Predictive World Models from Real-World Partial Observations

Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62% for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://github.com/robin-karlsson0/predictive-world-models.Comment: Accepted for IEEE MOST 202

ViCE: Visual Concept Embedding Discovery and Superpixelization

Recent self-supervised computer vision methods have demonstrated equal or better performance to supervised methods, opening for AI systems to learn visual representations from practically unlimited data. However, these methods are classification-based and thus ineffective for learning dense feature maps required for unsupervised semantic segmentation. This work presents a method to effectively learn dense semantically rich visual concept embeddings applicable to high-resolution images. We introduce superpixelization as a means to decompose images into a small set of visually coherent regions, allowing efficient learning of dense semantics by swapped prediction. The expressiveness of our dense embeddings is demonstrated by significantly improving the SOTA representation quality benchmarks on COCO (+16.27 mIoU) and Cityscapes (+19.24 mIoU) for both low- and high-resolution images

Learning to Predict Navigational Patterns from Partial Observations

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enables our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception. Code released upon publication.Comment: Under revie

Long-term activation of anti-tumor immunity in pancreatic cancer by a p53-expressing telomerase-specific oncolytic adenovirus

Background: Pancreatic cancer is an aggressive, immunologically “cold” tumor. Oncolytic virotherapy is a promising treatment to overcome this problem. We developed a telomerase-specific oncolytic adenovirus armed with p53 gene (OBP-702). Methods: We investigated the efficacy of OBP-702 for pancreatic cancer, focusing on its long-term effects via long-lived memory CD8 + T cells including tissue-resident memory T cells (TRMs) and effector memory T cells (TEMs) differentiated from effector memory precursor cells (TEMps). Results: First, in vitro, OBP-702 significantly induced adenosine triphosphate (ATP), which is important for memory T cell establishment. Next, in vivo, OBP-702 local treatment to murine pancreatic PAN02 tumors increased TEMps via ATP induction from tumors and IL-15Rα induction from macrophages, leading to TRM and TEM induction. Activation of these memory T cells by OBP-702 was also maintained in combination with gemcitabine+nab-paclitaxel (GN) in a PAN02 bilateral tumor model, and GN + OBP-702 showed significant anti-tumor effects and increased TRMs in OBP-702-uninjected tumors. Finally, in a neoadjuvant model, in which PAN02 cells were re-inoculated after resection of treated-PAN02 tumors, GN + OBP-702 provided long-term anti-tumor effects even after tumor resection. Conclusion: OBP-702 can be a long-term immunostimulant with sustained anti-tumor effects on immunologically cold pancreatic cancer

Machine learning algorithm‐based estimation model for the severity of depression assessed using Montgomery‐Asberg depression rating scale

Abstract Aim Depressive disorder is often evaluated using established rating scales. However, consistent data collection with these scales requires trained professionals. In the present study, the “rater & estimation‐system” reliability was assessed between consensus evaluation by trained psychiatrists and the estimation by 2 models of the AI‐MADRS (Montgomery‐Asberg Depression Rating Scale) estimation system, a machine learning algorithm‐based model developed to assess the severity of depression. Methods During interviews with trained psychiatrists and the AI‐MADRS estimation system, patients responded orally to machine‐generated voice prompts from the AI‐MADRS structured interview questions. The severity scores estimated from two models of the AI‐MADRS estimation system, the max estimation model and the average estimation model, were compared with those by trained psychiatrists. Results A total of 51 evaluation interviews conducted on 30 patients were analyzed. Pearson's correlation coefficient with the scores evaluated by trained psychiatrists was 0.76 (95% confidence interval 0.62–0.86) for the max estimation model, and 0.86 (0.76–0.92) for the average estimation model. The ANOVA ICC rater & estimation‐system reliability with the evaluation scores by trained psychiatrists was 0.51 (−0.09 to 0.79) for the max estimation model, and 0.75 (0.55–0.86) for the average estimation model. Conclusion The average estimation model of AI‐MADRS demonstrated substantially acceptable rater & estimation‐system reliability with trained psychiatrists. Accumulating a broader training dataset and the refinement of AI‐MADRS interviews are expected to improve the performance of AI‐MADRS. Our findings suggest that AI technologies can significantly modernize and potentially revolutionize the realm of depression assessments

Chemical array system, a platform to identify novel hepatitis B virus entry inhibitors targeting sodium taurocholate cotransporting polypeptide

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