Manifest
发表于 2025-3-30 10:19:01
rom the thematic hook “organization and knowledge” to the various theoretical perspectives. The concept of knowledge has gained much popularity in the organizational discourse of recent years, both in the context of popular and practically oriented management and organizational doctrines as well as
ARK
发表于 2025-3-30 14:58:09
Yiming Qian,Liangzhi Li,Huazhu Fu,Meng Wang,Qingsheng Peng,Yih Chung Tham,Chingyu Cheng,Yong Liu,Ricen und damit in der Verwendung von Macht kommt: Dabei gewinnt vor allem die Orientierung an Person und Gruppe massiv an Bedeutung. Auf eine Kurzformel gebracht bedeutet dies: Erfolgreiche Führung muss neben der Organisationsdynamik eine Expertise für die Gruppendynamik entwickeln. Allerdings stehen
不透明性
发表于 2025-3-30 19:31:49
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细颈瓶
发表于 2025-3-31 00:28:59
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentationbel-limited scenarios can lead to overfitting and suboptimal performance. Recently, prompt tuning has emerged as a more promising technique that introduces a few additional tunable parameters as prompts to a task-agnostic pre-trained model, and updates only these parameters using supervision from li
合并
发表于 2025-3-31 02:22:11
COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentationmance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the enti
删减
发表于 2025-3-31 07:01:56
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Cholesterol
发表于 2025-3-31 11:41:36
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incisive
发表于 2025-3-31 16:30:40
PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultraod that measures its thickness and roughness during routine ultrasound scans. Although advanced deep learning technology has shown promise in enabling automatic and accurate medical image segmentation, the lack of a large quantity of high-quality CIM labels may hinder the model training process. Act