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Getting Value Out of GenAI
21 Aug

Getting Value Out of GenAI

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(OK-product-studio/Shutterstock) As the initial excitement over generative AI starts to fade, some executives are beginning to question whether it will pan out in the long run. Others, however, are still quite bullish on GenAI’s potential to create value, provided it meets a few key requirements. Goldman Sachs turned some heads recently with a report casting doubt on GenAI and raising uncomfortable questions about the potential for companies to see a return on their GenAI investments. Gartner also issued a report recently saying 30% of GenAI initiatives will be abandoned by 2025. While there are some issues to be worked out…
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FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
21 Aug

FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models

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arXiv:2408.10276v1 Announce Type: new Abstract: Foundation models have demonstrated remarkable capabilities in handling diverse modalities and tasks, outperforming conventional artificial intelligence (AI) approaches that are highly task-specific and modality-reliant. In the medical domain, however, the development of comprehensive foundation models is constrained by limited access to diverse modalities and stringent privacy regulations. To address these constraints, this study introduces a novel knowledge injection approach, FedKIM, designed to scale the medical foundation model within a federated learning framework. FedKIM leverages lightweight local models to extract healthcare knowledge from private data and integrates this knowledge into a centralized foundation model using a…
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Parallel Processing of Point Cloud Ground Segmentation for Mechanical and Solid-State LiDARs
21 Aug

Parallel Processing of Point Cloud Ground Segmentation for Mechanical and Solid-State LiDARs

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arXiv:2408.10404v1 Announce Type: new Abstract: In this study, we introduce a novel parallel processing framework for real-time point cloud ground segmentation on FPGA platforms, aimed at adapting LiDAR algorithms to the evolving landscape from mechanical to solid-state LiDAR (SSL) technologies. Focusing on the ground segmentation task, we explore parallel processing techniques on existing approaches and adapt them to real-world SSL data handling. We validated frame-segmentation based parallel processing methods using point-based, voxel-based, and range-image-based ground segmentation approaches on the SemanticKITTI dataset based on mechanical LiDAR. The results revealed the superior performance and robustness of the range-image method, especially in its…
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NoMatterXAI: Generating “No Matter What” Alterfactual Examples for Explaining Black-Box Text Classification Models
21 Aug

NoMatterXAI: Generating “No Matter What” Alterfactual Examples for Explaining Black-Box Text Classification Models

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arXiv:2408.10528v1 Announce Type: new Abstract: In Explainable AI (XAI), counterfactual explanations (CEs) are a well-studied method to communicate feature relevance through contrastive reasoning of "what if" to explain AI models' predictions. However, they only focus on important (i.e., relevant) features and largely disregard less important (i.e., irrelevant) ones. Such irrelevant features can be crucial in many applications, especially when users need to ensure that an AI model's decisions are not affected or biased against specific attributes such as gender, race, religion, or political affiliation. To address this gap, the concept of alterfactual explanations (AEs) has been proposed. AEs explore an…
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FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy
21 Aug

FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Webcam-based Pupil Diameter Prediction Benefits from Upscaling
21 Aug

Webcam-based Pupil Diameter Prediction Benefits from Upscaling

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arXiv:2408.10397v1 Announce Type: new Abstract: Capturing pupil diameter is essential for assessing psychological and physiological states such as stress levels and cognitive load. However, the low resolution of images in eye datasets often hampers precise measurement. This study evaluates the impact of various upscaling methods, ranging from bicubic interpolation to advanced super-resolution, on pupil diameter predictions. We compare several pre-trained methods, including CodeFormer, GFPGAN, Real-ESRGAN, HAT, and SRResNet. Our findings suggest that pupil diameter prediction models trained on upscaled datasets are highly sensitive to the selected upscaling method and scale. Our results demonstrate that upscaling methods consistently enhance the accuracy…
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XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition
21 Aug

XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition

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arXiv:2408.10524v1 Announce Type: new Abstract: Contextualized ASR models have been demonstrated to effectively improve the recognition accuracy of uncommon phrases when a predefined phrase list is available. However, these models often struggle with bilingual settings, which are prevalent in code-switching speech recognition. In this study, we make the initial attempt to address this challenge by introducing a Cross-lingual Contextual Biasing(XCB) module. Specifically, we augment a pre-trained ASR model for the dominant language by integrating an auxiliary language biasing module and a supplementary language-specific loss, aimed at enhancing the recognition of phrases in the secondary language. Experimental results conducted on our…
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Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks
21 Aug

Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Evaluating Image-Based Face and Eye Tracking with Event Cameras
21 Aug

Evaluating Image-Based Face and Eye Tracking with Event Cameras

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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