The large language model has demonstrated its ability to reason and interpret in text-to-text applications. Current Chain of Thought (CoT) research focuses on either explaining reasoning steps or improving prediction ...
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We focus on the problem of locating one sink on balanced binary tree networks with uniform edge capacities, all while minimizing the total evacuation time for all evacuees (minsum criterion). The challenge with sink l...
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Information flow analysis (IFA) is a powerful technique for verifying confidentiality and integrity. This is highly desirable for embedded systems, where security violations can lead to significant economic damages or...
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Microarray data, when coupled with advanced computational and statistical techniques, offers profound insights into cause of diseases and personalized therapy. However, the enormous genes present in microarray data po...
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Distant reading and distant viewing (complementing close reading/viewing), allow humanities researchers to process larger volumes of source material, effectively increasing the resolution of our view on the human cond...
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ISBN:
(纸本)9783031724398;9783031724404
Distant reading and distant viewing (complementing close reading/viewing), allow humanities researchers to process larger volumes of source material, effectively increasing the resolution of our view on the human condition. Recently, document layouting methods based on neural networks have shown promise, but it is still a challenge for pre-trained models to perform well when applied to completely novel digitised sources without any fine-tuning, or in cases where a departure from the original model's classification grammar is a hard requirement. In this paper, we present a new annotated dataset of 423 newspaper pages and the baseline performance of a fine-tuned model on the dataset spanning 46 years. We evaluate the minimal amount of data required to fine-tune a YOLOv8n model to classify advertisements and obituaries in the newspaper Slovenski narod, issued at the turn of the 20th century. We compare the performance of progressively smaller annotation datasets to determine a region of diminishing returns. We show that the increase in performance for every additional annotated image fine-tuning a YOLOv8n model tapers out after about 250 annotated labels regardless of the number of classes trained.
This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded s...
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ISBN:
(纸本)9783031661457;9783031661464
This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.
Concept Activation Vectors (CAVs) offer insights into neural network decision-making by linking human friendly concepts to the model’s internal feature extraction process. However, when a new set of CAVs is discovere...
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science collaborations use computer grids to run expensive computational tasks on large data sets. Tasks as jobs across the network demand data and thereby workload management and data allocation to maintain the compu...
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Artificial Intelligence, particularly in Machine Learning and related research areas such as Operational Research, currently faces a reproducibility crisis. Researchers encounter difficulties reproducing key results d...
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Entity alignment aims to discover different references to the same entity in different graphs, and it is a key technique for solving graph-related problems. It has developed into one of the important tasks in knowledg...
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