Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201

The effects of frequent nocturnal home hemodialysis: the Frequent Hemodialysis Network Nocturnal Trial

Prior small studies have shown multiple benefits of frequent nocturnal hemodialysis compared to conventional three times per week treatments. To study this further, we randomized 87 patients to three times per week conventional hemodialysis or to nocturnal hemodialysis six times per week, all with single-use high-flux dialyzers. The 45 patients in the frequent nocturnal arm had a 1.82-fold higher mean weekly stdKt/Vurea, a 1.74-fold higher average number of treatments per week, and a 2.45-fold higher average weekly treatment time than the 42 patients in the conventional arm. We did not find a significant effect of nocturnal hemodialysis for either of the two coprimary outcomes (death or left ventricular mass (measured by MRI) with a hazard ratio of 0.68, or of death or RAND Physical Health Composite with a hazard ratio of 0.91). Possible explanations for the left ventricular mass result include limited sample size and patient characteristics. Secondary outcomes included cognitive performance, self-reported depression, laboratory markers of nutrition, mineral metabolism and anemia, blood pressure and rates of hospitalization, and vascular access interventions. Patients in the nocturnal arm had improved control of hyperphosphatemia and hypertension, but no significant benefit among the other main secondary outcomes. There was a trend for increased vascular access events in the nocturnal arm. Thus, we were unable to demonstrate a definitive benefit of more frequent nocturnal hemodialysis for either coprimary outcome

Impact of COVID-19 Pandemic on TAVR Activity: A Worldwide Registry

Background: The COVID-19 pandemic had a considerable impact on the provision of structural heart intervention worldwide. Our objectives were: 1) to assess the impact of the COVID-19 pandemic on transcatheter aortic valve replacement (TAVR) activity globally; and 2) to determine the differences in the impact according to geographic region and the demographic, development, and economic status of diverse international health care systems. Methods: We developed a multinational registry of global TAVR activity and invited individual TAVR sites to submit TAVR implant data before and during the COVID-19 pandemic. Specifically, the number of TAVR procedures performed monthly from January 2019 to December 2021 was collected. The adaptive measures to maintain TAVR activity by each site were recorded, as was a variety of indices relating to type of health care system and national economic indices. The primary subject of interest was the impact on TAVR activity during each of the pandemic waves (2020 and 2021) compared with the same period pre–COVID-19 (2019). Results: Data were received from 130 centers from 61 countries, with 14 subcontinents and 5 continents participating in the study. Overall, TAVR activity increased by 16.7% (2,337 procedures) between 2018 and 2019 (ie, before the pandemic), but between 2019 and 2020 (ie, first year of the pandemic), there was no significant growth (–0.1%; –10 procedures). In contrast, activity again increased by 18.9% (3,085 procedures) between 2020 and 2021 (ie, second year of the pandemic). During the first pandemic wave, there was a reduction of 18.9% (945 procedures) in TAVR activity among participating sites, while during the second and third waves, there was an increase of 6.7% (489 procedures) and 15.9% (1,042 procedures), respectively. Further analysis and results of this study are ongoing and will be available at the time of the congress. Conclusion: The COVID-19 pandemic initially led to a reduction in the number of patients undergoing TAVR worldwide, although health care systems subsequently adapted, and the number of TAVR recipients continued to grow in subsequent COVID-19 pandemic waves. Categories: STRUCTURAL: Valvular Disease: Aorti

MAP model order selection rule for 2-D sinusoids in white noise

We consider the problem of jointly estimating the number as well as the parameters of two-dimensional (2-D) sinusoidal signals, observed in the presence of an additive white Gaussian noise field. Existing solutions to this problem are based on model order selection rules and are derived for the parallel one-dimensional (1-D) problem. These criteria are then adapted to the 2-D problem using heuristic arguments. Employing asymptotic considerations, we derive a maximum a posteriori (MAP) model order selection criterion for jointly estimating the parameters of the 2-D sinusoids and their number. The proposed model order selection rule is strongly consistent. As an example, the model order selection criterion is applied as a component in an algorithm for parametric estimation and synthesis of textured images