scholarly journals Using Wikidata and Metaphactory to Underpin an Integrated Flora of Canada

2019 ◽  
Vol 3 ◽  
Author(s):  
Joel Sachs ◽  
Jocelyn Pender ◽  
Beatriz Lujan-Toro ◽  
James Macklin ◽  
Peter Haase ◽  
...  
Keyword(s):  
North America ◽  
Language Processing ◽  
Sequence Data ◽  
Strong Support ◽  
Character Trait ◽  
Coarse Grained ◽  
Morphological Properties ◽  
Faceted Search ◽  
Fine Grained ◽  
Floristic Data

We are using Wikidata and Metaphactory to build an Integrated Flora of Canada (IFC). IFC will be integrated in two senses: First, it will draw on multiple existing flora (e.g. Flora of North America, Flora of Manitoba, etc.) for content. Second, it will be a portal to related resources such as annotations, specimens, literature, and sequence data. Background We had success using Semantic Media Wiki (SMW) as the platform for an on-line representation of the Flora of North America (FNA). We used Charaparser (Cui 2012) to extract plant structures (e.g. “stem”), characters (e.g. “external texture”), and character values (e.g. “glabrous”) from the semi-structured FNA treatments. We then loaded this data into SMW, which allows us to query for taxa based on their character traits, and enables a broad range of exploratory analysis, both for purposes of hypothesis generation, and also to provide support for or against specific scientific hypotheses. Migrating to Wikidata/Wikibase We decided to explore a migration from SMW to Wikibase for three main reasons: simplified workflow; triple level provenance; and sustainability. Simplified workflow: Our workflow for our FNA-based portal includes Natural Language Processing (NLP) of coarse-grained XML to get the fine-grained XML, transforming this XML for input into SMW, and a custom SMW skin for displaying the data. We consider the coarse-grained XML to be canonical. When it changes (because we find an error, or we improve our NLP), we have to re-run the transformation, and re-load the data, which is time-consuming. Ideally, our presentation would be based on API calls to the data itself, eliminating the need to transform and re-load after every change. Provenance: Wikidata's provenance model supports having multiple, conflicting assertions for the same character trait, which is something that inevitably happens when floristic data is integrated. Sustainability: Wikidata has strong support from the Wikimedia Foundation, while SMW is increasingly seen as a legacy system. Wikibase vs. Wikidata Wikidata, however, is not a suitable home for the Integrated Flora of Canada. It is built upon a relatively small number of community curated properties, while we have ~4500 properties for the Asteraceae family alone. The model we want to pursue is to use Wikidata for a small group of core properties (e.g. accepted name, parent taxon, etc.), and to use our own instance of Wikibase for the much larger number of specialized morphological properties (e.g. adaxial leaf colour, leaf external texture, etc.) Essentially, we will be running our own Wikidata, over which we would exercise full control. Miller (2018) decribes deploying this curation model in another domain. Metaphactory Metaphactory is a suite of middleware and front-end interfaces for authoring, managing, and querying knowledge graphs, including mechanisms for faceted search and geospatial visualizations. It is also the software (together with Blazegraph) behind the Wikidata Query Service. Metaphactory provides us with a SPARQL endpoint; a templating mechanism that allows each taxonomic treatment to be rendered via a collection of SPARQL queries; reasoning capabilities (via an underlying graph database) that permit the organization of over 42,000 morphological properties; and a variety of search and discovery tools. There are a number of ways in which Wikidata and Metaphactory can work together, and we are still exploring questions such as: Will provenance be managed via named graphs, or via the Wikidata snak model?; How will data flow between the two platforms? Etc. We will report on our findings to date, and invite collaboration with related Wikimedia-based projects.

scholarly journals Innovative Deep Neural Network Modeling for Fine-Grained Chinese Entity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1001 ◽  
Author(s):  
Jingang Liu ◽  
Chunhe Xia ◽  
Haihua Yan ◽  
Wenjing Xu
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Training Model ◽  
Entity Recognition ◽  
Coarse Grained ◽  
Neural Network Modeling ◽  
Fine Grained ◽  
Named Entity

Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks.

scholarly journals Template-controlled mineralization: Determining film granularity and structure by surface functionality patterns

2015 ◽  
Vol 6 ◽  
pp. 1763-1768 ◽  
Author(s):  
Nina J Blumenstein ◽  
Jonathan Berson ◽  
Stefan Walheim ◽  
Petia Atanasova ◽  
Johannes Baier ◽  
...  
Keyword(s):  
Coarse Grained ◽  
Morphological Properties ◽  
Self Assembled Monolayers ◽  
Zinc Oxide Film ◽  
Surface Adhesion ◽  
Fine Grained ◽  
Surface Functionality ◽  
Surface Areas ◽  
Self Assembling ◽  
Assembled Monolayers

We present a promising first example towards controlling the properties of a self-assembling mineral film by means of the functionality and polarity of a substrate template. In the presented case, a zinc oxide film is deposited by chemical bath deposition on a nearly topography-free template structure composed of a pattern of two self-assembled monolayers with different chemical functionality. We demonstrate the template-modulated morphological properties of the growing film, as the surface functionality dictates the granularity of the growing film. This, in turn, is a key property influencing other film properties such as conductivity, piezoelectric activity and the mechanical properties. A very pronounced contrast is observed between areas with an underlying fluorinated, low energy template surface, showing a much more (almost two orders of magnitude) coarse-grained film with a typical agglomerate size of around 75 nm. In contrast, amino-functionalized surface areas induce the growth of a very smooth, fine-grained surface with a roughness of around 1 nm. The observed influence of the template on the resulting clear contrast in morphology of the growing film could be explained by a contrast in surface adhesion energies and surface diffusion rates of the nanoparticles, which nucleate in solution and subsequently deposit on the functionalized substrate.

scholarly journals The FGLOCTweet Corpus: An English tweet-based corpus for fine-grained location-detection tasks

2022 ◽  
Vol 10 (1) ◽  
pp. 117-133
Author(s):  
Nicolás José Fernández-Martínez
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Situation Awareness ◽  
Spatial Information ◽  
Emergency Situation ◽  
Coarse Grained ◽  
Text Data ◽  
Emergency Responders ◽  
Fine Grained ◽  
Location Detection

Location detection in social-media microtexts is an important natural language processing task for emergency-based contexts where locative references are identified in text data. Spatial information obtained from texts is essential to understand where an incident happened, where people are in need of help and/or which areas have been affected. This information contributes to raising emergency situation awareness, which is then passed on to emergency responders and competent authorities to act as quickly as possible. Annotated text data are necessary for building and evaluating location-detection systems. The problem is that available corpora of tweets for location-detection tasks are either lacking or, at best, annotated with coarse-grained location types (e.g. cities, towns, countries, some buildings, etc.). To bridge this gap, we present our semi-automatically annotated corpus, the Fine-Grained LOCation Tweet Corpus (FGLOCTweet Corpus), an English tweet-based corpus for fine-grained location-detection tasks, including fine-grained locative references (i.e. geopolitical entities, natural landforms, points of interest and traffic ways) together with their surrounding locative markers (i.e. direction, distance, movement or time). It includes annotated tweet data for training and evaluation purposes, which can be used to advance research in location detection, as well as in the study of the linguistic representation of place or of the microtext genre of social media.

Correlation between dislocation, grain boundary and interface of duplex SS in stress corrosion cracking(SCC)

1990 ◽  
Vol 48 (4) ◽  
pp. 928-929
Author(s):  
Wang Zheng-fang ◽  
Z.F. Wang
Keyword(s):  
Stainless Steel ◽  
Thermal Cycle ◽  
Secondary Phase ◽  
Corrosion Cracking ◽  
Coarse Grained ◽  
Test Environment ◽  
Fine Grained ◽  
Evaluation Test ◽  
Welding Thermal Cycle ◽  
Micro Analysis

The main purpose of this study highlights on the evaluation of chloride SCC resistance of the material,duplex stainless steel,OOCr18Ni5Mo3Si2 (18-5Mo) and its welded coarse grained zone(CGZ).18-5Mo is a dual phases (A+F) stainless steel with yield strength:512N/mm2 .The proportion of secondary Phase(A phase) accounts for 30-35% of the total with fine grained and homogeneously distributed A and F phases(Fig.1).After being welded by a specific welding thermal cycle to the material,i.e. Tmax=1350°C and t8/5=20s,microstructure may change from fine grained morphology to coarse grained morphology and from homogeneously distributed of A phase to a concentration of A phase(Fig.2).Meanwhile,the proportion of A phase reduced from 35% to 5-10°o.For this reason it is known as welded coarse grained zone(CGZ).In association with difference of microstructure between base metal and welded CGZ,so chloride SCC resistance also differ from each other.Test procedures:Constant load tensile test(CLTT) were performed for recording Esce-t curve by which corrosion cracking growth can be described, tf,fractured time,can also be recorded by the test which is taken as a electrochemical behavior and mechanical property for SCC resistance evaluation. Test environment:143°C boiling 42%MgCl2 solution is used.Besides, micro analysis were conducted with light microscopy(LM),SEM,TEM,and Auger energy spectrum(AES) so as to reveal the correlation between the data generated by the CLTT results and micro analysis.

scholarly journals Balanced Sparsity for Efficient DNN Inference on GPU

2019 ◽  
Vol 33 ◽  
pp. 5676-5683 ◽  
Author(s):  
Zhuliang Yao ◽  
Shijie Cao ◽  
Wencong Xiao ◽  
Chen Zhang ◽  
Lanshun Nie
Keyword(s):  
Deep Neural Networks ◽  
General Purpose ◽  
Coarse Grained ◽  
Efficient Computation ◽  
Model Accuracy ◽  
Sparse Model ◽  
Model Inference ◽  
Fine Grained ◽  
Practical Inference ◽  
Speed Up

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity.

scholarly journals Submarine lava deltas of the 2018 eruption of Kīlauea volcano

2021 ◽  
Vol 83 (4) ◽  
Author(s):  
S. Adam Soule ◽  
Michael Zoeller ◽  
Carolyn Parcheta
Keyword(s):  
Grain Size ◽  
Pore Pressure ◽  
Mechanistic Model ◽  
Large Fraction ◽  
Volcanic Hazards ◽  
Lava Flows ◽  
Coarse Grained ◽  
Fine Grained ◽  
Fine Grain ◽  
Delta Formation

AbstractHawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.

scholarly journals A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Fine Grained ◽  
Ddos Attack ◽  
Network Status ◽  
Ddos Attack Detection

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

scholarly journals Analysis Methods in Neural Language Processing: A Survey

2019 ◽  
Vol 7 ◽  
pp. 49-72 ◽  
Author(s):  
Yonatan Belinkov ◽  
James Glass
Keyword(s):  
Language Processing ◽  
Network Models ◽  
Research Trends ◽  
Neural Network Models ◽  
Fine Grained ◽  
Survey Paper ◽  
Review Analysis ◽  
Analysis Methods ◽  
New Models ◽  
Future Work

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

Rheology of hydrocarbon gels

1950 ◽  
Vol 200 (1061) ◽  
pp. 183-188 ◽  
Keyword(s):  
Mechanical Properties ◽  
Experimental Measurement ◽  
Theoretical Basis ◽  
Continuous Media ◽  
Point Of View ◽  
Coarse Grained ◽  
Testing Instrument ◽  
Fine Grained ◽  
Mineral Oils ◽  
Space And Time

Hydrocarbon gels contain a number of materials, such as rubber, greases, saponified mineral oils, etc., of great interest for various engineering purposes. Specific requirements in mechanical properties have been met by producing gels in appropriately chosen patterns of constituent components of visible, colloidal, molecular and atomic sizes, ranging from coarse-grained aggregates, represented by sponges, foams, emulsions, etc.; to fine-grained and apparently homogeneous ones, represented by optically clear compounds. The engineer who has to deal with the whole range of such materials will adopt a macroscopic point of view, based on an apparent continuity of all the material structures and of the distributions in space and time of the displacements and forces occurring under mechanical actions. It has been possible to determine these distributions in the framework of a comprehensive scheme in which the fundamental principles of the mechanics of continuous media provide the theoretical basis, and a testing instrument of new design, termed Rheogoniometer, the means of experimental measurement (Weissenberg 1931, 1934, 1946, 1947, 1948).

Microstructural Refinement in a Commercial Aluminum Alloy Processed by ECAP Using a Die Channel Angle of 110 Degrees

2015 ◽  
Vol 1114 ◽  
pp. 3-8
Author(s):  
Nicolae Şerban ◽  
Doina Răducanu ◽  
Nicolae Ghiban ◽  
Vasile Dănuţ Cojocaru
Keyword(s):  
Grain Refinement ◽  
Cross Section ◽  
High Strength ◽  
Secondary Phase ◽  
Coarse Grained ◽  
Metallographic Analysis ◽  
Second Phase ◽  
Channel Angle ◽  
Ambient Temperatures ◽  
Fine Grained

The properties of ultra-fine grained materials are superior to those of corresponding conventional coarse grained materials, being significantly improved as a result of grain refinement. Equal channel angular pressing (ECAP) is an efficient method for modifying the microstructure by refining grain size via severe plastic deformation (SPD) in producing ultra-fine grained materials (UFG) and nanomaterials (NM). The grain sizes produced by ECAP processing are typically in the submicrometer range and this leads to high strength at ambient temperatures. ECAP is performed by pressing test samples through a die containing two channels, equal in cross-section and intersecting at a certain angle. The billet experiences simple shear deformation at the intersection, without any precipitous change in the cross-section area because the die prevents lateral expansion and therefore the billet can be pressed more than once and it can be rotated around its pressing axis during subsequent passes. After ECAP significant grain refinement occurs together with dislocation strengthening, resulting in a considerable enhancement in the strength of the alloys. A commercial AlMgSi alloy (AA6063) was investigated in this study. The specimens were processed for a number of passes up to nine, using a die channel angle of 110°, applying the ECAP route BC. After ECAP, samples were cut from each specimen and prepared for metallographic analysis. The microstructure of the ECAP-ed and as-received material was investigated using optical (OLYMPUS – BX60M) and SEM microscopy (TESCAN VEGA II – XMU). It was determined that for the as-received material the microstructure shows a rough appearance, with large grains of dendritic or seaweed aspect and with a secondary phase at grain boundaries (continuous casting structure). For the ECAP processed samples, the microstructure shows a finished aspect, with refined, elongated grains, also with crumbled and uniformly distributed second phase particles after a typical ECAP texture.

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