Generative modeling aims to transform random noise into structured outputs. In this work, we enhance video diffusion models by allowing motion control via structured latent noise sampling. This is achieved by just a change in data: we pre-process training videos to yield structured noise. Consequently, our method is agnostic to diffusion model design, requiring no changes to model architectures or training pipelines. Specifically, we propose a novel noise warping algorithm, fast enough to run in real time, that replaces random temporal Gaussianity with correlated warped noise derived from optical flow fields, while preserving the spatial Gaussianity. The efficiency of our algorithm enables us to fine-tune modern video diffusion base models using warped noise with minimal overhead, and provide a one-stop solution for a wide range of userfriendly motion control: local object motion control, global camera movement control, and motion transfer. The harmonization between temporal coherence and spatial Gaussianity in our warped noise leads to effective motion control while maintaining per-frame pixel quality. Extensive experiments and user studies demonstrate the advantages of our method, making it a robust and scalable approach for controlling motion in video diffusion models. Please see our project webpage; source code and checkpoints are available on GitHub.

Practitioners use various feature importance metrics to rank features based on their importance to discard weak predictors, simplifying predictive models and improving generalizability. Model-dependent feature ranking methods often rely on training a machine learning model. However, a common issue is the confusion between feature importance and feature impact on the trained model, leading to potential misinterpretations in fields such as medicine and business. This problem primarily stems from the widespread use of model-based methods that leads to misinterpreting feature importance with its impact on the trained model. To address this, we introduce Vendi Score Importance (VSI), an interpretable model-independent feature ranking metric. VSI quantifies each feature’s impact on the overall Vendi score instead of a machine learning model, beginning with only the target/label and sequentially adding features. As a model-independent metric, VSI relies solely on feature and target values, making it applicable to both classification and regression tasks. We demonstrate VSI’s effectiveness through applications to four benchmark problems (two classification and two regression), comparing it to LIME, Permutation, and SHAP (model-agnostic methods), as well as Mutual Information Regression (MIR), a univariate, model-independent approach. Results show that VSI not only provides interpretable importance scores for each feature but also outperforms others in computational efficiency. To confirm VSI’s robustness against random noise, we repeated the feature ranking procedure 100 times, introducing different random features each time. The results highlight VSI’s superior performance, particularly in maintaining agnosticism to random noise.

This paper examines and optimizes parameters that affect the air cooling of a Lithium-Ion (Li-Ion) battery, used in Electric Vehicles (EVs). A battery pack containing 150 cylindrical type Li-Ion battery cells in a PVC casing is investigated. An equal number of tubes are used in the pack as a medium to cool the battery by using a fan when the vehicle is stationary or with ambient air when in motion. The parameters affecting the air cooling of battery are studied and optimized by considering their practical constraints. The objective function and Number of Transfer Unit (NTU) are developed Finally, a genetic algorithm method is employed to optimize the decision variables. Analysing the results shows that NTU can be maximized by increasing the diameter of tubes on the battery and keeping the air velocity in a certain range.