CHALLENGE OF RUBBER/GRAPHENE COMPOSITES AIMING AT REAL APPLICATIONS

2017 ◽  
Vol 90 (2) ◽  
pp. 225-237 ◽  
Author(s):  
Zhijun Yang ◽  
Baochun Guo ◽  
Liqun Zhang
Keyword(s):  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Rubber Composites ◽  
The Status

ABSTRACT Graphene has attracted a great deal of interest in recent years, illustrated by its potential in a variety of areas in physics, chemistry, and engineering. Specifically, graphene has opened up exciting possibilities for high-performance and functional rubber composites. Although copious literature deals with the fascinating properties related to graphene, its real (large scale) applications in rubber-based composites have not been approached. We discuss the state of the art in development in processing and the status in understanding of structure/performance relationships. Accordingly, the prospectives and challenges of some real applications of graphene-based rubber composites such as tires and sensors are surveyed and discussed.

scholarly journals Text Rewriting Improves Semantic Role Labeling

2014 ◽  
Vol 51 ◽  
pp. 133-164 ◽  
Author(s):  
K. Woodsend ◽  
M. Lapata
Keyword(s):  
Gold Standard ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Comparable Corpora ◽  
Rewrite Rules ◽  
Model Training

Large-scale annotated corpora are a prerequisite to developing high-performance NLP systems. Such corpora are expensive to produce, limited in size, often demanding linguistic expertise. In this paper we use text rewriting as a means of increasing the amount of labeled data available for model training. Our method uses automatically extracted rewrite rules from comparable corpora and bitexts to generate multiple versions of sentences annotated with gold standard labels. We apply this idea to semantic role labeling and show that a model trained on rewritten data outperforms the state of the art on the CoNLL-2009 benchmark dataset.

scholarly journals Text Rewriting Improves Semantic Role Labeling (Extended Abstract)

2017 ◽  
Author(s):  
Kristian Woodsend ◽  
Mirella Lapata
Keyword(s):  
Gold Standard ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Comparable Corpora ◽  
Rewrite Rules ◽  
Model Training

Large-scale annotated corpora are a prerequisite to developing high-performance NLP systems. Such corpora are expensive to produce, limited in size, often demanding linguistic expertise. In this paper we use text rewriting as a means of increasing the amount of labeled data available for model training. Our method uses automatically extracted rewrite rules from comparable corpora and bitexts to generate multiple versions of sentences annotated with gold standard labels. We apply this idea to semantic role labeling and show that a model trained on rewritten data outperforms the state of the art on the CoNLL-2009 benchmark dataset.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

Advancing the state of the art in high-performance logic and array technology

1992 ◽  
Vol 36 (5) ◽  
pp. 821-828 ◽  
Author(s):  
K. H. Brown ◽  
D. A. Grose ◽  
R. C. Lange ◽  
T. H. Ning ◽  
P. A. Totta
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The State ◽  
Array Technology

BISWSRBS: A Winograd-based CNN Accelerator with a Fine-grained Regular Sparsity Pattern and Mixed Precision Quantization

2021 ◽  
Vol 14 (4) ◽  
pp. 1-28
Author(s):  
Tao Yang ◽  
Zhezhi He ◽  
Tengchuan Kou ◽  
Qingzheng Li ◽  
Qi Han ◽  
...  
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The State ◽  
Optimization Approach ◽  
Quantization Scheme ◽  
Model Accuracy ◽  
Sparsity Pattern ◽  
Computing Platform ◽  
Energy Efficiency Improvement ◽  
Mixed Precision

Field-programmable Gate Array (FPGA) is a high-performance computing platform for Convolution Neural Networks (CNNs) inference. Winograd algorithm, weight pruning, and quantization are widely adopted to reduce the storage and arithmetic overhead of CNNs on FPGAs. Recent studies strive to prune the weights in the Winograd domain, however, resulting in irregular sparse patterns and leading to low parallelism and reduced utilization of resources. Besides, there are few works to discuss a suitable quantization scheme for Winograd. In this article, we propose a regular sparse pruning pattern in the Winograd-based CNN, namely, Sub-row-balanced Sparsity (SRBS) pattern, to overcome the challenge of the irregular sparse pattern. Then, we develop a two-step hardware co-optimization approach to improve the model accuracy using the SRBS pattern. Based on the pruned model, we implement a mixed precision quantization to further reduce the computational complexity of bit operations. Finally, we design an FPGA accelerator that takes both the advantage of the SRBS pattern to eliminate low-parallelism computation and the irregular memory accesses, as well as the mixed precision quantization to get a layer-wise bit width. Experimental results on VGG16/VGG-nagadomi with CIFAR-10 and ResNet-18/34/50 with ImageNet show up to 11.8×/8.67× and 8.17×/8.31×/10.6× speedup, 12.74×/9.19× and 8.75×/8.81×/11.1× energy efficiency improvement, respectively, compared with the state-of-the-art dense Winograd accelerator [20] with negligible loss of model accuracy. We also show that our design has 4.11× speedup compared with the state-of-the-art sparse Winograd accelerator [19] on VGG16.

scholarly journals Whom is political science for?

2001 ◽  
Vol 1 ◽  
pp. 4-8
Author(s):  
Andrea Theocharis ◽  
Marcus Graetsch
Keyword(s):  
Everyday Life ◽  
Political Science ◽  
State Of The Art ◽  
Status Quo ◽  
The State ◽  
Research And Teaching ◽  
The Everyday ◽  
The Status ◽  
Internet Discussion

We all study political science, but - what do we actually do here anyway? This essay expresses our thoughts about our subject. The everyday life in University doesn’t seem to give enough space for questioning what is this all about. Maybe a debate on that issue does not exist extensively because of fears of the loss of entitlement. The aim of this essay is to support the heightening of student’s awareness about the status quo of research and teaching in political science as we can judge it from our modest experiences. Trying to get to the basis of such a problem is not easy. The things here written are surely not the state of the art, but they could shine a better light on the problem what had been called the 'politics of political science' in an earlier Internet discussion on the IAPSS website. This paper should be understood as a start for a discussion, where we all can express our surely different experiences and ideas.

A Comprehensive Taxonomy of Dynamic Texture Representation

2023 ◽  
Vol 55 (1) ◽  
pp. 1-39
Author(s):  
Thanh Tuan Nguyen ◽  
Thanh Phuong Nguyen
Keyword(s):  
Large Scale ◽  
Environmental Changes ◽  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Research Activities ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Negative Impacts ◽  
Made In

Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

Precise No-Reference Image Quality Evaluation Based on Distortion Identification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Chenggang Yan ◽  
Tong Teng ◽  
Yutao Liu ◽  
Yongbing Zhang ◽  
Haoqian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Gaussian White Noise ◽  
The State ◽  
Reference Image

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.

Analysis of IT Governance on Spanish Organizations

2010 ◽  
pp. 439-455
Author(s):  
Alberto J. Arroyo ◽  
José D. Carrillo Verdún
Keyword(s):  
Corporate Governance ◽  
State Of The Art ◽  
It Governance ◽  
The State ◽  
Maximum Value ◽  
The Status

Corporate governance is a key element today in organizations and companies. IT Governance, as a part of corporate governance, plays its role in aligning IT with the business and obtaining the maximum value, minimizing the risks. Several frameworks and guidelines have been published in order to set the basis for this discipline. The recent release of the ISO 38500 (ISO 2008) ads an effort to standardize the different elements of IT governance. Despite these efforts, none of the different frameworks or guidelines is focused on the specific characteristics of small and medium companies (SMOs), although the authors consider that their conclusions are universal. Furthermore, there is no research so far that analyzed the status of IT governance in Spanish organizations. The aim of this work is to do a research to identify the state of the art of IT governance in the Spanish small and medium organizations.

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