The analysis of data from multiple experiments, such as observations of several individuals, is commonly approached using mixed-effects models, which account for variation between individuals through hierarchical representations. This makes mixed-effects models widely applied in fields such as biology, pharmacokinetics, and sociology. In this work, we propose a novel methodology for scalable Bayesian inference in hierarchical mixed-effects models. Our framework first constructs amortized approximations of the likelihood and the posterior distribution, which are then rapidly refined for each individual dataset, to ultimately approximate the parameters posterior across many individuals. The framework is easily trainable, as it uses mixtures of experts but without neural networks, leading to parsimonious yet expressive surrogate models of the likelihood and the posterior. We demonstrate the effectiveness of our methodology using challenging stochastic models, such as mixed-effects stochastic differential equations emerging in systems biology-driven problems. However, the approach is broadly applicable and can accommodate both stochastic and deterministic models. We show that our approach can seamlessly handle inference for many parameters. Additionally, we applied our method to a real-data case study of mRNA transfection. When compared to exact pseudomarginal Bayesian inference, our approach proved to be both fast and competitive in terms of statistical accuracy.

Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators. These approaches are collectively known as "simulation-based inference" (SBI). Recent SBI methods have made use of neural networks (NN) to provide approximate, yet expressive constructs for the unavailable likelihood function and the posterior distribution. However, they do not generally achieve an optimal trade-off between accuracy and computational demand. In this work, we propose an alternative that provides both approximations to the likelihood and the posterior distribution, using structured mixtures of probability distributions. Our approach produces accurate posterior inference when compared to state-of-the-art NN-based SBI methods, while exhibiting a much smaller computational footprint. We illustrate our results on several benchmark models from the SBI literature.

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models using training image data with only image-level supervision. Since precise pixel-level annotations are not accessible, existing methods typically focus on producing pseudo masks for training segmentation models by refining CAM-like heatmaps. However, the produced heatmaps may only capture discriminative image regions of target object categories or the associated co-occurring backgrounds. To address the issues, we propose a Semantic Prompt Learning for WSSS (SemPLeS) framework, which learns to effectively prompt the CLIP space to enhance the semantic alignment between the segmented regions and the target object categories. More specifically, we propose Contrastive Prompt Learning and Class-associated Semantic Refinement to learn the prompts that adequately describe and suppress the image backgrounds associated with each target object category. In this way, our proposed framework is able to perform better semantic matching between object regions and the associated text labels, resulting in desired pseudo masks for training the segmentation model. The proposed SemPLeS framework achieves SOTA performance on the standard WSSS benchmarks, PASCAL VOC and MS COCO, and demonstrated interpretability with the semantic visualization of our learned prompts. The codes will be released.

Speaking to BlueNotes on video, author and speaker Semple said as technology eats away at procedural workplace roles, workers can fight back by thinking better and sharing faster than machines. " Honesty is going to matter more and more because, let's face it, artificial intelligence and automation is increasingly effective." Semple said in large organisations, the higher people climb the greater the pressure on them to conform: "I'm always amazed at how difficult it is for senior people to say what they think and stick it out there." "Honesty is going to matter more and more because, let's face it, artificial intelligence and automation is increasingly effective," he said. "Your average white-collar knowledge worker is going to have to find other ways to add value over the next five to 10 years."

How much more black could Vantablack be? This stuff is the blackest black. It is so black that it makes reality look Photoshopped. Perception of depth and dimensionality disappears into a scotoma of darkness. You look at Vantablack, but nothing looks back at you. That's not why Vantablack caused an uproar last year. It was supposed to be a specialty product for aerospace and optics.

Want to beat the robots? Increasingly, open workplace communication will be a vital tool for knowledge workers hoping to fight off the rise of automation, communications expert Euan Semple says. Speaking to BlueNotes on video, author and speaker Semple said as technology eats away at procedural workplace roles, workers can fight back by thinking better and sharing faster than machines. Semple said in large organisations, the higher people climb the greater the pressure on them to conform: "I'm always amazed at how difficult it is for senior people to say what they think and stick it out there." "Honesty is going to matter more and more because, let's face it, artificial intelligence and automation is increasingly effective," he said. "Your average white-collar knowledge worker is going to have to find other ways to add value over the next five to 10 years."