A Novel Method for Reducing Overhead of Training Sentiment Analysis Network

Abstract Sentiment analysis based on statistics has rapidly developed in deep-learning. Bilateral attention neural network (BANN), especially Bidirectional Encoder Representations from Transformers (BERT), has reached high accuracy. However, with the increase of network depth and large-scale corpus, the computational overhead of BANN increases geometrically. How to reduce training corpus scale has correspondingly become an important research focus. This paper proposes a reduced corpus scale method called Concept-BERT, which consists of the following steps: firstly, using Formal Concept Analysis (FCA), Concept-BERT mines the association rules among corpus and reduces corpus attributes, and hence reducing corpus scale; secondly, reduced-corpus is inputed to BERT and the result is obtained; finally, the attention of Concept-BERT is analyzed. Concept-BERT is experimented for sentiment analysis on CoLA, SST-2, Dianping and Blogsenti, and its accuracy reaches 81.1, 92.9, 77.9 and 86.7 respectively. Our experimental results show that the proposed method has the same accuracy as BERT, using low-scale corpus and low overhead, and low-scale corpus doesn't affect model attention.

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Enzymatic Synthesis of 15N-L-aspartic Acid Using Recombinant Aspartase from Escherichia Coli K12

Revista de Chimie ◽

10.37358/rc.08.11.2004 ◽

2008 ◽

Vol 59 (11) ◽

Cited By ~ 1

Author(s):

Iulia Lupan ◽

Sergiu Chira ◽

Maria Chiriac ◽

Nicolae Palibroda ◽

Octavian Popescu

Keyword(s):

Amino Acids ◽

Aspartic Acid ◽

Enzymatic Synthesis ◽

Large Scale ◽

Recombinant Dna ◽

Industrial Enzymes ◽

Recombinant Dna Technology ◽

Bacterial Fermentation ◽

Natural Protein ◽

Novel Method

Amino acids are obtained by bacterial fermentation, extraction from natural protein or enzymatic synthesis from specific substrates. With the introduction of recombinant DNA technology, it has become possible to apply more rational approaches to enzymatic synthesis of amino acids. Aspartase (L-aspartate ammonia-lyase) catalyzes the reversible deamination of L-aspartic acid to yield fumaric acid and ammonia. It is one of the most important industrial enzymes used to produce L-aspartic acid on a large scale. Here we described a novel method for [15N] L-aspartic synthesis from fumarate and ammonia (15NH4Cl) using a recombinant aspartase.

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A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽

10.3390/cancers13092111 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2111

Author(s):

Bo-Wei Zhao ◽

Zhu-Hong You ◽

Lun Hu ◽

Zhen-Hao Guo ◽

Lei Wang ◽

...

Keyword(s):

Drug Target ◽

Large Scale ◽

Computational Models ◽

Structural Information ◽

Characteristic Curve ◽

Representation Learning ◽

Graph Representation ◽

Convolutional Network ◽

Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

IEEE Transactions on Computational Social Systems ◽

10.1109/tcss.2021.3051189 ◽

2021 ◽

pp. 1-13

Author(s):

Usman Naseem ◽

Imran Razzak ◽

Matloob Khushi ◽

Peter W. Eklund ◽

Jinman Kim

Keyword(s):

Sentiment Analysis ◽

Large Scale ◽

Data Set ◽

Twitter Data

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A Novel Method Facilitating the Simple and Low-Cost Preparation of Human Osteochondral Slice Explants for Large-Scale Native Tissue Analysis

International Journal of Molecular Sciences ◽

10.3390/ijms22126394 ◽

2021 ◽

Vol 22 (12) ◽

pp. 6394

Author(s):

Jacob Spinnen ◽

Lennard K. Shopperly ◽

Carsten Rendenbach ◽

Anja A. Kühl ◽

Ufuk Sentürk ◽

...

Keyword(s):

Gene Expression ◽

Cell Viability ◽

Large Scale ◽

Marker Gene ◽

Cell Swelling ◽

Tissue Cell ◽

Joint Research ◽

Sample Number ◽

Tnf Α ◽

Novel Method

For in vitro modeling of human joints, osteochondral explants represent an acceptable compromise between conventional cell culture and animal models. However, the scarcity of native human joint tissue poses a challenge for experiments requiring high numbers of samples and makes the method rather unsuitable for toxicity analyses and dosing studies. To scale their application, we developed a novel method that allows the preparation of up to 100 explant cultures from a single human sample with a simple setup. Explants were cultured for 21 days, stimulated with TNF-α or TGF-β3, and analyzed for cell viability, gene expression and histological changes. Tissue cell viability remained stable at >90% for three weeks. Proteoglycan levels and gene expression of COL2A1, ACAN and COMP were maintained for 14 days before decreasing. TNF-α and TGF-β3 caused dose-dependent changes in cartilage marker gene expression as early as 7 days. Histologically, cultures under TNF-α stimulation showed a 32% reduction in proteoglycans, detachment of collagen fibers and cell swelling after 7 days. In conclusion, thin osteochondral slice cultures behaved analogously to conventional punch explants despite cell stress exerted during fabrication. In pharmacological testing, both the shorter diffusion distance and the lack of need for serum in the culture suggest a positive effect on sensitivity. The ease of fabrication and the scalability of the sample number make this manufacturing method a promising platform for large-scale preclinical testing in joint research.

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A Novel Method for the Preparation of Fibrous CeO2–ZrO2–Y2O3 Compacts for Thermochemical Cycles

Crystals ◽

10.3390/cryst11080885 ◽

2021 ◽

Vol 11 (8) ◽

pp. 885

Author(s):

Nicole Knoblauch ◽

Peter Mechnich

Keyword(s):

Large Scale ◽

Preparation Method ◽

Cost Effective ◽

High Porosity ◽

Thermochemical Cycles ◽

Redox Cycles ◽

Redox Kinetics ◽

Direct Infiltration ◽

Novel Method ◽

Functional Components

Zirconium-Yttrium-co-doped ceria (Ce0.85Zr0.13Y0.02O1.99) compacts consisting of fibers with diameters in the range of 8–10 µm have been successfully prepared by direct infiltration of commercial YSZ fibers with a cerium oxide matrix and subsequent sintering. The resulting chemically homogeneous fiber-compacts are sinter-resistant up to 1923 K and retain a high porosity of around 58 vol% and a permeability of 1.6–3.3 × 10−10 m² at a pressure gradient of 100–500 kPa. The fiber-compacts show a high potential for the application in thermochemical redox cycling due its fast redox kinetics. The first evaluation of redox kinetics shows that the relaxation time of oxidation is five times faster than that of dense samples of the same composition. The improved gas exchange due to the high porosity also allows higher reduction rates, which enable higher hydrogen yields in thermochemical water-splitting redox cycles. The presented cost-effective fiber-compact preparation method is considered very promising for manufacturing large-scale functional components for solar-thermal high-temperature reactors.

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Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

Remote Sensing ◽

10.3390/rs13020320 ◽

2021 ◽

Vol 13 (2) ◽

pp. 320

Author(s):

José P. Granadeiro ◽

João Belo ◽

Mohamed Henriques ◽

João Catalão ◽

Teresa Catry

Keyword(s):

Large Scale ◽

Exposure Period ◽

Intertidal Area ◽

Digital Elevation ◽

Geographical Bias ◽

Novel Method ◽

Tidal Stage ◽

Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

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A Scalable Redefined Stochastic Blockmodel

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442589 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-28

Author(s):

Xueyan Liu ◽

Bo Yang ◽

Hechang Chen ◽

Katarzyna Musial ◽

Hongxu Chen ◽

...

Keyword(s):

Large Scale ◽

Network Science ◽

Learning Algorithm ◽

State Of The Art ◽

Real World Data ◽

Computational Overhead ◽

Stochastic Blockmodel ◽

Np Hard Problem ◽

Large Scale Networks ◽

The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

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