Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Systems, Process and Knowledge Management
Title: Softcite dataset: A dataset of software mentions in biomedical and economic research publications
Abstract: Software contributions to academic research are relatively invisible, especially to the formalized scholarly reputation system based on bibliometrics. In this article, we introduce a gold‐standard dataset of software mentions from the manual annotation of 4,971 academic PDFs in biomedicine and economics. The dataset is intended to be used for automatic extraction of software mentions from PDF format research publications by supervised learning at scale. We provide a description of the dataset and an extended discussion of its creation process, including improved text conversion of academic PDFs. Finally, we reflect on our challenges and lessons learned during the dataset creation, in hope of encouraging more discussion about creating datasets for machine learning use.
Extracted statement — Relation types: Version
Research field: Inorganic Chemistry
Title: Anilido-oxazoline-ligated rare-earth metal complexes: synthesis, characterization and highly cis-1,4-selective polymerization of isoprene
Abstract: <p>Anilido-oxazoline-ligated rare-earth metal complexes show strong fluorescence emissions and good catalytic performance on isoprene polymerization with high <italic>cis</italic>-1,4-selectivity.</p>
Extracted statement — Ligand: Anilido-oxazoline
Research field: Inorganic Chemistry
Title: Etching Silicon with Aqueous Acidic Ozone Solutions: Reactivity Studies and Surface Investigations
Abstract: Aqueous acidic ozone (O3)-containing solutions are increasingly used for silicon treatment in photovoltaic and semiconductor industries. We studied the behavior of aqueous hydrofluoric acid (HF)-containing solutions (i.e., HF–O3, HF–H2SO4–O3, and HF–HCl–O3 mixtures) toward boron-doped solar-grade (100) silicon wafers. The solubility of O3 and etching rates at 20 °C were investigated. The mixtures were analyzed for the potential oxidizing species by UV–vis and Raman spectroscopy. Concentrations of O3 (aq), O3 (g), and Cl2 (aq) were determined by titrimetric volumetric analysis. F–, Cl–, and SO42– ion contents were determined by ion chromatography. Model experiments were performed to investigate the oxidation of H-terminated silicon surfaces by H2O–O2, H2O–O3, H2O–H2SO4–O3, and H2O–HCl–O3 mixtures. The oxidation was monitored by diffuse reflection infrared Fourier transformation (DRIFT) spectroscopy. The resulting surfaces were examined by scanning electron microscopy (SEM) and X-ray photoelectron spectrosc...
Extracted statement — substrate: Silicon
Research field: Labor Economics
Title: The Impact of the Coronavirus Lockdown on Mental Health: Evidence from the US
Abstract: The coronavirus outbreak has caused significant disruptions to people’s lives. We document the impact of state-wide stay-at-home orders on mental health using real time survey data in the US. The lockdown measures lowered mental health by 0.085 standard deviations. This large negative effect is entirely driven by women. As a result of the lockdown measures, the existing gender gap in mental health has increased by 66%. The negative effect on women’s mental health cannot be explained by an increase in financial worries or childcare responsibilities.
Extracted statement — Control variables: Gender
Research field: Labor Economics
Title: The Impact of the Coronavirus Lockdown on Mental Health: Evidence from the US
Abstract: The coronavirus outbreak has caused significant disruptions to people’s lives. We document the impact of state-wide stay-at-home orders on mental health using real time survey data in the US. The lockdown measures lowered mental health by 0.085 standard deviations. This large negative effect is entirely driven by women. As a result of the lockdown measures, the existing gender gap in mental health has increased by 66%. The negative effect on women’s mental health cannot be explained by an increase in financial worries or childcare responsibilities.
Extracted statement — Examined (sub-)group: men
Research field: Labor Economics
Title: The Impact of the Coronavirus Lockdown on Mental Health: Evidence from the US
Abstract: The coronavirus outbreak has caused significant disruptions to people’s lives. We document the impact of state-wide stay-at-home orders on mental health using real time survey data in the US. The lockdown measures lowered mental health by 0.085 standard deviations. This large negative effect is entirely driven by women. As a result of the lockdown measures, the existing gender gap in mental health has increased by 66%. The negative effect on women’s mental health cannot be explained by an increase in financial worries or childcare responsibilities.
Extracted statement — Examined (sub-)group: women
Research field: Machine Learning
Title: DeepWalk: online learning of social representations
Abstract: We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Extracted statement — Uses dataset: BlogCatalog
Research field: Machine Learning
Title: DeepWalk: online learning of social representations
Abstract: We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Extracted statement — Uses dataset: Flickr
Research field: Machine Learning
Title: Clinical Concept Embeddings Learned from Massive Sources of Medical Data
Abstract: Word embeddings have emerged as a popular approach to unsupervised learning of word relationships in machine learning and natural language processing. In this article, we benchmark two of the most popular algorithms, GloVe and word2vec, to assess their suitability for capturing medical relationships in large sources of biomedical data. Leaning on recent theoretical insights, we provide a unified view of these algorithms and demonstrate how different sources of data can be combined to construct the largest ever set of embeddings for 108,477 medical concepts using an insurance claims database of 60 million members, 20 million clinical notes, and 1.7 million full text biomedical journal articles. We evaluate our approach, called cui2vec, on a set of clinically relevant benchmarks and in many instances demonstrate state of the art performance relative to previous results. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings.
Extracted statement — Has method : GloVe
Research field: Machine Learning
Title: Embedding logical queries on knowledge graphs
Abstract: Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.
Extracted statement — Type of data: Graph
Research field: Machine Learning
Title: DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
Abstract: Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever. Here, we combine the advantages of the popular bandit-based HPO method Hyperband (HB) and the evolutionary search approach of Differential Evolution (DE) to yield a new HPO method which we call DEHB. Comprehensive results on a very broad range of HPO problems, as well as a wide range of tabular benchmarks from neural architecture search, demonstrate that DEHB achieves strong performance far more robustly than all previous HPO methods we are aware of, especially for high-dimensional problems with discrete input dimensions. For example, DEHB is up to 1000x faster than random search. It is also efficient in computational time, conceptually simple and easy to implement, positioning it well to become a new default HPO method.
Extracted statement — keywords: Hyperband
Research field: Machine Learning
Title: Autoformer: Searching transformers for visual recognition
Abstract: Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at https://github.com/microsoft/Cream.
Extracted statement — keywords: ImageNet
Research field: Machine Learning
Title: Active Learning Yields Better Training Data for Scientific Named Entity Recognition
Abstract: Despite significant progress in natural language processing, machine learning models require substantial expertannotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is often more complicated when working with scientific text. For example, in polymer science, chemical structure may be encoded using nonstandard naming conventions, the same concept can be expressed using many different terms (synonymy), and authors may refer to polymers with ad-hoc labels. These challenges, which are not unique to polymer science, make it difficult to generate training data, as specialized skills are needed to label text correctly. We have previously designed polyNER, a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance. Our approach requires just five hours of expert time to achieve discrimination capacity comparable to that of a state-of-the-art chemical NER toolkit.
Extracted statement — Concept types: Polymers
Research field: Machine Learning
Title: NLTK: The Natural Language Toolkit
Abstract: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.
Extracted statement — is a: Toolkit
Research field: Machine Learning
Title: NLTK: The Natural Language Toolkit
Abstract: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.
Extracted statement — Documentation: Tutorials
Research field: Machine Learning
Title: Clinical Concept Embeddings Learned from Massive Sources of Medical Data
Abstract: Word embeddings have emerged as a popular approach to unsupervised learning of word relationships in machine learning and natural language processing. In this article, we benchmark two of the most popular algorithms, GloVe and word2vec, to assess their suitability for capturing medical relationships in large sources of biomedical data. Leaning on recent theoretical insights, we provide a unified view of these algorithms and demonstrate how different sources of data can be combined to construct the largest ever set of embeddings for 108,477 medical concepts using an insurance claims database of 60 million members, 20 million clinical notes, and 1.7 million full text biomedical journal articles. We evaluate our approach, called cui2vec, on a set of clinically relevant benchmarks and in many instances demonstrate state of the art performance relative to previous results. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings.
Extracted statement — Has method : word2vec
Research field: Machine Learning
Title: OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Abstract: MOTIVATION Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. RESULTS We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on either a corpus or abstracts or full-text articles to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins in a similarity measure to predict protein-protein interaction on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations using mouse model phenotypes. We demonstrate that OPA2Vec significantly outperforms existing methods for predicting gene-disease associations. Using evidence from mouse models, we apply OPA2Vec to identify candidate genes for several thousand rare and orphan diseases. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. AVAILABILITY AND IMPLEMENTATION https://github.com/bio-ontology-research-group/opa2vec. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Extracted statement — Has method : word2vec
Research field: Machine Learning
Title: DeepWalk: online learning of social representations
Abstract: We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Extracted statement — Uses dataset: Youtube
Research field: Materials Chemistry
Title: Up-scalable and controllable electrolytic production of photo-responsive nanostructured silicon
Abstract: The electrochemical reduction of solid silica has been investigated in molten CaCl2 at 900 °C for the one-step, up-scalable, controllable and affordable production of nanostructured silicon with promising photo-responsive properties. Cyclic voltammetry of the metallic cavity electrode loaded with fine silica powder was performed to elaborate the electrochemical reduction mechanism. Potentiostatic electrolysis of porous and dense silica pellets was carried out at different potentials, focusing on the influences of the electrolysis potential and the microstructure of the precursory silica on the product purity and microstructure. The findings suggest a potential range between −0.60 and −0.95 V (vs. Ag/AgCl) for the production of nanostructured silicon with high purity (>99 wt%). According to the elucidated mechanism on the electro-growth of the silicon nanostructures, optimal process parameters for the controllable preparation of high-purity silicon nanoparticles and nanowires were identified. Scaling-up the optimal electrolysis was successful at the gram-scale for the preparation of high-purity silicon nanowires which exhibited promising photo-responsive properties.
Extracted statement — (pseudo)reference electrode: Ag/AgCl
Research field: Materials Chemistry
Title: High-performance fullerene-free polymer solar cells with 6.31% efficiency
Abstract: <p>A nonfullerene electron acceptor (IEIC) based on indaceno[1,2-<italic>b</italic>:5,6-<italic>b</italic>′]dithiophene and 2-(3-oxo-2,3-dihydroinden-1-ylidene)malononitrile was designed and synthesized, and fullerene-free polymer solar cells based on the IEIC acceptor showed power conversion efficiencies of up to 6.31%.</p>
Extracted statement — Mobility: Electron
Research field: Materials Chemistry
Title: Non-fullerene polymer solar cells based on a selenophene-containing fused-ring acceptor with photovoltaic performance of 8.6%
Abstract: <p>In this work, we present a non-fullerene electron acceptor bearing a fused five-heterocyclic ring containing selenium atoms, denoted as IDSe-T-IC, for fullerene-free polymer solar cells (PSCs).</p>
Extracted statement — Mobility: Electron
Research field: Materials Chemistry
Title: Design and Synthesis of a Low Bandgap Small Molecule Acceptor for Efficient Polymer Solar Cells
Abstract: A novel non-fullerene acceptor, possessing a very low bandgap of 1.34 eV and a high-lying lowest unoccupied molecular orbital level of -3.95 eV, is designed and synthesized by introducing electron-donating alkoxy groups to the backbone of a conjugated small molecule. Impressive power conversion efficiencies of 8.4% and 10.7% are obtained for fabricated single and tandem polymer solar cells.
Extracted statement — Mobility: Electron