scholarly journals Extracting fine-grained location with temporal awareness in tweets: A two-stage approach

2017 ◽  
Vol 68 (7) ◽  
pp. 1652-1670 ◽  
Author(s):  
Chenliang Li ◽  
Aixin Sun
Keyword(s):  
Two Stage ◽  
Fine Grained

scholarly journals Coarse2Fine: a two-stage training method for fine-grained visual classification

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Amir Erfan Eshratifar ◽  
David Eigen ◽  
Michael Gormish ◽  
Massoud Pedram
Keyword(s):  
Training Method ◽  
Two Stage ◽  
Visual Classification ◽  
Fine Grained

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Two-stage local constrained sparse coding for fine-grained visual categorization

2017 ◽  
Vol 61 (1) ◽  
Author(s):  
Lihua Guo ◽  
Chenggang Guo ◽  
Lei Li ◽  
Qinghua Huang ◽  
Yanshan Li ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Two Stage ◽  
Visual Categorization ◽  
Fine Grained

scholarly journals Espeleogênese em quartzitos da Serra do Ibitipoca, sudeste de Minas Gerais

1997 ◽  
Vol 20 ◽  
pp. 75-87
Author(s):  
Atlas V. Corrêa Neto ◽  
João Baptista Filho
Keyword(s):  
Minas Gerais ◽  
Two Stage ◽  
Drainage Systems ◽  
Fine Grained ◽  
Silica Solution ◽  
Stage Process

The caves built in quartzites at the Ibitipoca Range are part of hierarquized underground drainage systems. Spelcogencsis was a two-stage process, following the sanding/piping model. In the first stage, porosity was generated by feldspar and phyllosilicates alteration and silica solution from quartz. The essential conditions for cave development were: (1) a large diference between local and regional base levels; (2) presence of rock layers specially susceptible to sanding and piping processes (fine-grained micaceous quartzite) and (3) a sequence of cycles of stability and uplift Different cave patterns and sizes can be explained by changes in one or more of the above conditionants.

scholarly journals Two-stage agglomeration of fine-grained herbal nettle waste

2017 ◽  
Vol 31 (4) ◽  
pp. 515-523 ◽  
Author(s):  
Sławomir Obidziński ◽  
Magdalena Joka ◽  
Olga Fijoł
Keyword(s):  
Particle Size ◽  
Moisture Content ◽  
Potato Starch ◽  
Process Temperature ◽  
Starch Solution ◽  
Two Stage ◽  
Fine Grained ◽  
Pellet Density ◽  
Binder Material

Abstract This paper compares the densification work necessary for the pressure agglomeration of fine-grained dusty nettle waste, with the densification work involved in two-stage agglomeration of the same material. In the first stage, the material was pre-densified through coating with a binder material in the form of a 5% potato starch solution, and then subjected to pressure agglomeration. A number of tests were conducted to determine the effect of the moisture content in the nettle waste (15, 18 and 21%), as well as the process temperature (50, 70, 90°C) on the values of densification work and the density of the obtained pellets. For pre-densified pellets from a mixture of nettle waste and a starch solution, the conducted tests determined the effect of pellet particle size (1, 2, and 3 mm) and the process temperature (50, 70, 90°C) on the same values. On the basis of the tests, we concluded that the introduction of a binder material and the use of two-stage agglomeration in nettle waste densification resulted in increased densification work (as compared to the densification of nettle waste alone) and increased pellet density.

Two-Stage Sintering of Nb2O5 Doped Zirconia Toughened Alumina (ZTA) Composites

2020 ◽  
Vol 861 ◽  
pp. 327-333
Author(s):  
Teow Hsien Loong ◽  
Se Yong Eh Noum ◽  
Wong Wai Mun
Keyword(s):  
Mechanical Properties ◽  
Hip Replacement ◽  
Niobium Oxide ◽  
Sintering Temperature ◽  
Well Being ◽  
Two Stage ◽  
Total Hip ◽  
Fine Grained ◽  
Second Stage ◽  
Zirconia Toughened Alumina

It is estimated that 130 million people will suffer from osteoarthritis by 2050 which require patient to undergo a surgical procedure known as total hip replacement which has lifespan of 20 years and failure rates of ~1%. This research would highlight the effects of doping Niobium Oxide (Nb2O5) between 0 vol % to 0.8 vol % into Zirconia-Toughened Alumina (ZTA) composites which is the main biomaterials used to manufacture total hip arthroplasty. The samples were sintered using two-stage sintering (TSS) between 1400°C and 1550°C for first-stage sintering temperature at heating rate of 20°C/min. At second stage, the samples were sintered at 1350°C and hold for 12 hours. It was found that TSS combined with addition of Nb2O5 as dopants were beneficial in producing fine-grained ZTA composites with improved mechanical properties compared to undoped ZTA composites produced via TSS. Compared to undoped ZTA composites, samples doped with Nb2O5 and sintered at T1 ≥1400°C were fully densed (>98%), achieved Vickers hardness more than 20 GPa and Young’s modulus higher than 410 GPa and at the same time fracture toughness of more than 8 MPam1/2. Based on the findings, production of ZTA composites with enhanced mechanical properties with longer lifespan is possible which is beneficial in ensuring the well-being of osteoarthritis patients.

scholarly journals StackDRL: Stacked Deep Reinforcement Learning for Fine-grained Visual Categorization

2018 ◽  
Author(s):  
Xiangteng He ◽  
Yuxin Peng ◽  
Junjie Zhao
Keyword(s):  
Reinforcement Learning ◽  
Visual Information ◽  
Experimental Validation ◽  
State Of The Art ◽  
Two Stage ◽  
Visual Categorization ◽  
Fine Grained ◽  
Reward Function ◽  
Main Challenge ◽  
Labor Consumption

Fine-grained visual categorization (FGVC) is the discrimination of similar subcategories, whose main challenge is to localize the quite subtle visual distinctions between similar subcategories. There are two pivotal problems: discovering which region is discriminative and representative, and determining how many discriminative regions are necessary to achieve the best performance. Existing methods generally solve these two problems relying on the prior knowledge or experimental validation, which extremely restricts the usability and scalability of FGVC. To address the "which" and "how many" problems adaptively and intelligently, this paper proposes a stacked deep reinforcement learning approach (StackDRL). It adopts a two-stage learning architecture, which is driven by the semantic reward function. Two-stage learning localizes the object and its parts in sequence ("which"), and determines the number of discriminative regions adaptively ("how many"), which is quite appealing in FGVC. Semantic reward function drives StackDRL to fully learn the discriminative and conceptual visual information, via jointly combining the attention-based reward and category-based reward. Furthermore, unsupervised discriminative localization avoids the heavy labor consumption of labeling, and extremely strengthens the usability and scalability of our StackDRL approach. Comparing with ten state-of-the-art methods on CUB-200-2011 dataset, our StackDRL approach achieves the best categorization accuracy.

Efficient Utilization of Copper Sulfide Ore in Chile by Flotation

2012 ◽  
Vol 524-527 ◽  
pp. 975-982
Author(s):  
Jiu Shuai Deng ◽  
Shu Ming Wen ◽  
Yong Jun Xian ◽  
Shao Jun Bai ◽  
Dan Liu ◽  
...  
Keyword(s):  
Copper Sulfide ◽  
Copper Recovery ◽  
Coarse Grained ◽  
Efficient Utilization ◽  
Two Stage ◽  
Fine Grained ◽  
Sulfide Ore ◽  
Concentrate Grade ◽  
Copper Sulfide Ore ◽  
The Ideal

The valuable minerals of a copper sulfide ore in Chile, mainly consist of chalcopyrite and chalcocite as well as contain small amounts of cuprite. Gangue minerals are mainly chlorite, quartz, calcite, arsenopyrite, etc. Most are coarse-grained disseminated minerals, while some are fine-grained disseminated minerals. Flotation has been used for processing these ores. The grinding fineness, lime dosage, collector type and dosage all have influences on flotation results. In the present work, the best condition of grinding fineness, lime, and collector dosage was used to separate the copper sulfide and other minerals in the ores. The results showed that, in the grinding fineness of −74 μm accounting for 80%, the vast majority of copper minerals were to monomer dissociation. The flowsheet of one stage roughing, two stage scavenging, and two stage cleaning was adopted. The ideal results of 27.58% and 96.89% for the copper concentrate grade and copper recovery, respectively, were obtained.

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