Was Clarke a Voluntarist? by Lukas Wolf

“…In response, defenders of the distinction have argued that these labels are needed in order to account for the famous correspondence between Leibniz (intellectualist) and Clarke (voluntarist). …”
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ECOLOGICAL AND SOCIALLY RESPONSIBLE ACTIVITIES OF BUSINESSES AS A DETERMINANT OF CUSTOMER PURCHASING BEHAVIOR by Katarzyna KILIAŃSKA

“…The research indicated that awareness of eco-labels positively influences the decision to purchase a product from an entity engaged in pro-environmental and pro-social activities. …”
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La refondation architecturale de la Cour de justice européenne à Luxembourg by Lorenzo Diez, Dominique Perrault

“…Finally, it shines a light on the promising innovation introduced by its label as “Outstanding Contemporary Architecture”, namely the possibility, through the label’s sliding 100-year period, of superimposing the theories and practices of architectural creation and conservation.…”
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Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans by Amr Elsawy, PhD, Tiarnan D.L. Keenan, PhD, MD, Alisa T. Thavikulwat, MD, Amy Lu, MD, Sunil Bellur, MD, Souvick Mukherjee, PhD, Elvira Agron, MS, Qingyu Chen, PhD, Emily Y. Chew, MD, Zhiyong Lu, PhD

“…Methods: Two datasets comprising of 1304 (826 labeled) and 1479 (1366 labeled) OCT scans were used to develop and evaluate Deep-RPD-Net and baseline models. …”
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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

“…In practical industrial environment, variable working condition can result in shifts in data distributions, and the labeled fault data in various working conditions is difficult to collect because rotating machines often works in normal status, and the insufficient labeled fault data brings data samples imbalance and performance degradation of intelligent fault diagnosis model. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. Unfortunately, labeled samples for pediatric supracondylar fractures are scarce and difficult to obtain. …”
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Comparison of HER2-Targeted Antibodies for Fluorescence-Guided Surgery in Breast Cancer by Solmaz AghaAmiri, Jo Simien, Alastair M. Thompson, Julie Voss, Sukhen C. Ghosh, Servando Hernandez Vargas, Sarah Kim, Ali Azhdarinia, Hop S. Tran Cao

“…In vitro findings demonstrated HER2-mediated binding for both fluorescent immunoconjugates and were in agreement with radioligand assays using dual-labeled immunoconjugates. In vivo and ex vivo studies showed preferential accumulation of the fluorescently-labeled mAbs in tumors and similar tumor-to-background ratios. …”
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Assessing Physician and Patient Agreement on Whether Patient Outcomes Captured in Clinical Progress Notes Reflect Treatment Success: Cross-Sectional Study by Sarah B Floyd, Jordyn C Sutton, Marvin Okon, Mary McCarthy, Liza Fisher, Benjamin Judkins, Zachary Cole Reynolds, Ann Blair Kennedy

“…From the full dataset of 1000 physician-labeled notes, a stratified random sample of 25 notes from each outcome label group was identified for this study. …”
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Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance by Arie Nugroho, M. Arief Soeleman, Ricardus Anggi Pramunendar, Affandy Affandy, Aris Nurhindarto

“…Segmentasi gambar adalah salah satu bidang dalam computer vision yang membahas bagaimana cara komputer mempelajari dan mengenali segmen dari suatu gambar sesuai label yang ditentukan. Dalam kenyataannya banyak data yang mempunyai class atau label yang tidak seimbang, tentunya akan mempengaruhi tingkat akurasi dari suatu prediksi. …”
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Segment anything model for few-shot medical image segmentation with domain tuning by Weili Shi, Penglong Zhang, Yuqin Li, Zhengang Jiang

“…However, acquiring large labeled datasets remains unattainable due to the substantial expertise and time required for image labeling, as well as heightened patient privacy concerns. …”
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A Case of Severe Cushing Syndrome due to Metastatic Adrenocortical Carcinoma Treated With Osilodrostat by Kathleen R. Ruddiman, DO, Catherine E. Price, MD, ECNU, FACE, Alexander K. Bonnecaze, MD

“…Background/Objective: Osilodrostat used with block-and-replace dosing regimen is an off-label alternative to traditional management of Cushing syndrome due to adrenocortical carcinoma (ACC). …”
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Comparison Between Convolutional Neural Network CNN and SVM in Skin Cancer Images Recognition by Zaid Ghazi Hadi, Ahmed R. Ajel, Ayad Q. Al-Dujaili

“…The network inputs are only disease labels and image pixels. About 320 clinical images of the different diseases have been used to train the CNN. …”
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DC-NNMN: Across Components Fault Diagnosis Based on Deep Few-Shot Learning by Juan Xu, Pengfei Xu, Zhenchun Wei, Xu Ding, Lei Shi

“…The cosine distance is merged into the K-Nearest Neighbor method to model the distance distribution between the unlabeled sample from the query set and labeled sample from the support set in high-dimensional fault features. …”
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Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization by Fereshteh Karimi, Mohammad Bagher Dowlatshahi, Amin Hashemi

“…The significance of semi-supervised learning becomes obvious when labeled instances are not always accessible; however, labeling such data may be costly or time-consuming. …”
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Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar by Yukun Gao, Lin Cao, Zongmin Zhao, Dongfeng Wang, Chong Fu, Yanan Guo

“…Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. …”
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Exploring the Therapeutic Landscape: A Narrative Review on Topical and Oral Phosphodiesterase-4 Inhibitors in Dermatology by Elena Carmona-Rocha, Lluís Rusiñol, Lluís Puig

“…Off-label use has been reported in diverse dermatological conditions, including aphthous stomatitis, chronic actinic dermatitis, atopic dermatitis, cutaneous sarcoidosis, hidradenitis suppurativa, lichen planus, and discoid lupus erythematosus. …”
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New Paraquat Requirements by Frederick Fishel, Brett Bultemeier, Jay Ferrell

“… This revision addresses the mitigation measures being undertaken by the EPA which became new labeling requirements for all paraquat products November 14, 2019. https://edis.ifas.ufl.edu/pi279 …”
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New Paraquat Requirements by Frederick Fishel, Brett Bultemeier, Jay Ferrell

“… This revision addresses the mitigation measures being undertaken by the EPA which became new labeling requirements for all paraquat products November 14, 2019. https://edis.ifas.ufl.edu/pi279 …”
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Application of semi-supervised Mean Teacher to rock image segmentation by Jiashan Li, Yuxue Wang

“…To address the issue of requiring a large number of labeled images for model training in traditional image segmentation methods, this paper proposes an improved semi-supervised Mean Teacher algorithm based on ResNet34-UNet. …”
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A semi-supervised transfer learning recognition method for radar compound jamming under small samples by Jinqiang WANG, Minhong SUN, Xianghong TANG, Zhaoyang QIU, Deguo ZENG

“…Aiming at the problem that more and more kinds of radar compound jamming signals and too few training samples were difficult to make the deep learning model reach the optimal state, a semi-supervised transfer learning recognition method for radar compound jamming under small samples was proposed, which solved the problem of low network training accuracy caused by the difficulty in obtaining labeled samples through unlabeled samples.The feature extractor and classifier obtained after pre-training of single jamming data set were transferred to small-scale compound jamming data set, and the model was fine-tuning by using weight imprinting and semi-supervised learning.The model parameters were optimized by the proposed nearest neighbor correlation loss nearest neighbor correlation loss (NNCL).The experimental results show that the recognition accuracy of the model can reach 93.20% when the jamming-to-noise ratio is 10 dB and there are only 5 labeled samples of the new class of compound jamming signals.…”
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