Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.Comment: Follow-up to arXiv:0706.3375. Journal submission 9 Jul 2007. Published 19 Dec 200

Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models

Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this paper, a novel approach is presented to globally model an OF combined with locally adaptive methods. We show that this model adapts perfectly to the 'true OF' in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution we describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and we establish the main underlying claim of high fidelity low parameter OF compression

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available

The activating receptors 2B4 and NTB-A, but not CRACC are subject to ligand-induced down-regulation on human natural killer cells

Activation of natural killer cells can be mediated by different receptors. Stimulation of the receptors 2B4, NTB-A and CRACC, members of the SLAM-related receptor family, induces cytotoxicity and cytokine production. The surface expression of 2B4 and other activating natural killer cell receptors is down-modulated after receptor engagement, which results in a weaker response to consecutive stimulation. We tested whether this regulatory mechanism applies to all SLAM-related receptors expressed by primary human natural killer cells. After co-culture with target cells expressing the respective ligands different effects on receptor surface expression were observed. While 2B4 ex-pression was strongly reduced, NTB-A showed less prominent down-modulation and the expression level of CRACC remained unchanged. The expression levels of the receptor-proximal signaling molecules SAP, EAT-2 and FynT did not change after receptor engagement. Co-culture with target cells expressing the ligands for NTB-A or CRACC had no impact on subsequent NTB-A or CRACC-mediated NK cell activation

International Synchronisation of the Pork Cycle

International Relations/Trade, Livestock Production/Industries,

Trade, market integration and spatial price transmission on EU pork markets following Eastern enlargement

The accession of ten countries to the EU in May 2004, and of Bulgaria and Romania in January 2007, eliminated barriers to trade between old and new, and among new member states. We analyse the effects of this accession on the integration of pork markets in the EU. Our results show that the speed of price transmission is positively related to the volume of pork trade between two countries. Our results also reveal that intra-regional price transmission between old or between new member states is more rapid than inter-regional price transmission between old and new member states, and that producer prices in the new member states adjust more rapidly to price changes in the old member states than vice versa. Price transmission is also more rapid between Euro-zone members and member states that share a common border. Finally, our results show that the strengths of these effects have changed in predictable ways in the years since accession took place, as a single, increasingly integrated European pork market has evolved

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.Comment: 32 pages, 21 figure

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

Within five to ten years after radical prostatectomy (RP), approximately 15-34% of prostate cancer (PCa) patients experience biochemical recurrence (BCR), which is defined as recurrence of serum levels of prostate-specific antigen >0.2 µg/L, indicating probable cancer recurrence. Models using clinicopathological variables for predicting this risk for patients lack accuracy. There is hope that new molecular biomarkers, like microRNAs (miRNAs), could be potential candidates to improve risk prediction. Therefore, we evaluated the BCR prognostic capability of 20 miRNAs, which were selected by a systematic literature review. MiRNA expressions were measured in formalin-fixed, paraffin-embedded (FFPE) tissue RP samples of 206 PCa patients by RT-qPCR. Univariate and multivariate Cox regression analyses were performed, to assess the independent prognostic potential of miRNAs. Internal validation was performed, using bootstrapping and the split-sample method. Five miRNAs (miR-30c-5p/31-5p/141-3p/148a-3p/miR-221-3p) were finally validated as independent prognostic biomarkers. Their prognostic ability and accuracy were evaluated using C-statistics of the obtained prognostic indices in the Cox regression, time-dependent receiver-operating characteristics, and decision curve analyses. Models of miRNAs, combined with relevant clinicopathological factors, were built. The five-miRNA-panel outperformed clinically established BCR scoring systems, while their combination significantly improved predictive power, based on clinicopathological factors alone. We conclude that this miRNA-based-predictor panel will be worth to be including in future studies

Inductive Theorem Proving meets Dependency Pairs

Current techniques and tools for automated termination analysis of term rewrite systems (TRSs) are already very powerful. However, they fail for algorithms whose termination is essentially due to an inductive argument. Therefore, we show how to couple the dependency pair method for TRS termination with inductive theorem proving. As confirmed by the implementation of our new approach in the tool AProVE, now TRS termination techniques are also successful on this important class of algorithms