1167 research outputs found
Sort by
AFM - One-line measurements - Raw Data
No full textThis raw data file contains AFM measurements acquired in PeakForce Tapping mode using a custom “one-line mode” setup, where the slow scan axis was disabled to repeatedly scan the same line. The data was collected on the sharp edge of the polymer sample, focusing on a single 20 µm line with a resolution of 1024 samples/line. A total of three consecutive scans were recorded under the following conditions: tip velocity of 20 µm/s, constant peak force of 25 nN, and excitation frequency of 2 kHz with a 100 nm oscillation amplitude. During acquisition, the relative humidity (RH) was cycled between 70% and 90%, completing approximately 5.5 cycles over the course of the measurement
Semantic Self-adaptation: Enhancing Generalization with a Single Sample
No full textThe lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation. Previous studies relied on the assumption of a static model, i. e., once the training process is complete, model parameters remain fixed at test time. In this work, we challenge this premise with a self-adaptive approach for semantic segmentation that adjusts the inference process to each input sample. Self-adaptation operates on two levels. First, it fine-tunes the parameters of convolutional layers to the input image using consistency regularization. Second, in Batch Normalization layers, self-adaptation interpolates between the training and the reference distribution derived from a single test sample. Despite both techniques being well known in the literature, their combination sets new state-of-the-art accuracy on synthetic-to-real generalization benchmarks. Our empirical study suggests that self-adaptation may complement the established practice of model regularization at training time for improving deep network generalization to out-of-domain data
Supplementary Material | An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systems
No full textHere you can find the supplementary material to the paper “An Empirical Comparison of Machine Learning Methods for Thermal Load Forecasting in Industrial Production Systems”: SampleMeasurementDataETAFactoryHNLTHNHT.csv (data set with measurement data of the thermal power of Heating Network Low Temperate and Heating Network High Temperature in kW, the mean ambient temperature of the next 48 hours in °C and the production state (no production | production) of the throughput parts cleaning machine in the ETA Factory)
Understanding the swelling behavior of P(DMAA-co-MABP) copolymer in paper-based actuators
No full textUnderstanding the swelling behavior of P(DMAA-co-MABP) copolymer in paper-based actuator
RevUtil
No full textProviding constructive feedback to paper authors is a core component of peer review. With reviewers increasingly having less time to perform reviews, automated support systems are required to ensure high reviewing quality, thus making the feedback in reviews useful for authors. To this end, we identify four key aspects of review comments (individual points in weakness sections of reviews) that drive the utility for authors: Actionability, Grounding & Specificity, Verifiability, and Helpfulness. To enable evaluation and development of models assessing review comments, we introduce the RevUtil dataset. We collect 1,430 human-labeled review comments and scale our data with 10k synthetically labeled comments for training purposes. The synthetic data additionally contains rationales, i.e., explanations for the aspect score of a review comment. Employing the RevUtil dataset, we benchmark fine-tuned models for assessing review comments on these aspects and generating rationales. Our experiments demonstrate that these fine-tuned models achieve agreement levels with humans comparable to, and in some cases exceeding, those of powerful closed models like GPT-4o. Our analysis further reveals that machine-generated reviews generally underperform human reviews on our four aspects.v1.
Non-linear mode coupling and excitation in non-axisymmetric droplet shape oscillations
No full textNon-linear mode coupling and excitation in non-axisymmetric droplet shape oscillation
The Nature of NLP: Analyzing Contributions in NLP Papers
No full textNLPContributions is a manually curated corpus of 2,888 peer-reviewed papers from the ACL Anthology (1974 – Feb 2024) in which every abstract sentence is annotated with up to eight fine-grained contribution types clustered into Knowledge (e.g., k-dataset, k-method) and Artifact (e.g., a-dataset, a-task) categories. The dataset is released in JSON and is intended for tasks such as contribution-sentence detection, multi-label classification, and longitudinal scientometric analyses
Laser-writing of fluorescent copolymers in mesoporous silica thin films:Public-data
No full textPowerPoint File of the public data of the paper, including responsive Origin Data. (In addition, there is a zip-archive including the raw data for the figures.
Is this chart lying to me? Automating the detection of misleading visualizations
No full textThe Misviz and Misviz-synth datasets accompany the paper "Is this chart lying to me? Automating the detection of misleading visualizations'". The datasets contain examples of misleading and non misleading visualizations. Misviz contains real-world chart images, while the charts in Misviz-synth are synthetic. The datasets are made available under a CC-BY-SA-4.0 license. Please cite our paper if you find this dataset useful to your work.V
Heterochromatome wide analyses reveal MBD2 as a phase separation scaffold for heterochromatin compartmentalization and composition
No full textHeterochromatin is essential for nuclear integrity, genome stability, and gene regulation. However, the mechanisms governing heterochromatin compartmentalization remain poorly understood. Recent studies suggest that phase separation underlies the organization of heterochromatin. Here, we integrated quantitative spatial proteomics, phase separation assays, and phase separation prediction tools to identify and characterize candidate phase separation scaffold proteins involved in heterochromatin compartmentalization. We in vitro reconstituted phase-separated heterochromatin condensates using heterochromatin fractions isolated from mouse brain. Mass spectrometric analysis yielded around 1000 proteins within them from which 250 were predicted to have scaffold phase separation properties using machine learning-based phase separation protein prediction tools. From these, 20 proteins, including methyl-CpG binding domain protein 2 (MBD2), were localized to pericentric heterochromatin compartments using gene ontology annotation. We demonstrated that MBD2 undergoes liquid-liquid phase separation via coiled coil-mediated homo-oligomerization, forming liquid-like condensates that regulate heterochromatin compartmentalization. Moreover, we found that MBD2-driven phase separation excludes histone acetyltransferase and recruits histone deacetylases. This was tested and validated in cellulo by the MBD2 mediated-NuRD assembly and subsequent deacetylation of histone H3 K27 and K9 within heterochromatin. This study advances our understanding of heterochromatin compartmentalization and highlights the role of MBD2 in heterochromatin dynamics and composition functionally regulating chromatin states.for revie