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Pedunculopontine Nucleus Stimulation: Where are We Now and What Needs to be Done to Move the Field Forward?

Hokuto Morita, Chris J. Hass, Elena Moro, Atchar Sudhyadhom, Rajeev Kumar, Michael S. Okun
2014 Frontiers in Neurology  
This review summarizes the current knowledge of PPN as a DBS target and areas that need to be addressed to advance the field.  ...  Thus far axial symptoms, such as postural instability and gait freezing, have been refractory to current treatment approaches and remain a critical unmet need.  ...  Conflict of Interest Statement: The authors have no relevant financial disclosures to this review. Dr.  ... 
doi:10.3389/fneur.2014.00243 pmid:25538673 pmcid:PMC4255598 fatcat:ptlq3rbuknbt7k4ggwvd7v5yc4

Detecting data errors

Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, Nan Tang
2016 Proceedings of the VLDB Endowment  
and (2) what is the best strategy to holistically run multiple tools to optimize the detection effort?  ...  Since different types of errors may coexist in the same data set, we often need to run more than one kind of tool.  ...  Outlier detection would be more effective on high-quality data sets where errors are rather rare.  ... 
doi:10.14778/2994509.2994518 fatcat:zx4yulp3d5gzbbntm6jnmmqxgy

Exploiting Unlabeled Data for Neural Grammatical Error Detection [article]

Zhuoran Liu, Yang Liu
2016 arXiv   pre-print
In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models.  ...  Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because  ...  Since we only detect errors without inferring their types, we are unable to provide the full list of error types our model is able to detect.  ... 
arXiv:1611.08987v2 fatcat:x5aoznel3vcxtjddecjbclotae

Detecting Data Errors with Statistical Constraints [article]

Jing Nathan Yan, Oliver Schulte, Jiannan Wang, Reynold Cheng
2019 arXiv   pre-print
A powerful approach to detecting erroneous data is to check which potentially dirty data records are incompatible with a user's domain knowledge.  ...  Experiments on synthetic and real-world data illustrate how SCs apply to error detection, and provide evidence that CODED performs better than state-of-the-art approaches.  ...  Due to the space limit, we just focus on the imputation errors in this experiment. The average F-score of CODED and DBoost are 0.45 and 0.24, respectively.  ... 
arXiv:1902.09711v1 fatcat:7aqsvizztvav3hybb2z3ivopcu

A strategy for designing error detection schemes for general data networks

J.M. Simmons
1994 IEEE Network  
Error detection mechanisms such as CRCs and length fields must be included along with the data to prevent errored data from being accepted as error-free by the destination.  ...  are most effective, and third, which layer should be responsible for detecting a given type of error.  ...  The tradeoffs are similar to what was discussed in section 5.1, where we considered using the routing field CRC to correct errors.  ... 
doi:10.1109/65.298162 fatcat:4xjsbw5jvvhxhmxz6mqm7uvzn4

Motion Estimation Testing Applications using Error Detection and Data Recovery Architecture
IJARCCE - Computer and Communication Engineering

2014 IJARCCE  
The proposed EDDR design for ME testing can detect errors and recover data with an acceptable area overhead and timing penalty. The functional verification and synthesis can be done by Xilinx ISE.  ...  This paper presents an error detection and data recovery (EDDR) design, based on the residue-and-quotient (RQ) code, to embed into motion estimation (ME) for video coding testing applications.  ...  Based on the RQ code, a RQCG-based TCG design is developed to generate the corresponding test codes to detect errors and recover data.  ... 
doi:10.17148/ijarcce.2014.31160 fatcat:ferqj2hbe5cillzhb6mf3lskpe

Automatic string test data generation for detecting domain errors

Ruilian Zhao, Michael R. Lyu, Yinghua Min
2009 Software testing, verification & reliability  
Our empirical work is conducted on a set of programs with string predicates, where extensive trials have been done for each string predicate, and the results are analysed using the SPSS tool.  ...  As identified by Howden, program errors ‡ can be classified into three categories: computation errors, missing-path errors and domain errors [11] , where error is used to represent the difference between  ...  ACKNOWLEDGEMENTS Shanshan Lv conducted extensive experiments and analysis for this paper. Here we express our heartfelt appreciation.  ... 
doi:10.1002/stvr.414 fatcat:6miejeoatncmzbxsaf4ynhod4m

Multiple negative selection algorithm: Improving detection error rates in IoT intrusion detection systems

Marin E. Pamukov, Vladimir K. Poulkov
2017 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)  
One of them being the need for an implementation of unsupervised learning and decision making in the detection syste 1 m.  ...  It uses a twotiered negative selection process to implement a co-stimulation approach aimed at decreasing the number of detection errors without the need of an operator input.  ...  It uses a two-tiered negative selection to implement a co-stimulation, aimed at lowering the detection errors without the need for human input.  ... 
doi:10.1109/idaacs.2017.8095140 dblp:conf/idaacs/PamukovP17 fatcat:rsr5lpwd2bbdpacu4qo6dhaotu

Interactive learning for efficiently detecting errors in insurance claims

Rayid Ghani, Mohit Kumar
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
Many practical data mining systems such as those for fraud detection and surveillance deal with building classifiers that are not autonomous but part of a larger interactive system with an expert in the  ...  Our interactive prioritization component is built on top of a batch classifier that has been trained to detect payment errors in health insurance claims and optimizes the interaction between the classifier  ...  Fraud Detection, Intrusion Detection, Medical Diagnosis, Information Filtering, and Video Surveillance are some examples where these systems are currently being used to aid experts in finding cases of  ... 
doi:10.1145/2020408.2020463 dblp:conf/kdd/GhaniK11 fatcat:5k4pstsbpnd4tehb6savpaidry

A reconstruction error-based framework for label noise detection

Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2021 Journal of Big Data  
Our binary classification approach, which considers label noise instances as anomalies, uniquely uses reconstruction errors for noisy data in order to identify and filter label noise.  ...  It has also been shown to increase model complexity and decrease model interpretability. In addition, label noise can cause the classification results of a learner to be poor.  ...  Acknowledgements We would like to thank the reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University.  ... 
doi:10.1186/s40537-021-00447-5 fatcat:qqextqkggnc7bg5qqbzel4dkme

An Automatic Comparison Approach to Detect Errors on 3D City Models [article]

Benjamin Gorszczyk, Guillaume Damiand, Sylvie Servigne, Abdoulaye Diakité, Gilles Gesquière
2016 Eurographics Workshop on Urban Data Modelling and Visualisation  
To provide better results, these models must be errors-free and that is why it is required to have a way to detect and to correct errors. These errors can be geometric, topological or semantic.  ...  This algorithm allowed to automatically detect and correct semantic errors on several models that are currently used by professionals.  ...  We can for example define a hole filling algorithm in order to guarantee that each volume has no boundary. In order to reach an error-free model, many things still need to be done.  ... 
doi:10.2312/udmv.20161416 dblp:conf/udmv/GorszczykDSDG16 fatcat:rixvht3j4zfkplqgesys2jtuse

A Bayesian approach to gross error detection in chemical process data

Ajit C. Tamhane, Corneliu Iordache, Richard S.H. Mah
1988 Chemometrics and Intelligent Laboratory Systems  
., Iordache, C. and Mah, R.S.H., 1988. A Bayesian approach to gross error detection in chemical process data. Part I: Model development.  ...  In this section we relax these assumptions and discuss how the basic model needs to be modified accordingly.  ...  and mj") according to equation (4.9) . Notice that we need to know the lifetimes tj" in order to apply this equation.  ... 
doi:10.1016/0169-7439(88)80011-x fatcat:3qk4wsj3xrdote22u3vstfnyh4

Detecting Reporting Errors in Data from Decentralised Autonomous Administrations with an Application to Hospital Data

Arnout van Delden, Jan van der Laan, Annemarie Prins
2018 Journal of Official Statistics  
We present an automatic procedure to detect suspicious data suppliers in decentralised administrative data in which shifts in reporting behaviour are likely to have affected the estimated output.  ...  Administrative data sources are increasingly used by National Statistical Institutes to compile statistics.  ...  Otherwise, selective manual data editing will be used, and, if needed, respondents are contacted.  ... 
doi:10.2478/jos-2018-0043 fatcat:paxyvo7pt5byfhsw2euviqy5ca

Learning from Mistakes is Easier Said Than Done: Group and Organizational Influences on the Detection and Correction of Human Error

Amy C. Edmondson
1996 Journal of Applied Behavioral Science  
Findings from patient care groups in two hospitals show systematic differences not just in the frequency of errors, but also in the likelihood that errors will be detected and learned from by group members  ...  This research explores how group-and organizational-level factors affect errors in administering drugs to hospitalized patients.  ...  Organizational and group interventions may be needed to encourage detection and discussion of error, although strategies for accomplishing this are beyond the scope of this article.  ... 
doi:10.1177/0021886396321001 fatcat:ednx4muywre7bbwiswj2yj75hq

Detecting drift bias and exposure errors in solar and photosynthetically active radiation data

J.D. Wood, T.J. Griffis, J.M. Baker
2015 Agricultural and Forest Meteorology  
Deployment exposure errors caused by sensor shading were also discovered by comparing the daily correlations between (i) K↓ and K EX and (ii) PAR and K EX .  ...  ) and PAR to K EX .  ...  Acknowledgements We would like to acknowledge the technical support provided by Bill Breiter, as well as the site PI's from other  ... 
doi:10.1016/j.agrformet.2015.02.015 fatcat:3imlq6lgprekdf2qaeqb66ehsa
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