GenVideo: One-shot Target-image and Shape Aware Video Editing using T2I Diffusion Models

Video editing methods based on diffusion models that rely solely on a text prompt for the edit are hindered by the limited expressive power of text prompts. Thus, incorporating a reference target image as a visual guide becomes desirable for precise control over edit. Also, most existing methods struggle to accurately edit a video when the shape and size of the object in the target image differ from the source object. To address these challenges, we propose "GenVideo" for editing videos leveraging target-image aware T2I models. Our approach handles edits with target objects of varying shapes and sizes while maintaining the temporal consistency of the edit using our novel target and shape aware InvEdit masks. Further, we propose a novel target-image aware latent noise correction strategy during inference to improve the temporal consistency of the edits. Experimental analyses indicate that GenVideo can effectively handle edits with objects of varying shapes, where existing approaches fail.Comment: CVPRw 202

Process of forming crosslinked copolymer film, crosslinked copolymer film formed thereby, and water purification membrane

Azidoaryl-substituted cyclooctene monomers and synthesized and used in the preparation of various copolymers. Among these copolymers are those prepared from ring-opening metathesis polymerization of cyclooctene, polyethylene glycol-substituted cyclooctene, and azidoaryl-substituted cyclooctene. These copolymers are useful in the formation of crosslinked films that reduce fouling of water purification membranes.Board of Regents, University of Texas Syste

Design and development of electronic jacquard for Korai mat weaving loom

A low-cost computerised mat weaving handloom has beendeveloped. After successful field trials at the client locations,300% increase in productivity is observed. Now any weaver canweave mat of any design within 2 days instead of 6-8 days,increasing their earning per mat. Easy to use pedallingmechanism, electronic jacquard and the software tool aredeveloped. This innovation facilitates electronic design storage,eliminates recurring cost on punched cards and the weaverdependence on designers while weaving marriage mats. The welldesigned pedalling and jacquard lifting mechanism results inbetter ergonomics. The system is designed to operate with powerof just 75 watt, so that it can be driven by solar power

Pre-transplant weight but not weight gain is associated with new-onset diabetes after transplantation: a multi-centre cohort Spanish study

Background. New-onset diabetes after transplantation (NODAT) is associated with poorer outcomes in kidney transplantation (KT). Thus, identification of modifiable risk factors may be crucial for ameliorating the impact of this entity on transplant outcomes. We assessed the relationships between the weight, body mass index (BMI) and weight gain with NODAT

Forward Osmosis Desalination Using Thermoresponsive Ionic Liquids: Bench-Scale Demonstration and Cost Analysis

Forward osmosis (FO) desalination using thermoresponsive ionic liquid (IL)-water mixtures is a promising technology for treating nontraditional water sources. However, its demonstration has primarily been at the lab-scale, with water flux and recovery values that are not representative of realistic applications. In this work, the performance of tetrabutyl-phosphonium trifluoroacetate (P4444TFA), as well as a new dual draw of P4444TFA with tetrabutyl-ammonium trifluoroacetate (N4444TFA) is characterized. The dual draw combines the higher osmolality of one IL with the lower critical solution temperature (LCST) of the second IL to outperform its constituents at the same total concentration of IL in water (70 wt %). Experiments were first performed in a lab-scale coupon tester to understand the effects of draw osmotic pressure and viscosity on water flux through the membrane. Bench-scale experiments were then performed in an element tester with a 1 m2 membrane area to evaluate the performance of IL-based FO for the desalination of produced water feed from oil and gas. Specifically, 10 kg of IL-water draw solution was used with 3 kg of real produced water feed, resulting in water recoveries of 60% with initial and final water fluxes of 14 LMH and 3 LMH, respectively. The bench-scale experimental results were used as inputs for a cost analysis, yielding a levelized cost of water (LCOW) of $1.18 per m3. This reveals the potential of IL-based draw solutions for cost-effective desalination of challenging feedwaters using FO

Novel Scan Strategies for Selective Laser Melting

This work investigates novel scan strategies for Selective Laser Melting with the powder bed fusion process. The capabilities and limitations of the additive manufacturing machine play a vital role in the design of the scan strategies. A scan strategy is highly dependent on the capability/execution of the scan hardware and the ability of the control system to steer the laser beam through the optical drives and deliver focused laser energy at the precise location on the powder bed. This work identifies novel scan strategies that overcome the limitations of current techniques relying on discrete point exposures for consolidating material. The discrete point exposure strategy delivers laser energy at pre-determined locations in a sequence along the scan path to consolidate and bind regions in a layer with layers below the current layer. The scan strategies for discrete point exposures are discussed, and novel strategies are proposed, characterised and evaluated in this research. The available technology (Laser(s), steering optics, mechatronics, firmware, and control architecture) used to build modern additive manufacturing (AM) machines has its limitations. The key considerations in developing scan strategies are the steering mirror dynamics and the challenges in controlling the position of the laser beam on the powder bed. The mirrors steering the laser during the execution of scan strategies are typically subjected to very high acceleration. Discrete scan strategies often start and stop the mirror movement, thus exacerbating the acceleration problems. This research explores the potential limitations of discrete scan strategies used by Laser powder bed fusion (LPBF) and proposes enhancements, such as sky-writing, that overcome some limitations. Sky-writing is studied in detail to improve repeatability and mark consistent exposures. This research also explores the reasons for local energy concentration in the build layers. Multi-layer scan strategies, introduced in this work, consolidate material over many consecutive layers and reduce/eliminate local energy concentration. This work investigates the broad control hardware, including steering and focus optics, software architecture and development methods to extend the understanding of LPBF to produce consistent metal parts with fewer defects. The scan strategies catering to the core and border areas are different as they are typically processed with different laser parameters. Novel techniques, such as blended borders, reduce defects between core and borders. Energy density, rate of energy delivery, gas flow and layer geometry influence the consolidation process and the material properties in the LPBF process. Local energy density varies based on the location of each exposure and the proximity of such exposures with other neighbouring exposures. The scan strategies proposed in this work distribute energy uniformly. Hexagonal discrete point exposure strategies demonstrated and evaluated in this work show higher part densities with a broader process regime. These strategies can be used alongside vector scan strategies. Novel metrology techniques to measure LPBF part distortion and the methods to compensate for such distortion are presented. A hexagonal multi-layer scan strategy using non-overlapping exposures is introduced to minimise residual stresses. Another proposed technique (independent of the scan strategy) relies on inverse compensation of the measured distortion. This work (for the first time) introduces sky-writing for discrete point exposures and combines this with vector scan strategies. Multi-layer hexagonal point exposure strategies are shown to improve part density and have a broader process regime, enabling fasting material parameter development

Automatic Generation of Descriptive Features for Predicting Vehicle Faults

Predictive Maintenance (PM) has been increasingly adopted in the Automotive industry, in the recent decades along with conventional approaches such as the Preventive Maintenance and Diagnostic/Corrective Maintenance, since it provides many advantages to estimate the failure before the actual occurrence proactively, and also being adaptive to the present status of the vehicle, in turn allowing flexible maintenance schedules for efficient repair or replacing of faulty components. PM necessitates the storage and analysis of large amounts of sensor data. This requirement can be a challenge in deploying this method on-board the vehicles due to the limited storage and computational power on the hardware of the vehicle. Hence, this thesis seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. This low dimensional representation will be used for predicting vehicle faults, specifically Turbocharger related failures. Since the Logged Vehicle Data (LVD) was base on all the data utilized in this thesis, it allowed for the evaluation of large populations of trucks without requiring additional measuring devices and facilities. The gradual degradation methodology is considered for describing vehicle condition, which allows for modeling the malfunction/ failure as a continuous process rather than a discrete flip from healthy to an unhealthy state. This approach eliminates the challenge of data imbalance of healthy and unhealthy samples. Two important hypotheses are presented. Firstly, Parallel StackedClassical Autoencoders would produce better representations com-pared to individual Autoencoders. Secondly, employing Learned Em-beddings on Categorical Variables would improve the performance of the Dimensionality reduction. Based on these hypotheses, a model architecture is proposed and is developed on the LVD. The model is shown to achieve good performance, and in close standards to the previous state-of-the-art research. This thesis, finally, illustrates the potential to apply parallel stacked architectures with Learned Embeddings for the Categorical features, and a combination of feature selection and extraction for numerical features, to predict the Remaining Useful Life (RUL) of a vehicle, in the context of the Turbocharger. A performance improvement of 21.68% with respect to the Mean Absolute Error (MAE) loss with an 80.42% reduction in the size of data was observed