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13 Aug

Slicing and Dicing the Real-Time Analytics Database Market

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(Tee11/Shutterstock) Organizations that are in the market for analytics databases that can serve an enormous quantity of queries on massive sets of fast-changing data may want to check out the latest Gigaom Sonar report on real-time analytics databases. Real-time analytics databases are a relatively new product category that has emerged over the past few years to serve the most demanding analytics workloads. The offerings in this sector combine existing technological capabilities, like OLAP and streaming data, in new ways to address novel data processing challenges at massive scale. The new Gigaom Sonar report from Andrew Brust, the analyst group’s longtime…

13 Aug

FuXi Weather: An end-to-end machine learning weather data assimilation and forecasting system

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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

13 Aug

Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution

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arXiv:2408.05440v1 Announce Type: new Abstract: Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is…

13 Aug

Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction

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arXiv:2408.05555v1 Announce Type: new Abstract: Previous studies reveal that Electronic Health Records (EHR), which have been widely adopted in the U.S. to allow patients to access their personal medical information, do not have high readability to patients due to the prevalence of medical jargon. Tailoring medical notes to individual comprehension by identifying jargon that is difficult for each person will enhance the utility of generative models. We present the first quantitative analysis to measure the impact of role-playing in LLM in medical term extraction. By comparing the results of Mechanical Turk workers over 20 sentences, our study demonstrates that LLM…

13 Aug

Mathematical Models of Computation in Superposition

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arXiv:2408.05451v1 Announce Type: new Abstract: Superposition -- when a neural network represents more ``features'' than it has dimensions -- seems to pose a serious challenge to mechanistically interpreting current AI systems. Existing theory work studies emph{representational} superposition, where superposition is only used when passing information through bottlenecks. In this work, we present mathematical models of emph{computation} in superposition, where superposition is actively helpful for efficiently accomplishing the task. We first construct a task of efficiently emulating a circuit that takes the AND of the $binom{m}{2}$ pairs of each of $m$ features. We construct a 1-layer MLP that uses superposition to…

13 Aug

A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities

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[Submitted on 10 Aug 2024] View a PDF of the paper titled A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities, by Jungpil Shin and 4 other authors View PDF Abstract:Researchers have been developing Hand Gesture Recognition (HGR) systems to enhance natural, efficient, and authentic human-computer interaction, especially benefiting those who rely solely on hand gestures for communication. Despite significant progress, the automatic and precise identification of hand gestures remains a considerable challenge in computer vision. Recent studies have focused on specific modalities like RGB images, skeleton data, and spatiotemporal interest points. This paper provides a…

13 Aug

Multi-layer Sequence Labeling-based Joint Biomedical Event Extraction

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arXiv:2408.05545v1 Announce Type: new Abstract: In recent years, biomedical event extraction has been dominated by complicated pipeline and joint methods, which need to be simplified. In addition, existing work has not effectively utilized trigger word information explicitly. Hence, we propose MLSL, a method based on multi-layer sequence labeling for joint biomedical event extraction. MLSL does not introduce prior knowledge and complex structures. Moreover, it explicitly incorporates the information of candidate trigger words into the sequence labeling to learn the interaction relationships between trigger words and argument roles. Based on this, MLSL can learn well with just a simple workflow. Extensive…

13 Aug

Predicting Long-Term Allograft Survival in Liver Transplant Recipients

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arXiv:2408.05437v1 Announce Type: new Abstract: Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant, leading to mortality or the need for retransplantation. Providing an accurate and interpretable model for individualized risk estimation of graft failure is essential for improving post-transplant care. To this end, we introduce the Model for Allograft Survival (MAS), a simple linear risk score that outperforms other advanced survival models. Using longitudinal patient follow-up data from the United States (U.S.), we develop our models on 82,959 liver transplant recipients and conduct multi-site evaluations on 11 regions. Additionally, by testing on a…

13 Aug

SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection

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arXiv:2408.05426v1 Announce Type: new Abstract: Laryngo-pharyngeal cancer (LPC) is a highly fatal malignant disease affecting the head and neck region. Previous studies on endoscopic tumor detection, particularly those leveraging dual-branch network architectures, have shown significant advancements in tumor detection. These studies highlight the potential of dual-branch networks in improving diagnostic accuracy by effectively integrating global and local (lesion) feature extraction. However, they are still limited in their capabilities to accurately locate the lesion region and capture the discriminative feature information between the global and local branches. To address these issues, we propose a novel SAM-guided fusion network (SAM-FNet), a dual-branch…

13 Aug

P3: A Policy-Driven, Pace-Adaptive, and Diversity-Promoted Framework for Optimizing LLM Training

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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