“…We compared our Replicative Contrastive Learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genome and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.…”
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“…Using simulation, we found that FastRecomb achieves state-of-the-art performance at 10k resolution, in terms of correlation coefficients between the estimated map and the ground truth. This is mainly due to the fact that FastRecomb can effectively take advantage of large panels comprising more than hundreds of thousands of haplotypes. …”
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“…We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness – detected states that result in a change in performance when stimulation is applied compared to no stimulation. …”
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“…The developed algorithm is trained on 3375 ground truth images (at different front-lit illuminations), less sensitive to the edges of the drops, and retains its stability for images that are synthetically blurred with higher Gaussian Blurring (GB) values (GB: 0–22) if compared to existing goniometers (GB: 0–12). …”
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“…This happens because early raters’ ideological bias, which is aligned with a message, influences later raters’ assessments away from the truth. These results suggest that network segregation poses an important problem for community misinformation detection systems that must be accounted for in the design of such systems.…”
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“…We provide installation metadata for more than 28000 installations. We supply ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. …”
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“…This paper proposes a method that applies a neural network for synchrotron X-ray Computed Tomography (CT) data to reconstruct the high-quality 3D structure of alveoli in intact mouse lungs at expiration, without needing ground-truth data. Our approach reconstructs the spatial sequence of CT images by using a deep-image prior with interpolated input latent variables, and in this way significantly enhances the images of alveolar structure compared with the prior art. …”
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“…SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.…”
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“…The model predicted the areas of dump particles with a mean percentage error of 0.39% and a standard deviation of 0.25 when compared to the ground truth values. The calculation of coordinates of the detected boundaries using the model significantly reduces the time and effort that are generally put in rock mechanics laboratories.…”
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“…Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. …”
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“…Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. …”
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“…This work also led to the production of a ground truth tractogram with less broken or looping streamlines and of an example of processed data, all available on the Tractometer website. …”
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“…In addition, technology readiness (as hard self-efficacy) and health-related information literacy (soft self-efficacy) are critical to cope with the dark aspects of information exhaustion, overload, and pandemic seriousness in the post-truth era. The study is unique, as it examines the role of the seriousness of the pandemic at its source and destination and fear of new outbreaks simultaneously, underlining the potential future of immersive tourism (i.e., virtual reality, augmented reality, or mixed reality-based tourism). …”
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“…Under the conditions tested, MSA could correctly detect simulated ground‐truth lesion‐symptom relationships with a sensitivity of 75% and specificity of ~90%. …”
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“…The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.…”
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“…EmergeNet detects the emergence timing with 100% accuracy compared with human-annotated ground-truth. Furthermore, it significantly outperforms UNet by generating very high-quality segmented masks of the coleoptiles in both natural light and dark environmental conditions.…”
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“…An AI model was trained and evaluated with the ground truth being whether a given applicant was invited for an interview. …”
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“…Although diaries and questionnaires on near-work show correlation to some degree, it remains unknown how they may correspond to ground truth. This is an important consideration because valid estimates of near-work have great utility for understanding the mechanisms by which dioptric demand drives excessive eye-growth, which is not yet entirely understood. …”
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“…Thematic analysis (i.e., organizing, coding, synthesizing) was used to analyze a priori themes based on the four agreements of the courageous conversations framework, stay engaged, expect to experience discomfort, speak your truth, and expect and accept non-closure. RESULTS: A total of 41 participants of which 20 (48.78%) were department staff members, 11 (26.83%) were department faculty members, and 10 (24.30%) were graduate students. …”
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“…With a dose of 43 mGy for a 1 mm thin object imaged with protons up to 20 MeV, the reconstructed WET of defined regions-of-interest was within 1.5% of the ground truth values. The spatial resolution, which strongly depends on the gap between object and imaging detector, was 2.5 lp mm(−1) for a realistic distance of 5 mm. …”
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