533 research outputs found
Unsupervised event abstraction using pattern abstraction and local process models
Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs
Unsupervised event abstraction using pattern abstraction and local process models
Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based on
connectome data. A connectome is a network representation of a human brain. The
typical connectome classification problem is very challenging because of the
small sample size and high dimensionality of the data. We propose to use
simultaneous approximate diagonalization of adjacency matrices in order to
compute their eigenstructures in more stable way. The obtained approximate
eigenvalues are further used as features for classification. The proposed
approach is demonstrated to be efficient for detection of Alzheimer's disease,
outperforming simple baselines and competing with state-of-the-art approaches
to brain disease classification
iPINNs: Incremental learning for Physics-informed neural networks
Physics-informed neural networks (PINNs) have recently become a powerful tool
for solving partial differential equations (PDEs). However, finding a set of
neural network parameters that lead to fulfilling a PDE can be challenging and
non-unique due to the complexity of the loss landscape that needs to be
traversed. Although a variety of multi-task learning and transfer learning
approaches have been proposed to overcome these issues, there is no incremental
training procedure for PINNs that can effectively mitigate such training
challenges. We propose incremental PINNs (iPINNs) that can learn multiple tasks
(equations) sequentially without additional parameters for new tasks and
improve performance for every equation in the sequence. Our approach learns
multiple PDEs starting from the simplest one by creating its own subnetwork for
each PDE and allowing each subnetwork to overlap with previously learned
subnetworks. We demonstrate that previous subnetworks are a good initialization
for a new equation if PDEs share similarities. We also show that iPINNs achieve
lower prediction error than regular PINNs for two different scenarios: (1)
learning a family of equations (e.g., 1-D convection PDE); and (2) learning
PDEs resulting from a combination of processes (e.g., 1-D reaction-diffusion
PDE). The ability to learn all problems with a single network together with
learning more complex PDEs with better generalization than regular PINNs will
open new avenues in this field
DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks
In this paper, we propose DeepAlign, a novel approach to multi-perspective
process anomaly correction, based on recurrent neural networks and
bidirectional beam search. At the core of the DeepAlign algorithm are two
recurrent neural networks trained to predict the next event. One is reading
sequences of process executions from left to right, while the other is reading
the sequences from right to left. By combining the predictive capabilities of
both neural networks, we show that it is possible to calculate sequence
alignments, which are used to detect and correct anomalies. DeepAlign utilizes
the case-level and event-level attributes to closely model the decisions within
a process. We evaluate the performance of our approach on an elaborate data
corpus of 252 realistic synthetic event logs and compare it to three
state-of-the-art conformance checking methods. DeepAlign produces better
corrections than the rest of the field reaching an overall score of
across all datasets, whereas the best comparable state-of-the-art
method reaches
Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices
BACKGROUND AND PURPOSE: To apply and evaluate an intensityâbased interpolation technique, enabling segmentation of motionâaffected neonatal brain MRI. METHODS: Moderateâlate preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderateâlate Preterm infants âBIMPâstudyâ) between August 2017 and November 2019. T2âweighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderateâsevere motion (n = 33 [29%]) were included. Motionâaffected slices were reâestimated using intensityâbased shapeâpreserving cubic spline interpolation, and automatically segmented in eight structures. Quality of interpolation and segmentation was visually assessed for errors after interpolation. Reliability was tested using interpolated control group scans (18/54 axial slices). Structural similarity index (SSIM) was used to compare T2âweighted scans, and SĂžrensenâDice was used to compare segmentation before and after interpolation. Finally, volumes of brain structures of the control group were used assessing sensitivity (absolute mean fraction difference) and bias (confidence interval of mean difference). RESULTS: Visually, segmentation of 25 scans (22%) with motion artifacts improved with interpolation, while segmentation of eight scans (7%) with adjacent motionâaffected slices did not improve. Average SSIM was .895 and SĂžrensenâDice coefficients ranged between .87 and .97. Absolute mean fraction difference was â€0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14â3.07 cm(3)), cerebrospinal fluid (0.39â1.65 cm(3)), deep gray matter (0.74â1.01 cm(3)), and brainstem volumes (0.07â0.28 cm(3)) and a negative bias in white matter volumes (â4.47 to â1.65 cm(3)). CONCLUSION: According to qualitative and quantitative assessment, intensityâbased interpolation reduced the percentage of discarded scans from 29% to 7%
Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI
The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Î, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'
Quality Assurance of Spectral Ultraviolet Measurements in Europe Through the Development of a Transportable Unit (QASUME)
QASUME is a European Commission funded project that aims to develop and test a transportable unit for providing quality assurance to UV spectroradiometric measurements conducted in Europe. The comparisons will be performed at the home sites of the instruments, thus avoiding the risk of transporting instruments to participate in intercomparison campaigns. Spectral measurements obtained at each of the stations will be compared, following detailed and objective comparison protocols, against collocated measurements performed by a thoroughly tested and validated travelling unit. The transportable unit comprises a spectroradiometer, its calibrator with a set of calibration lamps traceable to the sources of different Standards Laboratories, and devices for determining the slit function and the angular response of the local spectroradiometers. The unit will be transported by road to about 25 UV stations over a period of about two years. The spectroradiometer of the transportable unit is compared in an intercomparison campaign with six instruments to establish a relation, which would then be used as a reference for its calibration over the period of its regular operation at the European stations. Different weather patterns (from clear skies to heavy rain) were present during the campaign, allowing the performance of the spectroradiometers to be evaluated under unfavourable conditions (as may be experienced at home sites) as well as the more desirable dry conditions. Measurements in the laboratory revealed that the calibration standards of the spectroradiometers differ by up to 10%. The evaluation is completed through comparisons with the same six instruments at their homes sites
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