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"Seidl, Thomas"
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The ClusTree: indexing micro-clusters for anytime stream mining
Clustering streaming data requires algorithms that are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter-free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Additionally we present solutions to handle very fast streams through aggregation mechanisms and propose novel descent strategies that improve the clustering result on slower streams as long as time permits. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.
Journal Article
SF3B1 mutant MDS-initiating cells may arise from the haematopoietic stem cell compartment
Despite the recent evidence of the existence of myelodysplastic syndrome (MDS) stem cells in 5q-MDS patients, it is unclear whether haematopoietic stem cells (HSCs) could also be the initiating cells in other MDS subgroups. Here we demonstrate that
SF3B1
mutation(s) in our cohort of MDS patients with ring sideroblasts can arise from CD34
+
CD38
−
CD45RA
−
CD90
+
CD49f
+
HSCs and is an initiating event in disease pathogenesis. Xenotransplantation of
SF3B1
mutant HSCs leads to persistent long-term engraftment restricted to myeloid lineage. Moreover, genetically diverse evolving subclones of mutant
SF3B1
exist in mice, indicating a branching multi-clonal as well as ancestral evolutionary paradigm. Subclonal evolution in mice is also seen in the clinical evolution in patients. Sequential sample analysis shows clonal evolution and selection of the malignant driving clone leading to AML transformation. In conclusion, our data show
SF3B1
mutations can propagate from HSCs to myeloid progeny, therefore providing a therapeutic target.
Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders with diverse phenotypes and can derive from hematopietic stem cells after the acquisition of specific somatic aberrations. In this study, the authors show that MDS initiating cells in some cases of sideroblastic anemia with SF3B1 mutations, can arise from hematopoietic stem cells.
Journal Article
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change
This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFSE can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of a glacier. The problem size and complexity were chosen to reflect the challenges posed by future continental-scale studies while still facilitating a computationally feasible investigation of MFSE methods. When quantifying uncertainty introduced by a high-dimensional parameterization of the basal friction field, MFSE was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single-fidelity approach that only used the highest-fidelity model. This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice-flow fields predicted by the high-fidelity model. The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. Consequently, our results suggest that MFSE could be highly useful for reducing the cost of computing continental-scale probabilistic projections of sea-level rise due to ice-sheet mass change.
Journal Article
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS
OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapidly changing applications, a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time.
Journal Article
Using internal evaluation measures to validate the quality of diverse stream clustering algorithms
Measuring the quality of a clustering algorithm has shown to be as important as the algorithm itself. It is a crucial part of choosing the clustering algorithm that performs best for an input data. Streaming input data have many features that make them much more challenging than static ones. They are endless, varying and emerging with high speeds. This raised new challenges for the clustering algorithms as well as for their evaluation measures. Up till now, external evaluation measures were exclusively used for validating stream clustering algorithms. While external validation requires a ground truth which is not provided in most applications, particularly in the streaming case, internal clustering validation is efficient and realistic. In this article, we analyze the properties and performances of eleven internal clustering measures. In particular, we apply these measures to carefully synthesized stream scenarios to reveal how they react to clusterings on evolving data streams using both
k
-means-based and density-based clustering algorithms. A series of experimental results show that different from the case with static data, the
Calinski-Harabasz index
performs the best in coping with common aspects and errors of stream clustering for
k
-means-based algorithms, while the
revised validity index
performs the best for density-based ones.
Journal Article
Estimation and validation of spatio-temporal parameters for sprint running using a radio-based tracking system
Spatio-temporal parameters like step length, step frequency and ground contact time are directly related to sprinting performance. There is still a lack of knowledge, however, on how these parameters interact.
Recently, various algorithms for the automatic detection of step parameters during sprint running have been presented which have been based on data from motion capture systems, video cameras, opto-electronic systems or Inertial measurement units. However, all of these methods suffer from at least one of the following shortcomings: they are (a) not applicable for more than one sprinter simultaneously, (b) only capable of capturing a small volume or (c) do not provide accurate spatial parameters. To circumvent these issues, the radio-based local position measurement system RedFIR could be used to obtain spatio-temporal information during sprinting based on lightweight transmitters attached to the athletes. To assess and optimize the accuracy of these parameters 19 100 m sprints of twelve young elite athletes (age: 16.5 ± 2.3 years) were recorded by a radio-based tracking system and a opto-electronic reference instrument. Optimal filter parameters for the step detection algorithm were obtained based on RMSE differences between estimates and reference values on an unseen test set. Attaching a transmitter above the ankle showed the best results.
Bland-Altman analysis yielded 95% limits of agreement of [−14.65 cm, 15.05 cm] for step length [−0.016 s, 0.016 s] for step time and [−0.020 s, 0.028 s] for ground contact time, respectively. RMS errors smaller than 2% for step length and step time show the applicability of radio-based tracking systems to provide spatio-temporal parameters. This creates new opportunities for performance analysis that can be applied for any running discipline taking place within a stadium. Since analysis for multiple athletes is available in real-time this allows immediate feedback to coaches, athletes and media.
Journal Article
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study
When researchers publish new cluster algorithms, they usually demonstrate the strengths of their novel approaches by comparing the algorithms’ performance with existing competitors. However, such studies are likely to be optimistically biased towards the new algorithms, as the authors have a vested interest in presenting their method as favorably as possible in order to increase their chances of getting published. Therefore, the superior performance of newly introduced cluster algorithms is over-optimistic and might not be confirmed in independent benchmark studies performed by neutral and unbiased authors. This problem is known among many researchers, but so far, the different mechanisms leading to over-optimism in cluster algorithm evaluation have never been systematically studied and discussed. Researchers are thus often not aware of the full extent of the problem. We present an illustrative study to illuminate the mechanisms by which authors—consciously or unconsciously—paint their cluster algorithm’s performance in an over-optimistic light. Using the recently published cluster algorithm Rock as an example, we demonstrate how optimization of the used datasets or data characteristics, of the algorithm’s parameters and of the choice of the competing cluster algorithms leads to Rock’s performance appearing better than it actually is. Our study is thus a cautionary tale that illustrates how easy it can be for researchers to claim apparent “superiority” of a new cluster algorithm. This illuminates the vital importance of strategies for avoiding the problems of over-optimism (such as, e.g., neutral benchmark studies), which we also discuss in the article.
Journal Article
The ClasSi coefficient for the evaluation of ranking quality in the presence of class similarities
Evaluationmeasures play an important role in the design of new approaches, and often quality is measured by assessing the relevance of the obtained result set.While many evaluation measures based on precision/recall are based on a binary relevance model, ranking correlation coefficients are better suited for multi-class problems. State-of-the-art ranking correlation coefficients like Kendall's τ and Spearman's ρ do not allow the user to specify similarities between differing object classes and thus treat the transposition of objects from similar classes the same way as that of objects from dissimilar classes. We propose ClasSi, a new ranking correlation coefficient which deals with class label rankings and employs a class distance function to model the similarities between the classes. We also introduce a graphical representation of ClasSi which describes how the correlation evolves throughout the ranking.
Journal Article
Biomechanical imaging of cell stiffness and prestress with subcellular resolution
Knowledge of cell mechanical properties, such as elastic modulus, is essential to understanding the mechanisms by which cells carry out many integrated functions in health and disease. Cellular stiffness is regulated by the composition, structural organization, and indigenous mechanical stress (or prestress) borne by the cytoskeleton. Current methods for measuring stiffness and cytoskeletal prestress of living cells necessitate either limited spatial resolution but with high speed, or spatial maps of the entire cell at the expense of long imaging times. We have developed a novel technique, called biomechanical imaging, for generating maps of both cellular stiffness and prestress that requires less than 30 s of interrogation time, but which provides subcellular spatial resolution. The technique is based on the ability to measure tractions applied to the cell while simultaneously observing cell deformation, combined with capability to solve an elastic inverse problem to find cell stiffness and prestress distributions. We demonstrated the application of this technique by carrying out detailed mapping of the shear modulus and cytoskeletal prestress distributions of 3T3 fibroblasts, making no assumptions regarding those distributions or the correlation between them. We also showed that on the whole cell level, the average shear modulus is closely associated with the average prestress, which is consistent with the data from the literature. Data collection is a straightforward procedure that lends itself to other biochemical/biomechanical interventions. Biomechanical imaging thus offers a new tool that can be used in studies of cell biomechanics and mechanobiology where fast imaging of cell properties and prestress is desired at subcellular resolution.
Journal Article