Developmental Scaffolding with Large Language Models

Exploratoration and self-observation are key mechanisms of infant sensorimotor development. These processes are further guided by parental scaffolding accelerating skill and knowledge acquisition. In developmental robotics, this approach has been adopted often by having a human acting as the source of scaffolding. In this study, we investigate whether Large Language Models (LLMs) can act as a scaffolding agent for a robotic system that aims to learn to predict the effects of its actions. To this end, an object manipulation setup is considered where one object can be picked and placed on top of or in the vicinity of another object. The adopted LLM is asked to guide the action selection process through algorithmically generated state descriptions and action selection alternatives in natural language. The simulation experiments that include cubes in this setup show that LLM-guided (GPT3.5-guided) learning yields significantly faster discovery of novel structures compared to random exploration. However, we observed that GPT3.5 fails to effectively guide the robot in generating structures with different affordances such as cubes and spheres. Overall, we conclude that even without fine-tuning, LLMs may serve as a moderate scaffolding agent for improving robot learning, however, they still lack affordance understanding which limits the applicability of the current LLMs in robotic scaffolding tasks

Discovering Predictive Relational Object Symbols with Symbolic Attentive Layers

In this paper, we propose and realize a new deep learning architecture for discovering symbolic representations for objects and their relations based on the self-supervised continuous interaction of a manipulator robot with multiple objects on a tabletop environment. The key feature of the model is that it can handle a changing number number of objects naturally and map the object-object relations into symbolic domain explicitly. In the model, we employ a self-attention layer that computes discrete attention weights from object features, which are treated as relational symbols between objects. These relational symbols are then used to aggregate the learned object symbols and predict the effects of executed actions on each object. The result is a pipeline that allows the formation of object symbols and relational symbols from a dataset of object features, actions, and effects in an end-to-end manner. We compare the performance of our proposed architecture with state-of-the-art symbol discovery methods in a simulated tabletop environment where the robot needs to discover symbols related to the relative positions of objects to predict the observed effect successfully. Our experiments show that the proposed architecture performs better than other baselines in effect prediction while forming not only object symbols but also relational symbols. Furthermore, we analyze the learned symbols and relational patterns between objects to learn about how the model interprets the environment. Our analysis shows that the learned symbols relate to the relative positions of objects, object types, and their horizontal alignment on the table, which reflect the regularities in the environment.Comment: arXiv admin note: text overlap with arXiv:2208.0102

Catalytical decarboxylation of lignites

The catalytic effect of the transition metal ions Cr2+, Mn2+, Fe2+, Co2+, Cu2+ and Zn2+ on the decarboxylation of Beypazari lignite was investigated, in terms of the change in calorific values of the decarboxylated lignite samples obtained after the decarboxylation process and activation energies of the decarboxylation processes. The optimum temperature to run the decarboxylation experiments and the optimum concentrations of the metal ion loadings to obtain the highest calorific value coal after decarboxylation reactions were determined. Catalytic activity of Fe2+, Cu2+ and Zn2+ was relatively higher than those of Cr2+, Mn2+ and Co2+ ions in decarboxylation reactions. Activation energies of the decarboxylation reactions using different metal ions were calculated and correlated with increases in the calorific values. Catalytic effect of different metal ions were also related to their d-electron configurations of the ions

Thermal decarboxylation of demineralized Turkish Beypazari lignite by the catalytic effect of Cr2+, Fe2+, and Co2+

Demineralized Beypazari lignite were thermally decarboxylated using Cr2+, Fe2C, and Co2+ as decarboxylation catalysts. Effective loadings of Cr2+, Fe2+, and Co2C were 2, 5, and 3%, respectively. The calorific values of the demineralized lignite samples increased after the thermal decarboxylation experiments to values about 6, 12, and 15% higher than that of the untreated demineralized sample, when Cr2+, Fe2+, and Co2+, respectively, were used as catalysts. The most effective catalyst, with respect to the lowest activation energy attained, was Cr2+. Decarboxylation temperatures using Cr2+, Fe2+, and Co2+ as catalysts were 150, 100, and 200oC, respectively

Anna Karenina principle in personalized treatment of bladder cancer according to oncogram: which drug for which patient?

Aim: To evaluate the ex vivo efficacy of chemotherapy, immunotherapy and targeted agents with the oncogram method in patients with bladder cancer and determine the most appropriate personalized treatment agent using immune markers. Materials & methods: Bladder cancer tissues were obtained from each patient. After cultivation, cell cultures were divided into 12 groups for each patient and 11 drugs were administered. Cell viability and immunohistochemistry expression were examined. Results: A good response rate was determined to be a 23% viability drop. The nivolumab good response rate was slightly better in PD-L1-positive patients and the ipilimumab good response rate was slightly better in tumoral CTLA-4-positive cases. Interestingly, the cetuximab response was worse in EGFR-positive cases. Conclusion: Although good responses of drug groups after their ex vivo application by using oncogram were found to be higher than control group, this outcome differed on a per patient basis. Plain language summaryBladder cancer primary cell cultures were shown to be effective for drug sensitivity and also able to be used ex vivo in the process of determining personalized treatment. The ex vivo efficacy of 11 different agents was evaluated with oncogram in bladder cancer cell cultures obtained from patients. Together with clinicopathological features, evaluation of drug responses detected by oncogram can provide important information for pretreatment drug selection when deciding on individualized treatment. Tweetable abstractEvaluation of drug responses detected by oncogram can provide important information for pretreatment drug selection when determining individualized treatment. These results show us that the Anna Karenina principle can be adapted to bladder cancer