Mutual Information Metric Research Articles

Iterative Outlier Removal Clustering Based Time-Frequency-Spatial Feature Selection for Binary EEG Motor Imagery Decoding

Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) is a bridge in the instruments of rehabilitation and motor assistance field to control external assist devices without external stimulation. Feature extraction (FE) is a key to improving the performance of EEG MI decoding. Existing FE methods usually extract single-domain features such as spatial, frequency features, or dual-domain features such as time–frequency features. However, the multidomains features which describe the intent more comprehensively are not simultaneously extracted. Therefore, inspired by short-time Fourier transform (STFT) and common spatial pattern (CSP) FE methods, a frequency-spatial-temporal multidomain (FSTMD) FE method is proposed. To solve the problem of the high dimensions of the FSTMD features, a feature selection (FS) method is designed. In the proposed FSTMD FE process, the STFT is employed to extract the time–frequency domain features first. Then, the time–frequency features of each channel are cascaded. After that, the cascaded features are divided according to time segments. At last, the covariance transform is performed for each time segment. Since the proposed features have a one-to-one correspondence with frequency, channel, and time, the extracted features have good interpretability. The proposed FS method selects the features in the frequency-spatial domain of each time segment based on mutual information and correlation metrics first and then selects the key time segment based on clustering with outlier removal. The performance of the proposed feature is verified in BCI Competition IV Dataset IIa and IIb. Compared to the CSP feature in the only spatial domain, the proposed feature has significantly lower deviation and slightly higher accuracy. The average recognition accuracy of the proposed MI decoding framework is 85.1% on the dataset IIb session 3, which is competitive compared with the state-of-the-art methods.

PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.

Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer's disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders

Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAEt) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAEt). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAEt embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the F1 score in the task of classifying exoplanets. Moreover, the S-VRAEt model increases all the advantages of VRAEt, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.

Intensity-Mosaic: automatic panorama mosaicking of disordered images with insufficient features.

Purpose: Handling low-quality and few-feature medical images is a challenging task in automatic panorama mosaicking. Current mosaicking methods for disordered input images are based on feature point matching, whereas in this case intensity-based registration achieves better performance than feature-point registration methods. We propose a mosaicking method that enables the use of mutual information (MI) registration for mosaicking randomly ordered input images with insufficient features. Approach: Dimensionality reduction is used to map disordered input images into a low dimensional space. Based on the low dimensional representation, the image global correspondence can be recognized efficiently. For adjacent image pairs, we optimize the MI metric for registration. The panorama is then created after image blending. We demonstrate our method on relatively lower-cost handheld devices that acquire images from the retina in vivo, kidney ex vivo, and bladder phantom, all of which contain sparse features. Results: Our method is compared with three baselines: AutoStitch, "dimension reduction + SIFT," and "MI-Only." Our method compared to the first two feature-point based methods exhibits 1.25 (ex vivo microscope dataset) to two times (in vivo retina dataset) rate of mosaic completion, and MI-Only has the lowest complete rate among three datasets. When comparing the subsequent complete mosaics, our target registration errors can be 2.2 and 3.8 times reduced when using the microscopy and bladder phantom datasets. Conclusions: Using dimensional reduction increases the success rate of detecting adjacent images, which makes MI-based registration feasible and narrows the search range of MI optimization. To the best of our knowledge, this is the first mosaicking method that allows automatic stitching of disordered images with intensity-based alignment, which provides more robust and accurate results when there are insufficient features for classic mosaicking methods.

Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy.

Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6weekly CBCTs). The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83±0.09, 0.82±0.08, and 0.81±0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81±0.06 and 0.85±0.02. We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.

Spatial information transfer in hippocampal place cells depends on trial-to-trial variability, symmetry of place-field firing, and biophysical heterogeneities

The relationship between the feature-tuning curve and information transfer profile of individual neurons provides vital insights about neural encoding. However, the relationship between the spatial tuning curve and spatial information transfer of hippocampal place cells remains unexplored. Here, employing a stochastic search procedure spanning thousands of models, we arrived at 127 conductance-based place-cell models that exhibited signature electrophysiological characteristics and sharp spatial tuning, with parametric values that exhibited neither clustering nor strong pairwise correlations. We introduced trial-to-trial variability in responses and computed model tuning curves and information transfer profiles, using stimulus-specific (SSI) and mutual (MI) information metrics, across locations within the place field. We found spatial information transfer to be heterogeneous across models, but to reduce consistently with increasing levels of variability. Importantly, whereas reliable low-variability responses implied that maximal information transfer occurred at high-slope regions of the tuning curve, increase in variability resulted in maximal transfer occurring at the peak-firing location in a subset of models. Moreover, experience-dependent asymmetry in place-field firing introduced asymmetries in the information transfer computed through MI, but not SSI, and the impact of activity-dependent variability on information transfer was minimal compared to activity-independent variability. We unveiled ion-channel degeneracy in the regulation of spatial information transfer, and demonstrated critical roles for N-methyl-d-aspartate receptors, transient potassium and dendritic sodium channels in regulating information transfer. Our results demonstrate that trial-to-trial variability, tuning-curve shape and biological heterogeneities critically regulate the relationship between the spatial tuning curve and spatial information transfer in hippocampal place cells.

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

The Capacity of Single-Server Weakly-Private Information Retrieval

A private information retrieval (PIR) protocol guarantees that a user can privately retrieve files stored in a database without revealing any information about the identity of the requested file. Existing information-theoretic PIR protocols ensure perfect privacy, i.e., zero information leakage to the servers storing the database, but at the cost of high download. In this work, we present weakly-private information retrieval (WPIR) schemes that trade off perfect privacy to improve the download cost when the database is stored on a single server. We study the tradeoff between the download cost and information leakage in terms of mutual information (MI) and maximal leakage (MaxL) privacy metrics. By relating the WPIR problem to rate-distortion theory, the download-leakage function, which is defined as the minimum required download cost of all single-server WPIR schemes for a given level of information leakage and a fixed file size, is introduced. By characterizing the download-leakage function for the MI and MaxL metrics, the capacity of single-server WPIR is fully described.

Semi-automatic quantification of aortic root progressive dilation by automatic co-registration of computed tomography angiograms: a preliminary comparison with manual assessment in Marfan patients

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Spanish Ministry of Science, Innovation and Universities Instituto de Salud Carlos III Background. Dilation of the aortic root is a key feature of Marfan syndrome and it is related to the occurrence of aortic events and death. On top of maximum diameter, rapid annual growth rate is suggested by guidelines for indication of aortic root replacement. Current gold-standard for aortic root diameter assessment is manual quantification on multiplanar reformatted 3D computed tomography (CT) or magnetic resonance angiogram. However, inter- and intra-observer reproducibility are limited and different measurement methods, i.e. cusp-to-cusp and cusp-to-commissure, may be used in different clinical centres, leading to difficulties in the clinical assessment of progressive dilation. Purpose. We aimed to test whether aortic root growth rate during follow-up can be reliably quantified by semi-automatic co-registration of two CT angiograms. Methods. Seven Marfan syndrome patients, free from previous aortic surgery, with a total of 11 pairs of CT were identified. Manual assessment of six aortic root diameters (right-non coronary -RN- , right-left -RL- and left-non coronary -LN- cusp-to-cusp and R, L and N cusp-to-commissure) was obtained from all CTs by an experienced researcher blind to semi-automatic results. The thoracic aorta and the outflow tract were semi-automatically segmented in the baseline CT and commissure and cusps were manually located. A 10 mm-thick region of interest containing the aortic wall was automatically generated from segmentation boundary. Co-registration was obtained with three, fully-automatic steps. Firstly, baseline and follow-up CT scans were aligned by means of a rigid registration. Then, scans were co-registered with multi-resolution affine followed by b-spline non-rigid registrations based on mutual information metric. The transformation pertaining to the location of baseline commissure and cusps points was used to locate the same points in the follow-up scan (Fig. 1 top). Results. Follow-up duration was 35 ± 22 (range 12-70.3) months. Automatic quantification of diameter growth during the follow-up was obtained in 62 out of 66 (94%) diameter comparisons. High Pearson correlation coefficients (R) and ICC were found between manual and semi-automatic assessment of growth rate, both for cusp-to-cusp and cusp-to-commissure diameters: R = 0.727 and ICC = 0.678 for RN; R = 0.822 and ICC = 0.602 for RL; R = 0.648 and ICC = 0.668 for LN; R = 0.726 and ICC = 0.711 for R; R = 0.911 and ICC = 0.895 for L and R = 0.553 and ICC = 0.482 for N. Scatter and Bland-Altman plots for all growth rates (Fig. 1) confirmed very good correlation (R = 0.810) but a slight tendency (R=-0.270) for underestimation at high growth rate. No correlation was found between follow-up duration and difference between techniques (R = 0.06). Conclusions. Semi-automatic quantification of aortic root growth rate by co-registration of pairs of CT angiograms is feasible for follow-up as short as one year. Larger studies are needed to confirm these preliminary data. Abstract Figure. CT measurements. Automatic vs manual.

Clustering Introductory Computer Science Exercises Using Topic Modeling Methods

Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines are required. We investigated unsupervised semantic models (known as topic modeling techniques) to assist computer science teachers in this task and propose a method to transform Computer Science 1 teacher-provided code solutions into representative text documents, including the code structure information. By applying non-negative matrix factorization and latent Dirichlet allocation techniques, we extract the underlying relationship between questions and validate the results using an external dataset. We consider the interpretability of the learned concepts using 14 university professors' data, and the results confirm six semantically coherent clusters using the current dataset. Moreover, the six topics comprise the main concepts present in the test dataset, achieving 0.75 in the normalized pointwise mutual information metric. The metric correlates with human ratings, making the proposed method useful and providing semantics for large amounts of unannotated code.

Adaptive Radar Waveform Design Based on Weighted MI and the Difference of Two Mutual Information Metrics

This study deals with the problem of radar waveform design based on the weighted mutual information (MI) and the difference of two mutual information metrics (DMI) in signal-dependent interference. Since the target and clutter information are included in the received signal at the beginning of the design, DMI-based waveform is designed according to the following criterion: maximizing the MI between the received signal and target impulse response while minimizing the MI between the received signal and the clutter impulse response. This criterion is equivalent to maximizing the difference between the first MI and the second MI. Then maximizing the difference of two types of MI is used as the objective function, and the optimization model with the transmitted waveform energy constraint is established. In order to solve it, we resort to maximum marginal allocation (MMA) method to find the DMI-based waveform. Since DMI-based waveform does not allocate energy to the frequency band where the clutter power spectral density (PSD) is greater than the target PSD, we propose to weight the MI-based waveform and DMI-based waveform to synthesize the final optimal waveform. It could provide different trade-offs between two types of MI. Simulation results show the proposed algorithm is valid.

Are mom-and-pop and professional hosts actually competing against hotels?

Purpose This paper aims to examine the question of whether commercial, peer-to-peer accommodation platforms (Airbnb, in particular) and hotels are in fierce competition with each other with the possible presence of substitution threats, and compares the time series of the occupancy values across two supplier types. Design/methodology/approach The cities of Milan and Rome are used as case studies for this analysis. To assess the extent of synchronization, the series of Airbnb and hotels are transformed into a series of symbols that render their rhythmic behavior, and a mutual information metric is used to measure the effect. Findings The results show that Airbnb hosts and hotels have different seasonal patterns. The diverse occupancy trends support the absence of direct competition between Airbnb and hotels. The findings are consistent in the two analyzed cities (Milan and Rome). Interestingly, there are higher similarities between seasonal occupancy series of Airbnb listings in Milan and Rome, on one side, and hotels in Milan and Rome, on the other, than between Airbnb and hotels in the same city. Research limitations/implications The findings show a progressive de-synchronization (within mutual information) among the five groups of Airbnb hosts triggered by the rising professionalization degree. This result suggests the existence of a partial different business model for multi-listing hosts. Practical implications The study illustrates an absence of any substitution threat between Airbnb and hotels in both cities. This could have important consequences, especially for the pricing and revenue management policy. In fact, the higher the substitution threat, the higher the attention that Airbnb entrepreneurs should pay to the pricing strategy implemented by hotels, and vice versa. Originality/value This study sheds new light on the competition threat between Airbnb and hotels. In this study, hotels and Airbnb hosts appear as two very separate markets.

Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

Channel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink channel is firstly formulated as a two dimensional (2D) separable compressive sensing (CS) model according to the sparsity structure of the channel in angle of arrivals (AoAs) and angle of departures (AoDs) domains. Then a separable compressive sampling match pursuit (SCoSaMP) algorithm is proposed to solve the separable CS recovery problem for channel estimation. Based on the separable sparsity structure of the channel, we design the precoding and combining matrices under the metric of mutual information to further improve the performance of channel estimation. Simulations demonstrate the advantages of the proposed algorithm over the traditional CS-based channel estimation methods.

Feature grouping and selection: A graph-based approach

Most current feature selection techniques are focused on the incremental inclusion or exclusion of single individual features with respect to the candidate feature subset(s). The use of such approaches, where only the individual inclusion/exclusion of features is considered, means that information such as the collaborative contribution or correlation between features may be lost. The result is that the final selected feature subset may contain high levels of inter-feature redundancy, assuming that the key information embedded in the original feature set can still be retained. To address this problem, a general framework based on graph processing and three-way mutual information metrics is proposed in this paper that works by clustering similar features into groups, from which representative features are then drawn. Two different feature selection techniques based on this framework are presented: one by straightforward selection of representative features from the resulting feature groups and the other via a music-inspired metaheuristic search. Comparative experimental evaluation against traditional feature selection techniques over a diverse range of 20 benchmark datasets demonstrates the efficacy of the proposed approach. With these implementations, significant performance gains can be made in terms of classification accuracy in general and dimensionality reduction in particular while retaining feature semantics and considerably lessening the redundancy in the returned feature subsets.

Thermonuclear Reaction Rates and Primordial Nucleosynthesis

Abstract Assuming the best numerical value for the cosmic baryonic density and the existence of three neutrino flavors, standard Big Bang nucleosynthesis is a parameter-free model. It is important to assess if the observed primordial abundances can be reproduced by simulations. Numerous studies have shown that the simulations overpredict the primordial 7Li abundance by a factor of compared to the observations. The discrepancy may be caused by unknown systematics in 7Li observations, poorly understood depletion of lithium in stars, errors in thermonuclear rates that take part in the lithium and beryllium synthesis, or physics beyond the standard model. Here, we focus on the likelihood of a nuclear physics solution. The status of the key nuclear reaction rates is summarized. Big Bang nucleosynthesis simulations are performed with the most recent reaction rates, and the uncertainties of the predicted abundances are established using a Monte Carlo technique. Correlations between abundances and reaction rates are investigated based on the metric of mutual information. The rates of four reactions impact the primordial 7Li abundance: 3He(α,γ)7Be, d(p,γ)3He, 7Be(d,p)2α, and 7Be(n,p)7Li. We employ a genetic algorithm to search for simultaneous rate changes in these four reactions that may account for all observed primordial abundances. When the search is performed for reaction rate ranges that are much wider than recently reported uncertainties, no acceptable solutions are found. Based on the currently available evidence, we conclude that it is highly unlikely for the cosmological lithium problem to have a nuclear physics solution.

FSMI: Fast computation of Shannon mutual information for information-theoretic mapping

Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informative trajectories by maximizing an information-theoretic metric, such as the mutual information between the map and potential future measurements. Unfortunately, most existing information-based exploration algorithms are plagued by the computational difficulty of evaluating the Shannon mutual information metric. In this article, we consider the fundamental problem of evaluating Shannon mutual information between the map and a range measurement. First, we consider 2D environments. We propose a novel algorithm, called the fast Shannon mutual information (FSMI). The key insight behind the algorithm is that a certain integral can be computed analytically, leading to substantial computational savings. Second, we consider 3D environments, represented by efficient data structures, e.g., an OctoMap, such that the measurements are compressed by run-length encoding (RLE). We propose a novel algorithm, called FSMI-RLE, that efficiently evaluates the Shannon mutual information when the measurements are compressed using RLE. For both the FSMI and the FSMI-RLE, we also propose variants that make different assumptions on the sensor noise distribution for the purpose of further computational savings. We evaluate the proposed algorithms in extensive experiments. In particular, we show that the proposed algorithms outperform existing algorithms that compute Shannon mutual information as well as other algorithms that compute the Cauchy–Schwarz quadratic mutual information (CSQMI). In addition, we demonstrate the computation of Shannon mutual information on a 3D map for the first time.

Link community detection based on ensemble learning

Overlapping community detection is a hot topic in the research of data mining and graph theory. In this paper, we propose a link community detection method based on ensemble learning (LCDEL). First, we transform graph into line graph and construct node adjacency matrix of line graph. Second, we calculate node distance of line graph through a new distance metric and get node distance matrix of line graph. Third, we use PCA method to reduce dimensions of node distance matrix of line graph. Then, we cluster on the reduced node distance matrix by k-means clustering algorithm. Finally, we convert line graph back into original graph and get overlapping communities of original graph with ensemble learning. Experimental results on several real-world networks demonstrate effectiveness of LCDEL method in terms of Normalized Mutual Information (NMI), Extended Modularity (EQ) and F-score evaluation metrics.

PND83 IDENTIFYING NEUROMYELITIS OPTICA PATIENTS FROM INSURANCE CLAIMS DATA USING NFERX, A NATURAL LANGUAGE PROCESSING-BASED PLATFORM

Neuromyelitis optica spectrum disorder (NMO) is a rare auto-immune disease affecting the central nervous system and has clinical characteristics similar to multiple sclerosis (MS), making clinical diagnosis challenging. This study aims to improve the identification of NMO patients by leveraging nferX, a natural language processing platform, to enable encoding of patient claims that are specific to NMO versus MS. Disease phenotypes associated with NMO or MS were identified from the biomedical literature using the nferX platform. Patients with at least 1 NMO or MS diagnosis code were identified in the IQVIA Pharmetrics Plus database. Accompanying diagnosis, procedure, and drug codes present in greater than 0.1% of patients and at least 2-fold more prevalent in either cohort were mapped to nferX-identified phenotypes. These phenotypes’ relative association with NMO versus MS were quantified using nferX’s adaptation of the pointwise mutual information metric. Associations were aggregated into a summary score for each patient from the number of corresponding claims in the year preceding the initial NMO or MS diagnosis claim. Separation of scores between cohorts of NMO and MS patients identified with existing standard algorithms was assessed. A total of 307,166 patients with at least 1 NMO or MS claim between 2006-2018 were identified. 3,132 NMO patients and 102,611 MS patients were selected using previously published algorithms. The Cohen’s d separation of summary scores between these two cohorts was 1.50, suggesting significant separation. By applying a cutoff of one standard deviation above the mean summary score, 14,822 NMO patients were identified, including 13,150 patients without any claims for NMO. Use of the nferX platform has the potential to improve identification of NMO patients from claims data. Further validation of the approach is needed to support its future use.

An overlapping community detection algorithm based on node distance of line graph

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.

Complex Dual Tree Wavelet Multi-Level Feature Based Transformation Parameter Estimation for 3d Medical Image Registration

3D image registration of CT and MRI data is carried out using DTCWT sub bands by considering the features from all 64 bands. The features are selected by considering Mattes Mutual Information Metric and the optimizer algorithm estimates the optimum transformation parameters from all the 64 bands. Transformation parameters from eight low pass bands from each octave are averaged to identify optimum registration parameters. Similarly, for registration of high pass bands mean of transformation parameters from 56 bands are identified. The proposed registration algorithm is suitable for register multimodal medical images and the proposed algorithm is validated for more than 20 3D images. Mutual information and joint entropy is estimated to demonstrate the advantages of proposed algorithm overt that of intensity based algorithm. With features identified from 56 bands with six orientations the registered image is found to consist of features from both input images with closeness level measured to be within 12%.