doi: 10.48448/14fj-n857
Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM — an MDS model that extends an encoder-decoder architecture to incorporate a heterogeneous graph to repre- sent different semantic units (e.g., words and sentences) of the documents. This contrasts with existing MDS models which do not consider different edge types of graphs and as such do not capture the diversity of relationships in the documents. To preserve only key information and relationships of the documents in the heterogeneous graph, HGSUM uses graph pooling to compress the input graph. And to guide HGSUM to learn the compression, we introduce an additional objective that maximizes the similarity between the compressed graph and the graph constructed from the ground-truth summary during training. HGSUM is trained end-to-end with graph similarity and standard cross-entropy objectives. Experimental results over MULTI-NEWS, WCEP-100, and ARXIV show that HGSUM outperforms state-of-the-art MDS models. The code for our model and experiments is available at: https://github.com/oaimli/HGSum.
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3D RI reconstruction of a multicellular MCF-7 spheroid using Conventional Rytov and pMSS-Rytov.
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doi: 10.5446/61116
OGC-client ist eine einfach zu verwendende und leichte Bibliothek, die für die Interaktion mit OGC-Diensten konzipiert wurde. Unterstützungen für neue Protokolle werden laufend implementiert, um mit Neuentwicklungen der OGC-API Schritt zu halten! OGC-client ist eine einfach zu verwendende und leichte Bibliothek, die für die Interaktion mit OGC-Diensten konzipiert wurde. Sie ist einfach zu benutzen und ist eine strukturierte Möglichkeit OGC-Protokolle zu bedienen. Unterstützungen für neue Protokolle werden laufend implementiert, um mit Neuentwicklungen der OGC-API Schritt zu halten! Wenn Sie eine Webanwendung erstellen, die OGC-Dienste jeglicher Art (Karten, Daten, Sensoren usw.) abfragen soll, könnte diese Bibliothek genau das Richtige für Sie sein! Kommen Sie zu diesem Lightning Talk, um mehr zu erfahren.
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citations | 0 | |
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doi: 10.48448/7cmm-ew22
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doi: 10.48512/xcv8460266
Feature 9 (whole feature) in Surface 1 of the northeast quadrant of Structure 101 (pithouse main chamber).
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doi: 10.48448/f3et-d765
The optical properties of materials are determined by the spatial arrangement of their building blocks and nanomechanical metamaterials can be actuated by temperature changes, electric and magnetic fields, light and ultrasound. This provides an opportunity for sensing and light modulation based on engineered giant thermo-optical, electro-optical, magneto-optical, nonlinear optical, acousto-optical effects. We will give an overview, focusing on recent breakthroughs such as the optical detection of thermal motion in nanomechanical metamaterials, magnetic field sensing based on metamaterial nanomechanics, electrogyration a million times stronger than in natural materials, the realization of an optically bistable device and the demonstration of asymmetric transmission at microwatt power levels.
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doi: 10.48448/9r8h-x531
Zero-shot event extraction (EE) methods infer richly structured event records from text, based only on a minimal user specification and no training examples, which enables flexibility in exploring and developing applications. Most event extraction research uses the Automatic Content Extraction (ACE) annotated dataset to evaluate supervised EE methods, but can it be used to evaluate zero-shot and other low-supervision EE? We describe ACE's event structures and identify significant ambiguities and issues in current evaluation practice, including (1) coreferent argument mentions, (2) conflicting argument head conventions, and (3) ignorance of modality and event class details. By sometimes mishandling these subtleties, current work may dramatically understate the actual performance of zero-shot and other low-supervision EE, considering up to 32% of correctly identified arguments and 25% of correctly ignored event mentions as false negatives. For each issue, we propose recommendations for future evaluations so the research community can better utilize ACE as an event evaluation resource.
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After learning that one stimulus predicts an outcome (e.g., an aqua-colored rectangle leads to shock) and a very similar stimulus predicts no outcome (e.g., a slightly greener rectangle leads to no shock), some participants generalize the predictive relationship on the basis of physical similarity to the predictive stimulus, while others generalize on the basis of the relational difference between the two stimuli (e.g., ���higher likelihood of shock for bluer stimuli���). To date, these individual differences in generalization rules have remained unexplored in associative learning. Here, we present evidence that a given individual simultaneously entertains belief in both ���similarity��� and ���relational��� rules, and generalizes using a mixture of these strategies. Using a ���mixture of experts��� modelling framework constrained by participants self-reported rule beliefs, we show that considering multiple rules predicts generalization gradients better than a single rule, and that generalization behavior is better described as switching between, rather than averaging over, different rules.
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doi: 10.5446/14615
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doi: 10.26300/1qnc-9635
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doi: 10.48448/14fj-n857
Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM — an MDS model that extends an encoder-decoder architecture to incorporate a heterogeneous graph to repre- sent different semantic units (e.g., words and sentences) of the documents. This contrasts with existing MDS models which do not consider different edge types of graphs and as such do not capture the diversity of relationships in the documents. To preserve only key information and relationships of the documents in the heterogeneous graph, HGSUM uses graph pooling to compress the input graph. And to guide HGSUM to learn the compression, we introduce an additional objective that maximizes the similarity between the compressed graph and the graph constructed from the ground-truth summary during training. HGSUM is trained end-to-end with graph similarity and standard cross-entropy objectives. Experimental results over MULTI-NEWS, WCEP-100, and ARXIV show that HGSUM outperforms state-of-the-art MDS models. The code for our model and experiments is available at: https://github.com/oaimli/HGSum.
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citations | 0 | |
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3D RI reconstruction of a multicellular MCF-7 spheroid using Conventional Rytov and pMSS-Rytov.
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doi: 10.5446/61116
OGC-client ist eine einfach zu verwendende und leichte Bibliothek, die für die Interaktion mit OGC-Diensten konzipiert wurde. Unterstützungen für neue Protokolle werden laufend implementiert, um mit Neuentwicklungen der OGC-API Schritt zu halten! OGC-client ist eine einfach zu verwendende und leichte Bibliothek, die für die Interaktion mit OGC-Diensten konzipiert wurde. Sie ist einfach zu benutzen und ist eine strukturierte Möglichkeit OGC-Protokolle zu bedienen. Unterstützungen für neue Protokolle werden laufend implementiert, um mit Neuentwicklungen der OGC-API Schritt zu halten! Wenn Sie eine Webanwendung erstellen, die OGC-Dienste jeglicher Art (Karten, Daten, Sensoren usw.) abfragen soll, könnte diese Bibliothek genau das Richtige für Sie sein! Kommen Sie zu diesem Lightning Talk, um mehr zu erfahren.
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doi: 10.48448/7cmm-ew22
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doi: 10.48512/xcv8460266
Feature 9 (whole feature) in Surface 1 of the northeast quadrant of Structure 101 (pithouse main chamber).
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doi: 10.48448/f3et-d765
The optical properties of materials are determined by the spatial arrangement of their building blocks and nanomechanical metamaterials can be actuated by temperature changes, electric and magnetic fields, light and ultrasound. This provides an opportunity for sensing and light modulation based on engineered giant thermo-optical, electro-optical, magneto-optical, nonlinear optical, acousto-optical effects. We will give an overview, focusing on recent breakthroughs such as the optical detection of thermal motion in nanomechanical metamaterials, magnetic field sensing based on metamaterial nanomechanics, electrogyration a million times stronger than in natural materials, the realization of an optically bistable device and the demonstration of asymmetric transmission at microwatt power levels.
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citations | 0 | |
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doi: 10.48448/9r8h-x531
Zero-shot event extraction (EE) methods infer richly structured event records from text, based only on a minimal user specification and no training examples, which enables flexibility in exploring and developing applications. Most event extraction research uses the Automatic Content Extraction (ACE) annotated dataset to evaluate supervised EE methods, but can it be used to evaluate zero-shot and other low-supervision EE? We describe ACE's event structures and identify significant ambiguities and issues in current evaluation practice, including (1) coreferent argument mentions, (2) conflicting argument head conventions, and (3) ignorance of modality and event class details. By sometimes mishandling these subtleties, current work may dramatically understate the actual performance of zero-shot and other low-supervision EE, considering up to 32% of correctly identified arguments and 25% of correctly ignored event mentions as false negatives. For each issue, we propose recommendations for future evaluations so the research community can better utilize ACE as an event evaluation resource.
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citations | 0 | |
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