scholarly journals Shapelet Discovery by Lazy Time Series Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Wei Zhang ◽  
Zhihai Wang ◽  
Jidong Yuan ◽  
Shilei Hao
Keyword(s):  
Time Series ◽  
Training Dataset ◽  
Class Label ◽  
Considerable Research ◽  
Evaluation Dataset ◽  
Differential Ability ◽  
Global And Local ◽  
Insight Into ◽  
Specific Evaluation ◽  
Feature Frequency

As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.

scholarly journals The role of epigenetic modifications for the pathogenesis of Crohn's disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
M. Hornschuh ◽  
E. Wirthgen ◽  
M. Wolfien ◽  
K. P. Singh ◽  
O. Wolkenhauer ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Adaptive Immune Response ◽  
Adaptive Immune ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
New Biomarkers ◽  
Insight Into ◽  
Specific Evaluation

AbstractEpigenetics has become a promising field for finding new biomarkers and improving diagnosis, prognosis, and drug response in inflammatory bowel disease. The number of people suffering from inflammatory bowel diseases, especially Crohn's disease, has increased remarkably. Crohn's disease is assumed to be the result of a complex interplay between genetic susceptibility, environmental factors, and altered intestinal microbiota, leading to dysregulation of the innate and adaptive immune response. While many genetic variants have been identified to be associated with Crohn's disease, less is known about the influence of epigenetics in the pathogenesis of this disease. In this review, we provide an overview of current epigenetic studies in Crohn's disease. In particular, we enable a deeper insight into applied bioanalytical and computational tools, as well as a comprehensive update toward the cell-specific evaluation of DNA methylation and histone modifications.

Media Coverage and Solidarity after Tragedies: The Reporting of School Shootings in Two Nations

2012 ◽  
Vol 11 (6) ◽  
pp. 845-874 ◽  
Author(s):  
James Hawdon ◽  
James Hawdon ◽  
Atte Oksanen ◽  
James Hawdon ◽  
Atte Oksanen ◽  
...  
Keyword(s):  
New York ◽  
Media Coverage ◽  
New York Times ◽  
Virginia Tech ◽  
School Shootings ◽  
Considerable Research ◽  
National Comparison ◽  
The Media ◽  
Insight Into ◽  
Cross National

Abstract Although considerable research analyzes the media coverage of school shootings, there is a lack of cross-national comparative studies. Yet, a cross-national comparison of the media coverage of school shootings can provide insight into how this coverage can affect communities. Our research focuses on the reporting of the school shootings at Virginia Tech in the U.S. and Jokela and Kauhajoki in Finland. Using 491 articles from the New York Times and Helsingin Sanomat published within a month of each shooting we investigate how reports vary between the nations and among the tragedies. We investigate if one style of framing a tragedy, the use of a “tragic frame,” may contribute to differences in the communities’ response to the events.

Death and Disposal Locations of Serial Homicides: The Effect on Recovery Timeframes

2021 ◽  
pp. 108876792110068
Author(s):  
Brendan Chapman ◽  
Cody Raymer ◽  
David A. Keatley
Keyword(s):  
Time Series ◽  
Exploratory Study ◽  
Serial Killers ◽  
Disposal Site ◽  
Time Since Death ◽  
Forensic Evidence ◽  
Hostile Environments ◽  
Case Data ◽  
Insight Into ◽  
Homicide Case

Many factors affect the solvability of homicides, including body disposal location and time between death and recovery. The aim of this exploratory study was to probe a number of spatiotemporal variables for trends across a subset of solved homicide case data from 54 North American serial killers, active between 1920 and 2016 (125 solved cases) to identify areas for further research. We investigated murder site and body disposal site as location variables with eight subcategories across eight discrete time series, seeking insight into how these factors may affect the early stages of an investigation and (therefore by inference) solvability. The findings showed that bodies recovered after 48 hours are more likely discovered outdoor while those discovered within 24 hours, within the victim’s residence. This has implications for the ability to recover forensic evidence when bodes are located after a prolonged time since death as well as in more hostile environments.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

scholarly journals A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zhanwei Xuan ◽  
Xiang Feng ◽  
Jingwen Yu ◽  
Pengyao Ping ◽  
Haochen Zhao ◽  
...  
Keyword(s):  
Higher Order ◽  
System Level ◽  
Cell Proliferation And Differentiation ◽  
Proliferation And Differentiation ◽  
Great Insight ◽  
Novel Method ◽  
Global And Local ◽  
Novel Model ◽  
Insight Into ◽  
Orthogonal Iteration

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

Self-Tuning Varri Method in Preparing Fatigue Segment

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
M. H. Osman ◽  
Z. M. Nopiah ◽  
S. Abdullah ◽  
A. Lennie
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Training Dataset ◽  
Series Data ◽  
Additional Advantage ◽  
Local Maxima ◽  
Conventional Procedure ◽  
Computational Operation ◽  
Self Tuning ◽  
The Difference

An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.

scholarly journals Technological mediation in the future of experiential tourism

2019 ◽  
Vol 5 (2) ◽  
pp. 120-126 ◽  
Author(s):  
Ellis Urquhart
Keyword(s):  
Technological Development ◽  
Tourism Industry ◽  
Future Directions ◽  
Technological Mediation ◽  
Content Type ◽  
Academic Literature ◽  
Considerable Research ◽  
Theoretical Perspectives ◽  
The Future ◽  
Insight Into

Purpose The purpose of this paper is to consider the role that technology may play in the future of experiential tourism. This viewpoint paper begins to question future developments in technological mediation and how these may challenge the author’ view of experiences and their construction in a period of immense and rapid technological development. Design/methodology/approach This is a short viewpoint paper driven by theoretical perspectives in the existing academic literature and the author’s personal stance on the future of experiential tourism. Findings This paper suggests that while there is considerable research into the role and application of technology within tourism, there is a lack of future-orientated debate. The views expressed within the paper argue that three potential directions exist for the future of technological mediation in experiential tourism: mass acceptance and customisation; experiential convergence or “rewinding the clock”, each with significant implications for the management of technological mediation in experiential tourism. Originality/value The paper provides an initial insight into future directions of the tourism industry in a period of immense technological development. Based on existing theoretical perspectives, these viewpoints indicate three potential routes for the industry and act as a catalyst for further dialogue within tourism scholarship.

Comparative analyses of mean-variance and mean-semivariance approaches on global and local single factor market model for developed and emerging markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehmet Emin Yildiz ◽  
Yaman Omer Erzurumlu ◽  
Bora Kurtulus
Keyword(s):  
Emerging Markets ◽  
Stock Returns ◽  
Risk Measures ◽  
Emerging Market ◽  
Cost Of Equity ◽  
Content Type ◽  
The Cost ◽  
Global And Local ◽  
Insight Into ◽  
Better Than

PurposeThe beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.Design/methodology/approachThe study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.FindingsThe results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.Research limitations/implicationsThe data are limited to the index securities as beta could be time varying.Practical implicationsResults overall provide insight into the cost of equity calculation and emerging market assets valuation.Originality/valueThe framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

scholarly journals Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 925 ◽  
Author(s):  
Stephen Guth ◽  
Themistoklis P. Sapsis
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Extreme Event ◽  
Binary Classification ◽  
Selection Criterion ◽  
Area Under The Curve ◽  
Wave Model ◽  
Training Dataset ◽  
Ocean Engineering ◽  
Selection Of

The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. We consider the problem of deriving optimal predictors of extremes directly from data characterizing a complex system, by formulating the problem in the context of binary classification. Specifically, we assume that a training dataset consists of: (i) indicator time series specifying on whether or not an extreme event occurs; and (ii) observables time series, which are employed to formulate efficient predictors. We employ and assess standard binary classification criteria for the selection of optimal predictors, such as total and balanced error and area under the curve, in the context of extreme event prediction. For physical systems for which there is sufficient separation between the extreme and regular events, i.e., extremes are distinguishably larger compared with regular events, we prove the existence of optimal extreme event thresholds that lead to efficient predictors. Moreover, motivated by the special character of extreme events, i.e., the very low rate of occurrence, we formulate a new objective function for the selection of predictors. This objective is constructed from the same principles as receiver operating characteristic curves, and exhibits a geometric connection to the regime separation property. We demonstrate the application of the new selection criterion to the advance prediction of intermittent extreme events in two challenging complex systems: the Majda–McLaughlin–Tabak model, a 1D nonlinear, dispersive wave model, and the 2D Kolmogorov flow model, which exhibits extreme dissipation events.

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