scholarly journals Bounding Uncertainty for Active Batch Selection

2019 ◽  
Vol 33 ◽  
pp. 5240-5247 ◽  
Author(s):  
Hanmo Wang ◽  
Runwu Zhou ◽  
Yi-Dong Shen
Keyword(s):  
Objective Function ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Batch Mode ◽  
Initial Stage ◽  
Special Cases ◽  
Testing Data ◽  
The Relationship ◽  
Representative Samples ◽  
Screening Approaches

The success of batch mode active learning (BMAL) methods lies in selecting both representative and uncertain samples. Representative samples quickly capture the global structure of the whole dataset, while the uncertain ones refine the decision boundary. There are two principles, namely the direct approach and the screening approach, to make a trade-off between representativeness and uncertainty. Although widely used in literature, little is known about the relationship between these two principles. In this paper, we discover that the two approaches both have shortcomings in the initial stage of BMAL. To alleviate the shortcomings, we bound the certainty scores of unlabeled samples from below and directly combine this lower-bounded certainty with representativeness in the objective function. Additionally, we show that the two aforementioned approaches are mathematically equivalent to two special cases of our approach. To the best of our knowledge, this is the first work that tries to generalize the direct and screening approaches. The objective function is then solved by super-modularity optimization. Extensive experiments on fifteen datasets indicate that our method has significantly higher classification accuracy on testing data than the latest state-of-the-art BMAL methods, and also scales better even when the size of the unlabeled pool reaches 106.

scholarly journals An Efficient Algorithm for Ocean-Front Evolution Trend Recognition

2022 ◽  
Vol 14 (2) ◽  
pp. 259
Author(s):  
Yuting Yang ◽  
Kenneth Kin-Man Lam ◽  
Xin Sun ◽  
Junyu Dong ◽  
Redouane Lguensat
Keyword(s):  
Classification Accuracy ◽  
State Of The Art ◽  
Classification Algorithm ◽  
Final Decision ◽  
Ocean Fronts ◽  
Ocean Front ◽  
Novel Method ◽  
Evolution Laws ◽  
The Relationship ◽  
Evolution Trend

Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the evolution laws of marine hydrological elements are urgently needed. In this paper, a novel method, named Evolution Trend Recognition (ETR), is proposed to recognize the trend of ocean fronts, being the most important information in the ocean dynamic process. Therefore, in this paper, we focus on the task of ocean-front trend classification. A novel classification algorithm is first proposed for recognizing the ocean-front trend, in terms of the ocean-front scale and strength. Then, the GoogLeNet Inception network is trained to classify the ocean-front trend, i.e., enhancing or attenuating. The ocean-front trend is classified using the deep neural network, as well as a physics-informed classification algorithm. The two classification results are combined to make the final decision on the trend classification. Furthermore, two novel databases were created for this research, and their generation method is described, to foster research in this direction. These two databases are called the Ocean-Front Tracking Dataset (OFTraD) and the Ocean-Front Trend Dataset (OFTreD). Moreover, experiment results show that our proposed method on OFTreD achieves a higher classification accuracy, which is 97.5%, than state-of-the-art networks. This demonstrates that the proposed ETR algorithm is highly promising for trend classification.

Improving Gender Classification Using an Extended Set of Local Binary Patterns

2014 ◽  
Vol 5 (3) ◽  
pp. 47-66
Author(s):  
Abbas Roayaei Ardakany ◽  
Mircea Nicolescu ◽  
Monica Nicolescu
Keyword(s):  
Classification Accuracy ◽  
State Of The Art ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Gender Classification ◽  
High Classification Accuracy ◽  
Binary Descriptor ◽  
Central Pixel ◽  
Pixel Value ◽  
The Relationship

In this article, the authors designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, they proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. Traditional Local binary patterns include information about the relationship between a central pixel value and those of its neighboring pixels in a very compact manner. In the proposed method the authors incorporate into the descriptor more information from the neighborhood by using extra patterns. They have evaluated their approach on the standard FERET and CAS-PEAL databases and the experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. The results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.

Decoration technique and surface physics

1978 ◽  
Vol 36 (1) ◽  
pp. 436-437 ◽  
Author(s):  
H. Bethge
Keyword(s):  
Diffusion Path ◽  
Special Importance ◽  
Surface Physics ◽  
Step Structure ◽  
Initial Stage ◽  
Special Cases ◽  
Atomic Surface ◽  
The Mean ◽  
Bulk Structure ◽  
Decoration Technique

Besides the atomic surface structure, diverging in special cases with respect to the bulk structure, the real structure of a surface Is determined by the step structure. Using the decoration technique /1/ it is possible to image step structures having step heights down to a single lattice plane distance electron-microscopically. For a number of problems the knowledge of the monatomic step structures is important, because numerous problems of surface physics are directly connected with processes taking place at these steps, e.g. crystal growth or evaporation, sorption and nucleatlon as initial stage of overgrowth of thin films.To demonstrate the decoration technique by means of evaporation of heavy metals Fig. 1 from our former investigations shows the monatomic step structure of an evaporated NaCI crystal. of special Importance Is the detection of the movement of steps during the growth or evaporation of a crystal. From the velocity of a step fundamental quantities for the molecular processes can be determined, e.g. the mean free diffusion path of molecules.

Are misinformation, antiscientific claims, and conspiracy theories for political extremists?

2021 ◽  
Vol 24 (4) ◽  
pp. 583-605
Author(s):  
Adam M. Enders ◽  
Joseph E. Uscinski
Keyword(s):  
The Political ◽  
Conspiracy Theories ◽  
Political Orientations ◽  
Conspiracy Beliefs ◽  
Political Left ◽  
Identity Based ◽  
Nationally Representative ◽  
The Relationship ◽  
Political Groups ◽  
Representative Samples

Extremist political groups, especially “extreme” Republicans and conservatives, are increasingly charged with believing misinformation, antiscientific claims, and conspiracy theories to a greater extent than moderates and those on the political left by both a burgeoning scholarly literature and popular press accounts. However, previous investigations of the relationship between political orientations and alternative beliefs have been limited in their operationalization of those beliefs and political extremity. We build on existing literature by examining the relationships between partisan and nonpartisan conspiracy beliefs and symbolic and operational forms of political extremity. Using two large, nationally representative samples of Americans, we find that ideological extremity predicts alternative beliefs only when the beliefs in question are partisan in nature and the measure of ideology is identity-based. Moreover, we find that operational ideological extremism is negatively related to nonpartisan conspiracy beliefs. Our findings help reconcile discrepant findings regarding the relationship between political orientations and conspiracy beliefs.

scholarly journals Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 39
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Objective Function ◽  
Learning Process ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The Core ◽  
Learning Tasks ◽  
Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

scholarly journals BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yikui Zhai ◽  
He Cao ◽  
Wenbo Deng ◽  
Junying Gan ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Learning Strategy ◽  
State Of The Art ◽  
Activation Function ◽  
Training Data ◽  
Fine Grained ◽  
Pattern Recognition Problem ◽  
Face Features ◽  
Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

scholarly journals Proximity Effect Aware Detailed Placement in Electron Beam Lithography

2018 ◽  
Vol 232 ◽  
pp. 04046
Author(s):  
Yuhang Chen ◽  
Zhipeng Huang ◽  
Xiongfeng Chen ◽  
Jianli Chen ◽  
Wenxing Zhu
Keyword(s):  
Electron Beam ◽  
Objective Function ◽  
Proximity Effect ◽  
Electron Beam Lithography ◽  
State Of The Art ◽  
Experimental Result ◽  
Accurate Evaluation ◽  
Evaluation Scheme ◽  
Gauss Transform ◽  
Detailed Placement

Proximity effect is one of the most tremendous consequences that produces unacceptable exposures during electron beam lithography (EBL), and thus distorting the layout pattern. In this paper, we propose the first work which considers the proximity effect during layout stage. We first give an accurate evaluation scheme to estimate the proximity effect by fast Gauss transform. Then, we devote a proximity effect aware detailed placement objective function to simultaneously consider wirelength, density and proximity effect. Furthermore, cell swapping and cell matching based methods are used to optimize the objective function such that there is no overlap among cells. Compared with a state-of-the-art work, experimental result shows that our algorithm can efficiently reduce the proximity variations and maintain high wirelength quality at a reasonable runtime.

scholarly journals Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition

2015 ◽  
Vol 3 (2) ◽  
pp. 157-175 ◽  
Author(s):  
Peter B. Gilbert ◽  
Erin E. Gabriel ◽  
Ying Huang ◽  
Ivan S.F. Chan
Keyword(s):  
Causal Effect ◽  
Surrogate Endpoint ◽  
Effect Modification ◽  
Principal Stratification ◽  
Clinical Endpoint ◽  
Efficacy Trial ◽  
Marker Selection ◽  
Special Cases ◽  
High Utility ◽  
The Relationship

AbstractA common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification framework for addressing this problem based on data from a single randomized clinical efficacy trial, a variety of definitions and criteria for a good surrogate endpoint have been proposed, all based on or closely related to the “principal effects” or “causal effect predictiveness (CEP)” surface. We discuss CEP-based criteria for a useful surrogate endpoint, including (1) the meaning and relative importance of proposed criteria including average causal necessity (ACN), average causal sufficiency (ACS), and large clinical effect modification; (2) the relationship between these criteria and the Prentice definition of a valid surrogate endpoint; and (3) the relationship between these criteria and the consistency criterion (i.e. assurance against the “surrogate paradox”). This includes the result that ACN plus a strong version of ACS generally do not imply the Prentice definition nor the consistency criterion, but they do have these implications in special cases. Moreover, the converse does not hold except in a special case with a binary candidate surrogate. The results highlight that assumptions about the treatment effect on the clinical endpoint before the candidate surrogate is measured are influential for the ability to draw conclusions about the Prentice definition or consistency. In addition, we emphasize that in some scenarios that occur commonly in practice, the principal strata subpopulations for inference are identifiable from the observable data, in which cases the principal stratification framework has relatively high utility for the purpose of effect modification analysis and is closely connected to the treatment marker selection problem. The results are illustrated with application to a vaccine efficacy trial, where ACN and ACS for an antibody marker are found to be consistent with the data and hence support the Prentice definition and consistency.

ANN-Based Structure Optimization with Fatigue Reliability Constrains

2012 ◽  
Vol 204-208 ◽  
pp. 3128-3131
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Structural Optimization ◽  
Objective Function ◽  
Reliability Analysis ◽  
Response Surface ◽  
Minimum Weight ◽  
Structure Optimization ◽  
Fatigue Reliability ◽  
Ann Model ◽  
Surface Method ◽  
The Relationship

The ANN-based optimization for considering fatigue reliability requirements in structural optimization was proposed. The ANN-based response surface method was employed for performing fatigue reliability analysis. The fatigue reliability requirements were considered as constraints while the weight as the objective function, the ANN model was adopted to establish the relationship between the fatigue reliability and geometry dimension of the structure, the optimal results of the structure with a minimum weight was reached.

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