6 research outputs found
Open Arms: Open-Source Arms, Hands & Control
Open Arms is a novel open-source platform of realistic human-like robotic
hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the
capabilities and accessibility of humanoid robotic grasping and manipulation.
The Open Arms framework includes an open SDK and development environment,
simulation tools, and application development tools to build and operate Open
Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic
design, and manufacturing and their real-world applications with a teleoperated
nursing robot. From 2015 to 2022, the authors have designed and established the
manufacturing of Open Arms as a low-cost, high functionality robotic arms
hardware and software framework to serve both humanoid robot applications and
the urgent demand for low-cost prosthetics, as part of the Hanson Robotics
Sophia Robot platform. Using the techniques of consumer product manufacturing,
we set out to define modular, low-cost techniques for approximating the
dexterity and sensitivity of human hands. To demonstrate the dexterity and
control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN)
model that can generate robust antipodal grasps from input images of various
objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of
92.4% using our model architecture on a standard Cornell Grasping Dataset,
which contains a diverse set of household objects.Comment: Submitted to 36th Conference on Neural Information Processing Systems
(NeurIPS 2022
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Chemistry and materials science are complex. Recently, there have been great
successes in addressing this complexity using data-driven or computational
techniques. Yet, the necessity of input structured in very specific forms and
the fact that there is an ever-growing number of tools creates usability and
accessibility challenges. Coupled with the reality that much data in these
disciplines is unstructured, the effectiveness of these tools is limited.
Motivated by recent works that indicated that large language models (LLMs)
might help address some of these issues, we organized a hackathon event on the
applications of LLMs in chemistry, materials science, and beyond. This article
chronicles the projects built as part of this hackathon. Participants employed
LLMs for various applications, including predicting properties of molecules and
materials, designing novel interfaces for tools, extracting knowledge from
unstructured data, and developing new educational applications.
The diverse topics and the fact that working prototypes could be generated in
less than two days highlight that LLMs will profoundly impact the future of our
fields. The rich collection of ideas and projects also indicates that the
applications of LLMs are not limited to materials science and chemistry but
offer potential benefits to a wide range of scientific disciplines
Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer’s Disease
The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain’s neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer’s patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 × 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer’s vs. normal controls. The nonfractal-based approach provides a good representation of the brain’s neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively
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14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon â€
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines