Novel Molecular Simulation Approach to Liquid-Liquid Equilibria and Application to the Design of Desalination Solvents

Conventionally, seawater desalination involves energy-intensive distillation or pressure-driven membrane processes. Recent studies show that directional solvent extraction (DSE) could serve as a viable low-temperature and low-energy alternative. This method utilizes a solvent capable of selectively dissolving water by rejecting the salt ions and at the same time having negligible solubility in water phase followed by the extraction of the dissolved water by heating or cooling the solvent phase. Octanoicacid and decanoic acid have been discovered to serve as a viable candidate for DSE solvent with all the desirable properties. Their studies have shown that these solvent shave very low effectiveness of extracting water and thus need better solvent with good water extracting capacity for making the process more efficient. Such a solvent can be identified by screening molecules capable of extracting higher amount of water than octanoic acid and decanoic acid using a computational approach. Success of such a screening process critically depends on the ability to calculate the solubility of water in solvents and requires a priori prediction of liquid-liquid equilibria of the solvent and water. These calculations remain challenging from molecular simulations due to the difficulties associated with the transfer of molecules between two dense liquid phases. In order to overcome these limitations, a novel molecular simulation methodology is developed based on computing the fugacity of water as a function of concentration to calculate the water dissolution in a range of solvents assuming that the solubility of the solvent is negligible in water. The calculated solubilities are shown to yield good agreement with experimental data for long chain carboxylic acids over a range of temperatures. After developing the method, different solvents were studied which includes branched and fluorinated structures of octanoic acid. Also, the effect of adding alcohol functional group to the molecule at different positions was also studied. Finally, the developed method was also extended to study systems showing mutual solubility in both the directions and was demonstrated using butanol-water system.Chemical Engineerin

A Perceptual Shape Loss for Monocular 3D Face Reconstruction

Monocular 3D face reconstruction is a wide-spread topic, and existing approaches tackle the problem either through fast neural network inference or offline iterative reconstruction of face geometry. In either case carefully-designed energy functions are minimized, commonly including loss terms like a photometric loss, a landmark reprojection loss, and others. In this work we propose a new loss function for monocular face capture, inspired by how humans would perceive the quality of a 3D face reconstruction given a particular image. It is widely known that shading provides a strong indicator for 3D shape in the human visual system. As such, our new 'perceptual' shape loss aims to judge the quality of a 3D face estimate using only shading cues. Our loss is implemented as a discriminator-style neural network that takes an input face image and a shaded render of the geometry estimate, and then predicts a score that perceptually evaluates how well the shaded render matches the given image. This 'critic' network operates on the RGB image and geometry render alone, without requiring an estimate of the albedo or illumination in the scene. Furthermore, our loss operates entirely in image space and is thus agnostic to mesh topology. We show how our new perceptual shape loss can be combined with traditional energy terms for monocular 3D face optimization and deep neural network regression, improving upon current state-of-the-art results.Comment: Accepted to PG 2023. Project page: https://studios.disneyresearch.com/2023/10/09/a-perceptual-shape-loss-for-monocular-3d-face-reconstruction/ Video: https://www.youtube.com/watch?v=RYdyoIZEuU

Prospective nutritional, therapeutic, and dietary benefits of camel milk making it a viable option for human consumption: Current state of scientific knowledge

For over five thousand years, people in Asia and Africa have known about the health benefits of camel milk. Thus, it is used not only as a food source but also as a medicine. The similarities between camel milk and human milk have been scientifically proven. Camel milk is unique among ruminant milk because it is high in vitamins C and E and low in sugar and cholesterol. Still, it contains a wide variety of beneficial minerals (including sodium, potassium, iron, copper, zinc, and magnesium), besides being rich in several nutrients, including monounsaturated and polyunsaturated fatty acids, serum albumin, lactoferrin, immunoglobulins, lysozyme and the hormone insulin. Because of these components, many medical professionals now recommend camel milk as a treatment for various human ailments. It has been demonstrated to be effective in treating gastrointestinal issues, Type 1 diabetes, and food allergies. As a bonus, camel milk has been utilized to cure autism, lower cholesterol, prevent psoriasis, heal inflammation, aid tuberculosis patients, boost the body's natural defences, and impede the spread of cancer cells. Those who have problems digesting lactose may still be able to tolerate it. Conversely, camel milk can also help reduce an excessively high bilirubin, globulin, and granulocyte count. Drinking camel milk does not affect the erythrocyte sedimentation rate, hemoglobin concentration, and leukocyte count. The proteins in camel milk have an adequate ratio of critical amino acids. Immunoglobulins, which fight disease, are contained inside, and their small size allows antigens to penetrate and boosts the immune system's efficacy. This article highlights the health benefits and medicinal uses of camel milk

Fully Controllable Data Generation for Realtime Face Capture

Data driven realtime face capture has gained considerable momentum in the last few years thanks to deep neural networks that leverage specialized datasets to speedup the acquisition of face geometry and appearance. However generaliz- ing such neural solutions to generic in-the-wild face capture continues to remain a challenge due to the lack of, or a means to generate a high quality in-the-wild face database with all forms of groundtruth (geometry, appearance, environment maps, etc.). In this thesis we recognize this data bottleneck and propose a com- prehensive framework for controllable, high quality, in-the-wild data generation that can support present and future applications in face capture. We approach this problem in four stages starting with the building of a high quality 3D face database consisting of a few hundred subjects in a studio setting. This database will serve as a strong prior for 3D face geometry and appearance for several meth- ods discussed in this thesis. To build this 3D database and to automate the regis- tration of scans to a template mesh, we propose the first deep facial landmark de- tector capable of operating on 4K resolution imagery while also achieving state-of- the-art performance on several in-the-wild benchmarks. Our second stage lever- ages the proposed 3D face database to build powerful nonlinear 3D morphable models for static geometry modelling and synthesis. We propose the first seman- tic deep face model that combines the semantic interpretability of traditional 3D morphable models with the nonlinear expressivity of neural networks. We later extend this semantic deep face model with a novel transformer based architec- ture and propose the Shape Transformer, for representing and manipulating face shapes irrespective of their mesh connectivity. The third stage of our data gen- eration pipeline involves extending the approaches for static geometry synthesis to support facial deformations across time so as to synthesize dynamic perfor- mances. To synthesize facial performances we propose two parallel approaches, one involving performance retargeting and another based on a data driven 4D (3D + time) morphable model. We propose a local anatomically constrained fa- cial performance retargeting technique that uses only a handful of blendshapes ( 20 shapes) to achieve production quality results. This retargeting technique can readily be used to create novel animations for any given actor via animation transfer. Our second contribution for generating facial performances is through a transformer based 4D autoencoder that encodes a sequence of expression blend weights into a learned performance latent space. Novel performances can then be generated at inference time by sampling this learned latent space. The fourth and final stage of our data generation pipeline involves the creation of photorealistic imagery that can go along with the facial geometry and animations synthesized thus far. We propose a hybrid rendering approach that leverages state-of-the-art techniques for ray traced skin rendering and a pretrained 2D generative model for photorealistic and consistent inpainting of the skin renders. Our hybrid ren- dering technique allows for the creation of an infinite number of training samples where the user has full control over the facial geometry, appearance, lighting and viewpoint. The techniques presented in this thesis will serve as the foundation for creating large scale photorealistic in-the-wild face datasets to support the next generation of realtime face capture

Symbol Timing Recovery . . .

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, NevadaShaped offset quadrature phase shift keying (SOQPSK) is a highly bandwidth efficient modulation technique used widely in military and aeronautical telemetry standards. It can be classified as a form of continuous phase modulation (CPM), but its major distinction from other CPM schemes is that it has a constrained (correlated) ternary data alphabet. CPM-based detection models for SOQPSK have been developed only recently. One roadblock standing in the way of these detectors being adopted is that existing symbol timing recovery techniques for CPM are not always applicable since the data symbols are correlated. We investigate the performance of one CPM-based timing error detector (TED) that can be used with SOQPSK, and apply it to the versions of SOQPSK used in military (MIL-STD SOQPSK) and telemetry group (SOQPSK-TG) standards. We derive the theoretical performance limits on the accuracy of timing recovery for SOQPSK, as given by the modified Cramer-Rao bound (MCRB), and show that the proposed TED performs close to these bounds in computer simulations and is free of false-lock points. We also show that the proposed scheme outperforms a non-data aided TED that was recently developed for SOQPSK. These results show that the proposed scheme has great promise in a wide range of applications due to its low complexity, strong performance, and lack of false-lock points.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Attention-Driven Cropping for Very High Resolution Facial Landmark Detection

Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today. Existing datasets used to train such algorithms are primarily made up of only low resolution images, and current algorithms are limited to inputs of comparable quality and resolution as the training dataset. On the other hand, high resolution imagery is becoming increasingly more common as consumer cameras improve in quality every year. Therefore, there is need for algorithms that can leverage the rich information available in high resolution imagery. Naively attempting to reuse existing network architectures on high resolution imagery is prohibitive due to memory bottlenecks on GPUs. The only current solution is to downsample the images, sacrificing resolution and quality. Building on top of recent progress in attention-based networks, we present a novel, fully convolutional regional architecture that is specially designed for predicting landmarks on very high resolution facial images without downsampling. We demonstrate the flexibility of our architecture by training the proposed model with images of resolutions ranging from 256 x 256 to 4K. In addition to being the first method for facial landmark detection on high resolution images, our approach achieves superior performance over traditional (holistic) state-of-the-art architectures across ALL resolutions, leading to a general-purpose, extremely flexible, high quality landmark detector

Semantic Deep Face Models

Face models built from 3D face databases are often used in computer vision and graphics tasks such as face reconstruction, replacement, tracking and manipulation. For such tasks, commonly used multi-linear morphable models, which provide semantic control over facial identity and expression, often lack quality and expressivity due to their linear nature. Deep neural networks offer the possibility of non-linear face modeling, where so far most research has focused on generating realistic facial images with less focus on 3D geometry, and methods that do produce geometry have little or no notion of semantic control, thereby limiting their artistic applicability. We present a method for nonlinear 3D face modeling using neural architectures that provides intuitive semantic control over both identity and expression by disentangling these dimensions from each other, essentially combining the benefits of both multi-linear face models and nonlinear deep face networks. The result is a powerful, semantically controllable, nonlinear, parametric face model. We demonstrate the value of our semantic deep face model with applications of 3D face synthesis, facial performance transfer, performance editing, and 2D landmark-based performance retargeting

Adaptive Convolutions for Structure-Aware Style Transfer

Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properties of style features to a content image, and can transfer a large number of styles in real time. However, AdaIN is a global operation; thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive Convolutions (AdaConv), a generic extension of AdaIN, to allow for the simultaneous transfer of both statistical and structural styles in real time. Apart from style transfer, our method can also be readily extended to style-based image generation, and other tasks where AdaIN has already been adopted

Improved Lighting Models for Facial Appearance Capture

Facial appearance capture techniques estimate geometry and reflectance properties of facial skin by performing a computationally intensive inverse rendering optimization in which one or more images are re-rendered a large number of times and compared to real images coming from multiple cameras. Due to the high computational burden, these techniques often make several simplifying assumptions to tame complexity and make the problem more tractable. For example, it is common to assume that the scene consists of only distant light sources, and ignore indirect bounces of light (on the surface and within the surface). Also, methods based on polarized lighting often simplify the light interaction with the surface and assume perfect separation of diffuse and specular reflectance. In this paper, we move in the opposite direction and demonstrate the impact on facial appearance capture quality when departing from these idealized conditions towards models that seek to more accurately represent the lighting, while at the same time minimally increasing computational burden. We compare the results obtained with a state-of-the-art appearance capture method [RGB*20], with and without our proposed improvements to the lighting model