Caged-electron States and Split-electron States in Endohedral Alkali C60

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

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Negotiations over Accounting Issues: The Congruency of Audit Partner and Chief Financial Officer Recalls

Auditing A Journal of Practice & Theory ◽

10.2308/aud.2005.24.s-1.171 ◽

2005 ◽

Vol 24 (s-1) ◽

pp. 171-193 ◽

Cited By ~ 24

Author(s):

Michael Gibbins ◽

Susan A. McCracken ◽

Steven E. Salterio

Keyword(s):

Negotiation Process ◽

Chief Financial Officers ◽

Chief Financial Officer ◽

Relative Importance ◽

Common Elements ◽

Contextual Features ◽

Large Numbers ◽

The Common ◽

High Level ◽

Two Sides

Much of what takes place in auditor-client management negotiations occurs in unobservable settings and normally does not result in publicly available archival records. Recent research has increasingly attempted to probe issues relating to accounting negotiations in part due to recent events in the financial world. In this paper, we compare recalls from the two sides of such negotiations, audit partners, and chief financial officers (CFOs), collected in two field questionnaires. We examine the congruency of the auditors' and the CFOs' negotiation recalls for all negotiation elements and features that were common across the two questionnaires (detailed analyses of the questionnaires are reported elsewhere). The results show largely congruent recall: only limited divergences in recall of common elements and features. Specifically, we show a high level of congruency across CFOs and audit partners in the type of issues negotiated, parties involved in resolving the issue, and the elements making up the negotiation process, including agreement on the relative importance of various common accounting contextual features. The analysis of the common accounting contextual features suggests that certain contextual features are consistently important across large numbers of negotiations, whether viewed from the audit partner's or the CFO's perspective, and hence may warrant future study. Finally, the comparative analysis allows us to identify certain common elements and contextual features that may influence both audit partners and CFOs to consider the accounting negotiation setting as mainly distributive (win-lose).

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Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

Applied Sciences ◽

10.3390/app11156975 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6975

Author(s):

Tao Zhang ◽

Lun He ◽

Xudong Li ◽

Guoqing Feng

Keyword(s):

Performance Improvement ◽

State Of The Art ◽

Error Rates ◽

Convolutional Network ◽

Convolutional Networks ◽

Sentence Level ◽

End To End ◽

High Level ◽

Improved Accuracy ◽

Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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France’s State of the Art Distributed Optical Fibre Sensors Qualified for the Monitoring of the French Underground Repository for High Level and Intermediate Level Long Lived Radioactive Wastes

Sensors ◽

10.3390/s17061377 ◽

2017 ◽

Vol 17 (6) ◽

pp. 1377 ◽

Cited By ~ 17

Author(s):

Sylvie Delepine-Lesoille ◽

Sylvain Girard ◽

Marcel Landolt ◽

Johan Bertrand ◽

Isabelle Planes ◽

...

Keyword(s):

Optical Fibre ◽

State Of The Art ◽

Radioactive Wastes ◽

Intermediate Level ◽

Optical Fibre Sensors ◽

High Level

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Ba-, Si- and vacancy-rich phlogopites from the talc-bearing sulfide ore deposit of La Creuse, Beaujolais, France

Mineralogical Magazine ◽

10.1180/mgm.2018.124 ◽

2018 ◽

Vol 82 (5) ◽

pp. 1187-1210

Author(s):

Marie-Lola Pascal ◽

Michel Fonteilles ◽

Véronique Tournis ◽

Benoît Baptiste ◽

Jean-Louis Robert ◽

...

Keyword(s):

Goodness Of Fit ◽

Monoclinic Space Group ◽

Excess Charge ◽

Formula Unit ◽

X Ray Diffraction ◽

Ore Deposit ◽

Sulfide Ore ◽

The Common ◽

The Stability ◽

High Level

ABSTRACTBa-rich and Si-rich phlogopites occur in the talc-bearing rocks of the La Creuse sulfide ore deposit in Beaujolais, France. They form a group of compositions completely separated from the common Al-rich phlogopites that occur in the surrounding talc-free metasiltites and metarhyolites, with higher Ba and Mg and lower Al contents. The Ba-rich phlogopites have a relatively narrow compositional range (0.24 to 0.80 Ba per formula unit, for 44 valencies) with high and constant Si (5.8 atoms per formula unit, apfu) and Mg + Fe (5.6 apfu), probably buffered by the presence of talc. Compared to low-Al phlogopites from talc-free rocks, the excess charge introduced by the BaK–1 substitution is compensated by interlayer vacancies. Such a high level of interlayer vacancy (0.56 pfu), related to the talc-producing metasomatic conditions, is essential for the stability of this special group of Ba-rich and Si-rich phlogopites.Single crystal X-ray diffraction analyses were performed. Ba-rich and Si-rich phlogopite is monoclinic, space group C2/m, (R = 5.31%) with a = 5.3185(5), b = 9.2136(9), c = 10.1349(11) Å and β = 100.131(11)°. The occupancies of Mg/Fe and K/Ba were refined exploring different vacancies. The solutions giving the best R factor (4.77%) and goodness-of-fit (1.06) are obtained with 15% < vacancy < 40% at the interlayer site.

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Saliency Detection by Multilevel Deep Pyramid Model

Journal of Sensors ◽

10.1155/2018/8249180 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Hai Wang ◽

Lei Dai ◽

Yingfeng Cai ◽

Long Chen ◽

Yong Zhang

Keyword(s):

Background Noise ◽

State Of The Art ◽

Saliency Detection ◽

Saliency Map ◽

Multiple Features ◽

Low Level ◽

Pyramid Model ◽

High Level ◽

Different Levels ◽

Better Than

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

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DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Sensors ◽

10.3390/s21206780 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6780

Author(s):

Zhitong Lai ◽

Rui Tian ◽

Zhiguo Wu ◽

Nannan Ding ◽

Linjian Sun ◽

...

Keyword(s):

Multiple Scales ◽

Feature Fusion ◽

State Of The Art ◽

Depth Estimation ◽

Multi Scale ◽

Pyramid Structure ◽

Benchmark Datasets ◽

The Common ◽

Monocular Depth ◽

Multiple Stages

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

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A Survey of Graphical Page Object Detection with Deep Neural Networks

10.20944/preprints202104.0739.v1 ◽

2021 ◽

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space

Complex & Intelligent Systems ◽

10.1007/s40747-021-00513-8 ◽

2021 ◽

Author(s):

Yunhong Gong ◽

Yanan Sun ◽

Dezhong Peng ◽

Peng Chen ◽

Zhongtai Yan ◽

...

Keyword(s):

Common Sense ◽

Expert Knowledge ◽

State Of The Art ◽

Data Set ◽

The Past ◽

Computed Tomography Images ◽

Intensive Use ◽

Hospital Resources ◽

The Common ◽

Ct Data

AbstractThe COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations

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From Driverless Dilemmas to More Practical Common-Sense Tests for Automated Vehicles

10.31234/osf.io/ypbve ◽

2020 ◽

Author(s):

Julian De Freitas ◽

Bryant Walker Smith ◽

Andrea Censi ◽

Luigi Di Lillo ◽

Sam E. Anthony ◽

...

Keyword(s):

Common Sense ◽

Autonomous Systems ◽

Driving Behavior ◽

Automated Vehicles ◽

Everyday Lives ◽

The Common ◽

High Level ◽

First Time ◽

Trolley Dilemmas ◽

Relevant Component

For the first time in history, automated vehicles (AVs) are being deployed in populated environments. This unprecedented transformation of our everyday lives demands a significant undertaking: endowing complex autonomous systems with ethically acceptable behavior. We outline how one prominent, ethically-relevant component of AVs—driving behavior—is inextricably linked to stakeholders in the technical, regulatory, and social spheres of the field. Whereas humans are presumed (rightly or wrongly) to have the ‘common sense’ to behave ethically in new driving situations beyond a standard driving test, AVs do not (and probably should not) enjoy this presumption. We examine, at a high level, how to test the common sense of an AV. We start by reviewing discussions of ‘driverless dilemmas’, adaptions of the traditional ‘trolley dilemmas’ of philosophy that have sparked discussion on AV ethics but have limited use to the technical and legal spheres. Then, we explain how to substantially change the premises and features of these dilemmas (while preserving their behavioral diagnostic spirit) in order to lay the foundations for a more practical and relevant framework that tests driving common sense as an integral part of road rules testing.

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