Thermally Treated to Perfection: Enhancing Wood Color and Properties with Surface Thermal Treatment

Darker-colored wood species usually have highervalues, many of which are endangered and underprotection. Chemical stains and finishes might alsoachieve similar color shades, but customers prefernon-chemical alternatives. Thermal treatment (TT)is one of the low-toxicity choices. It could producedarker shades and enhance some materialproperties but requires a large initial investmentand is time-consuming. This study aimed toevaluate a new type of TT: Surface ThermalTreatment (STT). White Ash, Yellow Poplar, and RedOak were selected and treated on a heated pressat varying temperatures and times. Artificial NeuralNetwork (ANN) was employed to model therelationship between temperature, time, and colorchange. Results demonstrated that STT can achieveefficient thermal modification. The combination oftemperature and duration brought differentshades to all 3 species. Application of the ANNmodel can simulate the process results fast with ahigh degree of accuracy (R2 =0.96)

A Context-based Multi-task Hierarchical Inverse Reinforcement Learning Algorithm

Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks, which is essential for general-purpose robots, based on multi-task expert demonstrations. Existing MIL algorithms suffer from low data efficiency and poor performance on complex long-horizontal tasks. We develop Multi-task Hierarchical Adversarial Inverse Reinforcement Learning (MH-AIRL) to learn hierarchically-structured multi-task policies, which is more beneficial for compositional tasks with long horizons and has higher expert data efficiency through identifying and transferring reusable basic skills across tasks. To realize this, MH-AIRL effectively synthesizes context-based multi-task learning, AIRL (an IL approach), and hierarchical policy learning. Further, MH-AIRL can be adopted to demonstrations without the task or skill annotations (i.e., state-action pairs only) which are more accessible in practice. Theoretical justifications are provided for each module of MH-AIRL, and evaluations on challenging multi-task settings demonstrate superior performance and transferability of the multi-task policies learned with MH-AIRL as compared to SOTA MIL baselines

Multi-task Hierarchical Adversarial Inverse Reinforcement Learning

Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots. Existing MIL algorithms suffer from low data efficiency and poor performance on complex long-horizontal tasks. We develop Multi-task Hierarchical Adversarial Inverse Reinforcement Learning (MH-AIRL) to learn hierarchically-structured multi-task policies, which is more beneficial for compositional tasks with long horizons and has higher expert data efficiency through identifying and transferring reusable basic skills across tasks. To realize this, MH-AIRL effectively synthesizes context-based multi-task learning, AIRL (an IL approach), and hierarchical policy learning. Further, MH-AIRL can be adopted to demonstrations without the task or skill annotations (i.e., state-action pairs only) which are more accessible in practice. Theoretical justifications are provided for each module of MH-AIRL, and evaluations on challenging multi-task settings demonstrate superior performance and transferability of the multi-task policies learned with MH-AIRL as compared to SOTA MIL baselines.Comment: This paper is accepted at ICML 2023. arXiv admin note: text overlap with arXiv:2210.0196

Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks

In this paper, we propose a data-free method of extracting Impressions of each class from the classifier's memory. The Deep Learning regime empowers classifiers to extract distinct patterns (or features) of a given class from training data, which is the basis on which they generalize to unseen data. Before deploying these models on critical applications, it is advantageous to visualize the features considered to be essential for classification. Existing visualization methods develop high confidence images consisting of both background and foreground features. This makes it hard to judge what the crucial features of a given class are. In this work, we propose a saliency-driven approach to visualize discriminative features that are considered most important for a given task. Another drawback of existing methods is that confidence of the generated visualizations is increased by creating multiple instances of the given class. We restrict the algorithm to develop a single object per image, which helps further in extracting features of high confidence and also results in better visualizations. We further demonstrate the generation of negative images as naturally fused images of two or more classes.Comment: ICIP 202

Sepsis Treatment: Reinforced Sequential Decision-Making for Saving Lives

Sepsis, a life-threatening condition triggered by the body\u27s exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. Our project introduces the PosNegDM: Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The PosNegDM framework significantly improves patient survival, saving 97.39% of patients and outperforming established machine learning algorithms (Decision Transformer and Behavioral Cloning) with survival rates of 33.4% and 43.5%, respectively. Additionally, ablation studies underscore the critical role of the transformer-based decision maker and the integration of a mortality classifier in enhancing overall survival rates. Our proposed approach presents a promising avenue for enhancing sepsis treatment outcomes, contributing to improved patient care and reduced healthcare costs