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Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse problems, the reverse sampling steps are modified to approximately sample from a measurement-conditioned distribution. However, these modifications may be unsuitable for certain settings (e.g., presence of measurement noise) and non-linear tasks, as they often struggle to correct errors from earlier steps and generally require a large number of optimization and/or sampling steps. To address these challenges, we state three conditions for achieving measurement-consistent diffusion trajectories. Building on these conditions, we propose a new optimization-based sampling method that not only enforces standard data manifold measurement consistency and forward diffusion consistency, as seen in previous studies, but also incorporates our proposed step-wise and network-regularized backward diffusion consistency that maintains a diffusion trajectory by optimizing over the input of the pre-trained model at every sampling step. By enforcing these conditions (implicitly or explicitly), our sampler requires significantly fewer reverse steps. Therefore, we refer to our method as Step-wise Triple- Consistent Sampling (SITCOM). Compared to SOTA baselines, our experiments across several linear and non-linear tasks (with natural and medical images) demonstrate that SITCOM achieves competitive or superior results in terms of standard similarity metrics and run-time. 

There is a need to develop new and sustainable agricultural technologies to help provide global food security, and nanoscale materials show promising results in this area. In this study, mesoporous silica nanoparticles (MSNs) and chitosan-coated mesoporous silica nanoparticles (CTS-MSNs) were synthesized and applied to soybeans (Glycine max) by two different strategies in greenhouse and field studies to study the role of dissolved silicic acid and chitosan in enhancing plant growth and suppressing disease damage caused by Fusarium virguliforme. Plant growth and health were assessed by measuring the soybean biomass and chlorophyll content in both healthy and Fusarium-infected plants at harvest. In the greenhouse study, foliar and seed applications with 250 mg/L nanoparticle treatments were compared. A single seed treatment of MSNs reduced disease severity by 30% and increased chlorophyll content in both healthy and infected plants by 12%. Based on greenhouse results, seed application was used in the follow-up field study and MSNs and CTS-MSNs reduced disease progression by 12 and 15%, respectively. A significant 32% increase was observed for chlorophyll content for plants treated with CTS-MSNs. Perhaps most importantly, nanoscale silica seed treatment significantly increased (23–68%) the micronutrient (Zn, Mn, Mg, K, B) content of soybean pods, suggesting a potential sustainable strategy for nano-enabled biofortification to address nutrition insecurity. Overall, these findings indicate that MSN and CTS-MSN seed treatments in soybeans enable disease suppression and increase plant health as part of a nano-enabled strategy for sustainable agriculture. 

The uncertainty in modeling emotions makes speech emotion recognition (SER) systems less reliable. An intuitive way to increase trust in SER is to reject predictions with low confidence. This approach assumes that an SER system is well calibrated, where highly confident predictions are often right and low confident predictions are often wrong. Hence, it is desirable to calibrate the confidence of SER classifiers. We evaluate the reliability of SER systems by exploring the relationship between confidence and accuracy, using the expected calibration error (ECE) metric. We develop a multi-label variant of the post-hoc temperature scaling (TS) method to calibrate SER systems, while preserving their accuracy. The best method combines an emotion co-occurrence weight penalty function, a class-balanced objective function, and the proposed multi-label TS calibration method. The experiments show the effectiveness of our developed multi-label calibration method in terms of ac- curacy and ECE.