Traditional materials informatics leverages big data and machine learning (ML) to forecast material performance based on structural features but often overlooks valuable textual information. In this work, we proposed ...
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Traditional materials informatics leverages big data and machine learning (ML) to forecast material performance based on structural features but often overlooks valuable textual information. In this work, we proposed a novel methodology for predicting material performance through context-based modeling using large language models (LLMs). This method integrates both numerical and textual information, enhancing predictive accuracy and scalability. In the case study, the approach is applied to predict the performance of solid amine CO2 adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO2 adsorption uptake based on input features, including material properties and experimental conditions. The results show that context-based modeling can reduce prediction error in comparison to traditional ML models in the prediction task. We adopted Sapley Additive exPlanations (SHAP) to further elucidate the importance of various input features. This work highlights the potential of LLMs in materials science, offering a cost-effective, efficient solution for complex predictive tasks.
Online video sharing systems, such as YouTube, do not provide users enough support to explore community videos that portray people within their social circle. Such services typically consider each video clip as an iso...
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ISBN:
(纸本)9781450319928
Online video sharing systems, such as YouTube, do not provide users enough support to explore community videos that portray people within their social circle. Such services typically consider each video clip as an isolated object, and not as part of a set of related clips. Even though social networks archive media based on higher-order social relationships, they do not provide support for searching and navigating media content that was captured at a particular event by different people. The contribution of this paper is a web-based interface, and the underlying system infrastructure, that allow for socially-aware exploration of media assets from an event. The system combines the best from both video sharing systems and social networks, allowing users to explore and navigate (fragments of) video clips based on their own personal/social interests. The work resulted, as well, in the identification of key requirements for this novel type of socially-aware interfaces. This paper reports on comparative results from a two-phased study conducted during the last four years. The research effort includes a full implementation and deployment of a system, and a series of experiments and user trials that confirmed that our design decisions enable users to explore and view videos they care about.
Online video sharing systems, such as YouTube, do not provide users enough support to explore community videos that portray people within their social circle. Such services typically consider each video clip as an iso...
详细信息
ISBN:
(纸本)9781450319928
Online video sharing systems, such as YouTube, do not provide users enough support to explore community videos that portray people within their social circle. Such services typically consider each video clip as an isolated object, and not as part of a set of related clips. Even though social networks archive media based on higher-order social relationships, they do not provide support for searching and navigating media content that was captured at a particular event by different people. The contribution of this paper is a web-based interface, and the underlying system infrastructure, that allow for socially-aware exploration of media assets from an event. The system combines the best from both video sharing systems and social networks, allowing users to explore and navigate (fragments of) video clips based on their own personal/social interests. The work resulted, as well, in the identification of key requirements for this novel type of socially-aware interfaces. This paper reports on comparative results from a two-phased study conducted during the last four years. The research effort includes a full implementation and deployment of a system, and a series of experiments and user trials that confirmed that our design decisions enable users to explore and view videos they care about.
We propose and evaluate a number of novel improvements to the mesh-based coding scheme for 3-D brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing ...
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We propose and evaluate a number of novel improvements to the mesh-based coding scheme for 3-D brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing of only the brain part of the image;2) content-based (adaptive) mesh generation using spatial edges and optical flow between two consecutive slices;3) a simple solution for the aperture problem at the edges, where an accurate estimation of motion vectors is not possible;and 4) context-based entropy coding of the residues after motion compensation using affine transformations. We address only lossless coding of the images, and compare the performance of uniform and adaptive mesh-based schemes. The bit rates achieved (about 2 bits per voxel) by these schemes are comparable to those of the state-of-the-art three-dimensional (3-D) wavelet-based schemes. The mesh-based schemes have been shown to be effective for the compression of 3-D brain computed tomography data also. Adaptive mesh-based schemes perform marginally better than the uniform mesh-based methods, at the expense of increased complexity.
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