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The optimization of the latents and parameters of diffusion models with respect to some differentiable metric defined on the output of the model is a challenging and complex problem. The sampling for diffusion models is done by solving either the probability flow ODE or diffusion SDE wherein a neural network approximates the score function allowing a numerical ODE/SDE solver to be used. However, naïve backpropagation techniques are memory intensive, requiring the storage of all intermediate states, and face additional complexity in handling the injected noise from the diffusion term of the diffusion SDE. We propose a novel family of bespoke ODE solvers to the continuous adjoint equations for diffusion models, which we call AdjointDEIS. We exploit the unique construction of diffusion SDEs to further simplify the formulation of the continuous adjoint equations using exponential integrators. Moreover, we provide convergence order guarantees for our bespoke solvers. Significantly, we show that the continuous adjoint equations for diffusion SDEs actually simplify to a simple ODE. Lastly, we demonstrate the effectiveness of AdjointDEIS for guided generation with an adversarial attack in the form of the face morphing problem. Our code will be released at https: //github.com/zblasingame/AdjointDEIS. 

In recent years, ransomware attacks have grown dramatically. New variants continually emerging make tracking and mitigating these threats increasingly difficult using traditional detection methods. As the landscape of ransomware evolves, there is a growing need for more advanced detection techniques. Neural networks have gained popularity as a method to enhance detection accuracy, by leveraging low-level hardware information such as hardware events as features for identifying ransomware attacks. In this paper, we investigated several state-of-the-art supervised learning models, including XGBoost, LightGBM, MLP, and CNN, which are specifically designed to handle time series data or image-based data for ransomware detection. We compared their detection accuracy, computational efficiency, and resource requirements for classification. Our findings indicate that particularly LightGBM, offer a strong balance of high detection accuracy, fast processing speed, and low memory usage, making them highly effective for ransomware detection tasks.