641 research outputs found
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Estimating the reliability function of the asymmetrical hybrid parallel-series system: Applied study at the state company for vegetable oils industry
The research studied and analyzed the hybrid parallel-series systems of asymmetrical components by applying different experiments of simulations used to estimate the reliability function of those systems through the use of the maximum likelihood method as well as the Bayes standard method via both symmetrical and asymmetrical loss functions following Rayleigh distribution and Informative Prior distribution.
The simulation experiments included different sizes of samples and default parameters which were then compared with one another depending on Square Error averages. Following that was the application of Bayes standard method by the Entropy Loss function that proved successful throughout the experimental side in finding the reliability function for the soap manufacturing machines of the State Company for Vegetable Oils Industries whose behavior was based on the hybrid parallel-series system of a symmetrical component that followed both the exponential distribution and the Rayleigh distribution
Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis : A multicenter study
Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ ρ =(-0.42)-(-0.76); p- values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
Environmental sensors are crucial for monitoring weather conditions and the
impacts of climate change. However, it is challenging to maximise measurement
informativeness and place sensors efficiently, particularly in remote regions
like Antarctica. Probabilistic machine learning models can evaluate placement
informativeness by predicting the uncertainty reduction provided by a new
sensor. Gaussian process (GP) models are widely used for this purpose, but they
struggle with capturing complex non-stationary behaviour and scaling to large
datasets. This paper proposes using a convolutional Gaussian neural process
(ConvGNP) to address these issues. A ConvGNP uses neural networks to
parameterise a joint Gaussian distribution at arbitrary target locations,
enabling flexibility and scalability. Using simulated surface air temperature
anomaly over Antarctica as ground truth, the ConvGNP learns spatial and
seasonal non-stationarities, outperforming a non-stationary GP baseline. In a
simulated sensor placement experiment, the ConvGNP better predicts the
performance boost obtained from new observations than GP baselines, leading to
more informative sensor placements. We contrast our approach with physics-based
sensor placement methods and propose future work towards an operational sensor
placement recommendation system. This system could help to realise
environmental digital twins that actively direct measurement sampling to
improve the digital representation of reality.Comment: In review for the Climate Informatics 2023 special issue of
Environmental Data Scienc
Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study
Background and rationale:
Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining.
Methods:
Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor.
Results:
In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings.
Conclusion:
Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings
Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study
Atrofia; IRM; Esclerosis múltipleAtròfia; IRM; Esclerosi múltipleAtrophy; MRI; Multiple SclerosisBackground and rationale
Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining.
Methods
Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor.
Results
In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings.
Conclusion
Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.The study was funded by the Nauta fonds through a travel grant. The MS Center Amsteram is supported by the Dutch MS Research Foundation through a program grant (current grant 18-358f). D.B. is supported by project PI18/00823 from the “Fondo de Investigación Sanitaria Carlos III”. F.B. and O.C. are supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The acquisition of data in London was funded by supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. A sincere thank you to Tom Verhoeven for his editing of the figures
Advanced Numerical Modeling in Manufacturing Processes
In manufacturing applications, a large number of data can be collected by experimental studies and/or sensors. This collected data is vital to improving process efficiency, scheduling maintenance activities, and predicting target variables. This dissertation explores a wide range of numerical modeling techniques that use data for manufacturing applications. Ignorance of uncertainty and the physical principle of a system are shortcomings of the existing methods. Besides, different methods are proposed to overcome the shortcomings by incorporating uncertainty and physics-based knowledge.
In the first part of this dissertation, artificial neural networks (ANNs) are applied to develop a functional relationship between input and target variables and process parameter optimization. The second part evaluates the robust response surface optimization (RRSO) to quantify different sources of uncertainty in numerical analysis. Additionally, a framework based on the Bayesian network (BN) approach is proposed to support decision-making. Due to various uncertainties, estimating interval and probability distribution are often more helpful than deterministic point value estimation. Thus, the Monte Carlo (MC) dropout-based interval prediction technique is explored in the third part of this dissertation. A conservative interval prediction technique for the linear and polynomial regression model is also developed using linear optimization.
Applications of different data-driven methods in manufacturing are useful to analyze situations, gain insights, and make essential decisions. But, the prediction by data-driven methods may be physically inconsistent. Thus, in the fourth part of this dissertation, a physics-informed machine learning (PIML) technique is proposed to incorporate physics-based knowledge with collected data for improving prediction accuracy and generating physically consistent outcomes. Each numerical analysis section is presented with case studies that involve conventional or additive manufacturing applications.
Based on various case studies carried out, it can be concluded that advanced numerical modeling methods are essential to be incorporated in manufacturing applications to gain advantages in the era of Industry 4.0 and Industry 5.0. Although the case study for the advanced numerical modeling proposed in this dissertation is only presented in manufacturing-related applications, the methods presented in this dissertation is not exhaustive to manufacturing application and can also be expanded to other data-driven engineering and system applications
Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
This paper presents a new methodology to alleviate the fundamental trade-off
between accuracy and latency in spiking neural networks (SNNs). The approach
involves decoding confidence information over time from the SNN outputs and
using it to develop a decision-making agent that can dynamically determine when
to terminate each inference.
The proposed method, Dynamic Confidence, provides several significant
benefits to SNNs. 1. It can effectively optimize latency dynamically at
runtime, setting it apart from many existing low-latency SNN algorithms. Our
experiments on CIFAR-10 and ImageNet datasets have demonstrated an average 40%
speedup across eight different settings after applying Dynamic Confidence. 2.
The decision-making agent in Dynamic Confidence is straightforward to construct
and highly robust in parameter space, making it extremely easy to implement. 3.
The proposed method enables visualizing the potential of any given SNN, which
sets a target for current SNNs to approach. For instance, if an SNN can
terminate at the most appropriate time point for each input sample, a ResNet-50
SNN can achieve an accuracy as high as 82.47% on ImageNet within just 4.71 time
steps on average. Unlocking the potential of SNNs needs a highly-reliable
decision-making agent to be constructed and fed with a high-quality estimation
of ground truth. In this regard, Dynamic Confidence represents a meaningful
step toward realizing the potential of SNNs
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