Heuristics for high-utility local process model mining

Local Process Models (LPMs) describe structured fragments of process behavior occurring in the context of less structured business processes. In contrast to traditional support-based LPM discovery, which aims to generate a collection of process models that describe highly frequent behavior, High-Utility Local Process Model (HU-LPM) discovery aims to generate a collection of process models that provide useful business insights by specifying a utility function. Mining LPMs is a computationally expensive task, because of the large search space of LPMs. In supportbased LPM mining, the search space is constrained by making use of the property that support is anti-monotonic. We show that in general, we cannot assume a provided utility function to be anti-monotonic, therefore, the search space of HU-LPMs cannot be reduced without loss. We propose four heuristic methods to speed up the mining of HU-LPMs while still being able to discover useful HU-LPMs. We demonstrate their applicability on three real-life data sets

Explaining Predictive Uncertainty with Information Theoretic Shapley Values

Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast, explaining the $\textit{uncertainty}$ of model outputs has received relatively little attention. We adapt the popular Shapley value framework to explain various types of predictive uncertainty, quantifying each feature's contribution to the conditional entropy of individual model outputs. We consider games with modified characteristic functions and find deep connections between the resulting Shapley values and fundamental quantities from information theory and conditional independence testing. We outline inference procedures for finite sample error rate control with provable guarantees, and implement an efficient algorithm that performs well in a range of experiments on real and simulated data. Our method has applications to covariate shift detection, active learning, feature selection, and active feature-value acquisition

Mode conversation losses in overmolded millimeter wave transmission lines

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 106-109).Millimeter wave transmission lines are integral components for many important applications like nuclear fusion and NMR spectroscopy. In low loss corrugated transmission lines propagating the HE,1 mode with a high waveguide radius to wavelength ratio (a/X), the transmission line loss is predominantly a result of mode conversion in components such as miter bends. The theory for determining losses in miter bends though is only approximate, and is based instead on the problem of the loss across a diameter-length gap between two waveguide sections. Through simulation, we verified that the existing analytic theory of this gap loss is correct; however, our simulations could not verify the assumption that the miter bend loss is half the loss in the gap. We also considered the problem of higher order modes (HOMs) mixed with an HE11 input entering the miter bend. Using a numerical technique, we found that the loss through the miter bend is dependent on both the amplitude of the HOM content as well as its phase relative to the phase of the HE11 mode. While the overall loss averaged across all phases remains the same with increasing HOM content, the power that fails to traverse the gap tends to increase, and it is this power that appears as very high order modes that will cause heating around the miter bend. For the ITER transmission line, the loss based on gap theory is 0.027 dB and, using a coherent technique, we measured a loss of 0.05 + 0.02 dB with a vector network analyzer (VNA).(cont.) We also set out to measure the mode conversion caused by a miter bend by using a 3-axis scanner system to measure the field patterns within the ITER waveguide. Due to the presence of higher order modes output by the HE I launcher, definitive results on the mode conversion attributed to the miter bend could not be obtained. Using a phase retrieval code, we were able to calculate the mode purity of the launcher output and found it to be 98 + 0.5 %. Future work will concentrate on reducing this HOM content to enable measurements of the miter bend mode conversion.by David S. Tax.S.M

Experimental study of a high efficiency step-tunable MW gyrotron oscillator

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 191-205).The gyrotron is a source capable of producing megawatt power levels at millimeter-wave frequencies for many important applications, including electron cyclotron heating and current drive in magnetic fusion devices. It is important that the gyrotron operates with high efficiency and provides a high quality output beam to minimize system size, maximize reliability and avoid additional losses in external systems. This thesis presents the experimental study of such a gyrotron designed to operate at MW power levels and whose initial 110 GHz operation was expanded to include operation at 124.5 GHz. To this end, a new set of components, including a cavity, mode converter, and output window were designed for operation at both frequencies. The cavity was designed using the code MAGY and the Q factors of 830 for the TE22,6,1mode at 110 GHz and 1060 for the TE24,7,1 mode at 124.5 GHz would be suitable for CW operation in an industrial gyrotron. The mode converter consisting of a dimpled-wall launcher and 4 phasecorrecting mirrors could theoretically produce an output beam with 99 % Gaussian beam content at each frequency while a single-disc window was implemented with over 99.5 % power transmission at both frequencies. The achieved output power in experiment was 1.1 MW at 110 GHz and 850 kW at 124.5 GHz for the design parameters of 96 kV and 40 A. At 98 kV and 42 A, the gyrotron achieved 1.25 MW and 1 MW at 110 and 124.5 GHz, respectively. Mode competition is typically a major limitation in such gyrotrons, and stable single-mode operation was demonstrated at both frequencies. At 110 GHz, the output beam had 98.8 % Gaussian beam content, while at 124.5 GHz, the output beam quality was 94.4 %. Another experiment within this thesis demonstrated the implementation of a mode converter with smooth mirrors that would be less susceptible to machining and misalignment errors. A Gaussian beam content of 96 % was measured in that experiment. In addition, a thorough study of the gyrotron start-up scenario was performed, for which experimental work had been lacking in the literature. The start-up scenario is the sequence of modes that are excited during the rise of the voltage pulse and is essential for the gyrotron to operate in its most efficient regime known as the hard self-excitation regime. This gyrotron operates nominally in the TE22,6,1 mode near the 110 GHz cutoff frequency with an axial field profile that is approximately Gaussian at the steady-state peak voltage. In experiments performed in the smooth mirror mode converter configuration, lower frequency modes were observed at lower voltages as opposed to higher frequency modes as predicted by theory. Analysis of these modes showed that they are backward-wave modes far from their cutoff frequency which have higher order axial field profiles, i.e. TE21,6,3 and TE21,6,4 modes at frequencies of 108-109 GHz. The excitation of these modes was investigated and shown to be possible by using theory and single-mode simulations with the code MAGY. This discovery was important as these modes were not included in past code runs, and thus future improvements can be made to incorporate this effect.by David S. Tax.Ph.D

The importance of correcting for signal drift in diffusion MRI

PURPOSE: To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift. METHODS: We investigated the signal magnitude of non-diffusion-weighted EPI volumes in a series of diffusion-weighted imaging experiments to determine whether signal magnitude changes over time. Different scan protocols and scanners from multiple vendors were used to verify this on phantom data, and the effects on diffusion kurtosis tensor estimation in phantom and in vivo data were quantified. Scalar metrics (eigenvalues, fractional anisotropy, mean diffusivity, mean kurtosis) and directional information (first eigenvectors and tractography) were investigated. RESULTS: Signal drift, a global signal decrease with subsequently acquired images in the scan, was observed in phantom data on all three scanners, with varying magnitudes up to 5% in a 15-min scan. The signal drift has a noticeable effect on the estimation of diffusion parameters. All investigated quantitative parameters as well as tractography were affected by this artifactual signal decrease during the scan. CONCLUSION: By interspersing the non-diffusion-weighted images throughout the session, the signal decrease can be estimated and compensated for before data analysis; minimizing the detrimental effects on subsequent MRI analyses. Magn Reson Med, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

Classifying Process Instances Using Recurrent Neural Networks

Process Mining consists of techniques where logs created by operative systems are transformed into process models. In process mining tools it is often desired to be able to classify ongoing process instances, e.g., to predict how long the process will still require to complete, or to classify process instances to different classes based only on the activities that have occurred in the process instance thus far. Recurrent neural networks and its subclasses, such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), have been demonstrated to be able to learn relevant temporal features for subsequent classification tasks. In this paper we apply recurrent neural networks to classifying process instances. The proposed model is trained in a supervised fashion using labeled process instances extracted from event log traces. This is the first time we know of GRU having been used in classifying business process instances. Our main experimental results shows that GRU outperforms LSTM remarkably in training time while giving almost identical accuracies to LSTM models. Additional contributions of our paper are improving the classification model training time by filtering infrequent activities, which is a technique commonly used, e.g., in Natural Language Processing (NLP).Peer reviewe

Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain

The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong () gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from at to at ), while this dependence on b in the corticospinal tract is less pronounced (from at to at ). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices

BACKGROUND AND PURPOSE: To apply and evaluate an intensity‐based interpolation technique, enabling segmentation of motion‐affected neonatal brain MRI. METHODS: Moderate‐late preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderate‐late Preterm infants “BIMP‐study”) between August 2017 and November 2019. T2‐weighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderate‐severe motion (n = 33 [29%]) were included. Motion‐affected slices were re‐estimated using intensity‐based shape‐preserving 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 T2‐weighted scans, and Sørensen‐Dice 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 motion‐affected slices did not improve. Average SSIM was .895 and Sørensen‐Dice coefficients ranged between .87 and .97. Absolute mean fraction difference was ≤0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14‐3.07 cm(3)), cerebrospinal fluid (0.39‐1.65 cm(3)), deep gray matter (0.74‐1.01 cm(3)), and brainstem volumes (0.07‐0.28 cm(3)) and a negative bias in white matter volumes (–4.47 to –1.65 cm(3)). CONCLUSION: According to qualitative and quantitative assessment, intensity‐based interpolation reduced the percentage of discarded scans from 29% to 7%