-mechanical stability of nanostructured Ti-doped Ga2O 3. The results are presented and discussed in this thesis along with structure-property relationships and implications for sensor technology