Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Inorganic Chemistry
Title: Modified TMAH based etchant for improved etching characteristics on Si{1 0 0} wafer
Abstract: Wet bulk micromachining is a popular technique for the fabrication of microstructures in research labs as well as in industry. However, increasing the throughput still remains an active area of research, and can be done by increasing the etching rate. Moreover, the release time of a freestanding structure can be reduced if the undercutting rate at convex corners can be improved. In this paper, we investigate a non-conventional etchant in the form of NH2OH added in 5 wt% tetramethylammonium hydroxide (TMAH) to determine its etching characteristics. Our analysis is focused on a Si{1 0 0} wafer as this is the most widely used in the fabrication of planer devices (e.g. complementary metal oxide semiconductors) and microelectromechanical systems (e.g. inertial sensors). We perform a systematic and parametric analysis with concentrations of NH2OH varying from 5% to 20% in step of 5%, all in 5 wt% TMAH, to obtain the optimum concentration for achieving improved etching characteristics including higher etch rate, undercutting at convex corners, and smooth etched surface morphology. Average surface roughness (R a), etch depth, and undercutting length are measured using a 3D scanning laser microscope. Surface morphology of the etched Si{1 0 0} surface is examined using a scanning electron microscope. Our investigation has revealed a two-fold increment in the etch rate of a {1 0 0} surface with the addition of NH2OH in the TMAH solution. Additionally, the incorporation of NH2OH significantly improves the etched surface morphology and the undercutting at convex corners, which is highly desirable for the quick release of microstructures from the substrate. The results presented in this paper are extremely useful for engineering applications and will open a new direction of research for scientists in both academic and industrial laboratories.
Extracted statement — Primary etching solution: tetramethylammonium hydroxide
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 — Indicator for well-being: Mental health
Research field: Learner-Interface Interaction
Title: Dimensions of transactional distance in the world wide web learning environment: a factor analysis
Abstract: Moore's Theory of Transactional Distance hypothesizes that distance is a pedagogical, not geographic phenomenon. It is a distance of understandings and perceptions that might lead to a communication gap or a psychological space of potential misunderstandings between people. Moore also suggests that this distance has to be overcome if effective, deliberate, planned learning is to occur. However, the conceptualizations of transactional distance in a telecommunication era have not been systematically addressed. Investigating 71 learners' experiences with WorldWide Web, this study examined the postulate of Moore's theory and identified the dimensions (factors) constituting transactional distance in such learning environment. Exploratory factor analysis using a principal axis factor method was carried out. It was concluded that this concept represented multifaceted ideas. Transactional distance consisted of four dimensions-instructor-learner, learner-learner, learner-content, and learner-interface transactional distance. The results inform researchers and practitioners of Web-based instruction concerning the factors of transactional distance that need to be taken into account and overcome in WWW courses.
Extracted statement — Has approach: Moore's Theory of Transactional Distance
Research field: Life Sciences
Title: A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences
Abstract: Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.
Extracted statement — method: FAIR balloon plot
Research field: Life Sciences
Title: A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences
Abstract: Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.
Extracted statement — Domain: Life Sciences
Research field: Machine Learning
Title: A Fast and Accurate Dependency Parser using Neural Networks
Abstract: Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2% improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.
Extracted statement — uses: a small number of dense features
Research field: Machine Learning
Title: OWL2Vec*: Embedding of OWL Ontologies
Abstract: Abstract Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named , which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, often significantly outperforms the state-of-the-art methods in our experiments.
Extracted statement — Has evaluation task: Class subsumption prediction
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: Differential Evolution
Research field: Machine Learning
Title: Onto2vec: joint vector-based representation of biological entities and their ontology-based annotations
Abstract: Motivation Biological knowledge is widely represented in the form of ontology‐based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology‐based annotations are frequently used as features in machine learning applications. Results We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity‐based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec‐generated feature vectors can significantly improve prediction of protein‐protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data‐driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state‐of‐the‐art in several predictive applications in which ontologies are involved. Availability and implementation https://github.com/bio‐ontology‐research‐group/onto2vec
Extracted statement — Uses dataset: Gene Ontology (GO)
Research field: Machine Learning
Title: On2vec: Embedding-based relation prediction for ontology population
Abstract: Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.
Extracted statement — Has method : Hierarchy Model
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: hyperparameter optimization
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: Image Classification
Research field: Machine Learning
Title: node2vec: Scalable Feature Learning for Networks
Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Extracted statement — Has evaluation task: Link prediction
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: Machine Learning
Research field: Machine Learning
Title: node2vec: Scalable Feature Learning for Networks
Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Extracted statement — Has evaluation task: multi-label classification
Research field: Machine Learning
Title: Bio-joie: Joint representation learning of biological knowledge bases
Abstract: The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene on-tology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease.
Extracted statement — Has evaluation task: PPI type prediction
Research field: Machine Learning
Title: Generating Typed Dependency Parses from Phrase Structure Parses
Abstract: This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar and the Link parser. The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.
Extracted statement — Other resources: the Stanford parser