1167 research outputs found
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Code for Site Area Utilization
No full textcode for running color recognition on a specified image, along with calculating the percentage of said color in the image1.
2H and 17O NMR Studies of Solvent Dynamics Related to the Cononsolvency of Poly(N-Isopropyl Acrylamide) in Ethanol-Water Mixtures
No full textDatasets of the figures shown in the article with the same title as this submission. Original manuscript submitted to Soft Matter in January 2025. Revised version submitted to Soft Matter on 2025-03-04.2025-03-0
Content-Adaptive Downsampling in Convolutional Neural Networks
No full textMany convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost. However, this comes at the price of losing granularity in the feature maps, limiting the ability to correctly understand images or recover fine detail in dense prediction tasks. To address this, common practice is to replace the last few downsampling operations in a CNN with dilated convolutions, allowing to retain the feature map resolution without reducing the receptive field, albeit increasing the computational cost. This allows to trade off predictive performance against cost, depending on the output feature resolution. By either regularly downsampling or not downsampling the entire feature map, existing work implicitly treats all regions of the input image and subsequent feature maps as equally important, which generally does not hold. We propose an adaptive downsampling scheme that generalizes the above idea by allowing to process informative regions at a higher resolution than less informative ones. In a variety of experiments, we demonstrate the versatility of our adaptive downsampling strategy and empirically show that it improves the cost-accuracy trade-off of various established CNNs
Superresolution-Compatible DNA Labeling Technique with Silicon Rhodamine -Linked Nucleotide Reveals Chromatin Mobility and Organization Changes During Neuronal Differentiation
No full textChromatin dynamics play a crucial role in cellular differentiation, yet tools for studying global chromatin mobility in living cells remain limited. Here, we developed a novel probe for the metabolic labeling of chromatin and tracking its mobility during neural differentiation. The labeling system utilizes a newly developed silicon rhodamine-conjugated deoxycytidine triphosphate (dCSiRTP). We show that this dCTP is efficiently delivered into living human induced pluripotent stem cells (iPSCs) and neural stem cells (NSCs) via a synthetic transporter (SNTT1). Using correlative confocal microscopy and stimulated emission depletion (STED) super-resolution microscopy, we quantified the sizes of labeled chromatin domains. Time-lapse super-resolution microscopy combined with single particle tracking revealed that chromatin mobility decreases during the transition from iPSCs (pluripotent state) to NSCs and neurons (differentiated state). This reduction in mobility correlates with the differentiation state, reflecting changes in chromatin organization during cell fate commitment. Concomitant mechanistic insights obtained from micrococcal nuclease digestion assays, chromatin compaction and histone modification analyses revealed a decrease in chromatin accessibility during neuronal differentiation. These data indicate that chromatin adopts a more constrained structure with reduced accessibility and increased heterochromatin-associated histone modifications. These findings provide new insights into chromatin regulation during neurogenesis
How charges separate when surfaces are dewetted - Supplementary Material
No full textThis repository contains supplemental data for the publication
Aaron D. Ratschow, Lisa S. Bauer, Pravash Bista, Stefan A. L. Weber,
Hans-Jürgen Butt, and Steffen Hardt, How charges separate when surfaces
are dewetted, Physical Review Letters 132, 224002 (2024)
The folders starting with "fig" contain the data used in the respective plots of the main text and the supplemental material.
The file "20221020_Analytical_Derivation_handwritten.pdf" contains the original handwritten derivation of the analytical model with more details and intermediate calculation steps than the final paper version. Note that the variable naming has changed in the process of manuscript preparation.
Finally, the file "01_slide_electrification_Comsol_template" contains a COMSOL model file with the setup used in the numerical analysis. The numerical grid used in the final study is included and by adjusting the parameters in the parameter list, all results in the publication can be recalculated using this setup
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
No full textOmnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps without relying on expensive active sensing. However, existing omnidirectional stereo matching approaches achieve only limited depth accuracy across diverse environments, depth ranges, and lighting conditions, due to the scarcity of real-world data. We present DFI-OmniStereo, a novel omnidirectional stereo matching method that leverages a large-scale pre-trained foundation model for relative monocular depth estimation within an iterative optimization-based stereo matching architecture. We introduce a dedicated two-stage training strategy to utilize the relative monocular depth features for our omnidirectional stereo matching before scale-invariant fine-tuning. DFI-OmniStereo achieves state-of-the-art results on the real-world Helvipad dataset, reducing disparity MAE by approximately 16% compared to the previous best omnidirectional stereo method
BRONCO150 Mapping: Medically Relevant vs. Non-Medically Relevant Statements
No full textThis is a mapping file based on the BRONCO150 dataset, in which statements are labeled as either medically relevant or non-medically relevant. The original dataset is the publicly available Berlin-Tübingen Oncology Corpus (BRONCO150) by Kittner et al. (2021), accessible via the following DOI: 10.1093/jamiaopen/ooab025
OxyflameC7-Campaign2_AS-FTIR-Data
No full textDataset for the FTIR measurements in the second experimental campaign including the low swirl, 500kWth biomass flame. FTIR analysis optimized for the analysis of unburned hydrocarbons
STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond
No full textDataset associated with STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond
Critical text assessment is at the core of many expert activities, such as fact-checking, peer review, and essay grading. Yet, existing work treats critical text assessment as a black box problem, limiting interpretability and human-AI collaboration. To close this gap, we introduce Structured Reasoning in Critical Text Assessment (STRICTA), a novel specification framework to model text assessment as an explicit, step-wise reasoning process. STRICTA breaks down the assessment into a graph of interconnected reasoning steps drawing on causality theory (Pearl, 1995). This graph is populated based on expert interaction data
and used to study the assessment process and facilitate human-AI collaboration. We formally define STRICTA and apply it in a study
on biomedical paper assessment, resulting in a dataset of over 4000 reasoning steps from roughly 40 biomedical experts on more than 20 papers. We use this dataset to empirically study expert reasoning in critical text assessment, and investigate if LLMs are able to imitate and support experts within these workflows. The resulting tools and datasets pave the way for studying collaborative expert-AI reasoning in text assessment, in peer review and beyond
2025_Stegmann_Enhancing_Silver_Sintering
No full textUpdated research and raw data to publication: Enhancing Silver Sintering - Effect of Copper Substrate Microstructure on Silver Adhesion and Bond StrengthUpdated version after peer revie