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Emperical Interference

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Bioinspired Autonomous Miniature Robots

Dynamic Locomotion

Embodied Vision

Human Aspects of Machine Learning

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Learning and Dynamical Systems

Locomotion in Biorobotic and Somatic Systems

Micro, Nano, and Molecular Systems

Movement Generation and Control

Neural Capture and Synthesis

Physics for Inference and Optimization

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Human Aspects of Machine Learning Unpublished A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence Sühr, T., Samadi, S., Farronato, C. 2024 (Submitted) BibTeX
Dynamic Locomotion Unpublished An Open-Source Modular Treadmill for Dynamic Force Measurement with Load Dependant Range Adjustment Sarvestani, A., Ruppert, F., Badri-Spröwitz, A. 2023 (Submitted)
Ground reaction force sensing is one of the key components of gait analysis in legged locomotion research. To measure continuous force data during locomotion, we present a novel compound instrumented treadmill design. The treadmill is 1.7 m long, with a natural frequency of 170 Hz and an adjustable range that can be used for humans and small robots alike. Here, we present the treadmill’s design methodology and characterize it in its natural frequency, noise behavior and real-life performance. Additionally, we apply an ISO 376 norm conform calibration procedure for all spatial force directions and center of pressure position. We achieve a force accuracy of ≤ 5.6 N for the ground reaction forces and ≤ 13 mm in center of pressure position.
arXiv DOI URL BibTeX
Human Aspects of Machine Learning Social Foundations of Computation Unpublished Challenging the validity of personality tests for large language models Tom, S., Florian, D., Samadi, S., Kelava, A. arXiv, 2023 (Submitted)
With large language models (LLMs) like GPT-4 appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate personality traits of LLMs using questionnaires originally developed for humans. While reusing measures is a resource-efficient way to evaluate LLMs, careful adaptations are usually required to ensure that assessment results are valid even across human subpopulations. In this work, we provide evidence that LLMs’ responses to personality tests systematically deviate from human responses, implying that the results of these tests cannot be interpreted in the same way. Concretely, reversecoded items (“I am introverted” vs.“I am extraverted”) are often both answered affirmatively. Furthermore, variation across prompts designed to “steer” LLMs to simulate particular personality types does not follow the clear separation into five independent personality factors from human samples. In light of these results, we believe that it is important to investigate tests’ validity for LLMs before drawing strong conclusions about potentially ill-defined concepts like LLMs’“personality”.
BibTeX
Unpublished Developing formalin-based fixative agents for post-mortem brain MRI at 9.4T Magnetic Resonance in Medicine, 2021 BibTeX
Unpublished Estimations by stable motions and applications Das, A., Denker, M., Levina, A., Tabacu, L. 2020
We propose a nonparametric parameter estimation of confidence intervals when the underlying has large or infinite variance. We explain the method by a simple numerical example and provide an application to estimate the coupling strength in neuronal networks.
BibTeX
Unpublished Asymptotic Normality of the Median Heuristic Garreau, D. July 2017, preprint URL BibTeX