06311 Abstracts Collection -- Sensor Data and Information Fusion in Computer Vision and Medicine

From 30.07.06 to 04.08.06, the Dagstuhl Seminar 06311 ``Sensor Data and Information Fusion in Computer Vision and Medicine\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. Sensor data fusion is of increasing importance for many research fields and applications. Multi-modal imaging is routine in medicine, and in robitics it is common to use multi-sensor data fusion. During the seminar, researchers and application experts working in the field of sensor data fusion presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. The second part briefly summarizes the contributions

06311 Executive Summary -- Sensor Data and Information Fusion in Computer Vision and Medicine

Today many technical systems are equipped with multiple sensors and information sources, like cameras, ultrasound sensors or web data bases. It is no problem to generate an exorbitantly large amount of data, but it is mostly unsolved how to take advantage of the expectation that the collected data provide more information than the sum of its parts. The design and analysis of algorithms for sensor data and information acquisition and fusion as well as the usage in a differentiated application field was the major focus of the Seminar held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. 24 researchers, practitioners, and application experts from different areas met to summarize the current state-of-the-art technology in data and information fusion, to discuss current research problems in fusion, and to envision future demands of this challenging research field. The considered application scenarios for data and information fusion were in the fields of computer vision and medicine

Pitch determination considering laryngealization effects in spoken dialogs

A frequent phenomenon in spoken dialogs of the information seeking type are short elliptic utterances whose mood (declarative or interrogative) can only be distinguished by intonation. The main acoustic evidence is conveyed by the fundamental frequency or Fo-contour. Many algorithms for Fo determination have been reported in the literature. A common problem are irregularities of speech known as "laryngealizations". This article describes an approach based on neural network techniques for the improved determination of fundamental frequency. First, an improved version of our neural network algorithm for reconstruction of the voice source signal (glottis signal) is presented. Second, the reconstructed voice source signal is used as input to another neural network distinguishing the three classes "voiceless", "voiced non-laryngealized", and "voiced laryngealized". Third, the results are used to improve an existing Fo algorithm. Results of this approach are presented and discussed in the context of the application in a spoken dialog system

Miravirsen (SPC3649) can inhibit the biogenesis of miR-122

MicroRNAs (miRNAs) are short noncoding RNAs, which bind to messenger RNAs and regulate protein expression. The biosynthesis of miRNAs includes two precursors, a primary miRNA transcript (pri-miRNA) and a shorter pre-miRNA, both of which carry a common stem-loop bearing the mature miRNA. MiR-122 is a liver-specific miRNA with an important role in the life cycle of hepatitis C virus (HCV). It is the target of miravirsen (SPC3649), an antimiR drug candidate currently in clinical testing for treatment of HCV infections. Miravirsen is composed of locked nucleic acid (LNAs) ribonucleotides interspaced throughout a DNA phosphorothioate sequence complementary to mature miR-122. The LNA modifications endow the drug with high affinity for its target and provide resistance to nuclease degradation. While miravirsen is thought to work mainly by hybridizing to mature miR-122 and blocking its interaction with HCV RNA, its target sequence is also present in pri- and pre-miR-122. Using new in vitro and cellular assays specifically developed to discover ligands that suppress biogenesis of miR-122, we show that miravirsen binds to the stem-loop structure of pri- and pre-miR-122 with nanomolar affinity, and inhibits both Dicer- and Drosha-mediated processing of miR-122 precursors. This inhibition may contribute to the pharmacological activity of the drug in ma

Active Perception using Light Curtains for Autonomous Driving

Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using light curtains, a resource-efficient controllable sensor that measures depth at user-specified locations in the environment. Crucially, we propose using prediction uncertainty of a deep learning based 3D point cloud detector to guide active perception. Given a neural network's uncertainty, we derive an optimization objective to place light curtains using the principle of maximizing information gain. Then, we develop a novel and efficient optimization algorithm to maximize this objective by encoding the physical constraints of the device into a constraint graph and optimizing with dynamic programming. We show how a 3D detector can be trained to detect objects in a scene by sequentially placing uncertainty-guided light curtains to successively improve detection accuracy. Code and details can be found on the project webpage: http://siddancha.github.io/projects/active-perception-light-curtains.Comment: Published at the European Conference on Computer Vision (ECCV), 202

Going back to the source : inverse filtering of the speech signal with ANNs

In this paper we present a new method transforming speech signals to voice source signals (VSS) using artificial neural networks (ANN). We will point out that the ANN mapping of speech signals into source signals is quite accurate, and most of the irregularities in the speech signal will lead to an irregularity in the source signal, produced by the ANN (ANN-VSS). We will show that the mapping of the ANN is robust with respect to untrained speakers, different recording conditions and facilities, and different vocabularies. We will also present preliminary results which show that from the ANN source signal pitch periods can be determined accurately

Personalised aesthetics with residual adapters

The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, user specific preferences over aesthetics in photography, while surpassing the state-of-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.Comment: 12 pages, 4 figures. In Iberian Conference on Pattern Recognition and Image Analysis proceeding

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

Myeloperoxidase gene-463G > A polymorphism and premature coronary artery disease

We investigated the association between myeloperoxidase gene -463G > A polymorphism and premature coronary artery disease (CAD) in two Chinese population samples: 229 patients and 230 controls. Genotypes were determined by ligase detection reaction-polymerase chain reaction sequencing and the grouping technique. We found lower frequencies of both the A/A genotype and the A allele in patients (p < 0.05). Multivariate logistic regression showed that the risk of premature CAD in subjects carrying the AA genotype was reduced by 83% in relation to individuals carrying the G/G genotype (OR = 0.172, 95% CI: 0.057-0.526, p = 0.002). Our results indicate that -463G > A polymorphism of the myeloperoxidase gene is associated with premature CAD in Chinese individuals, suggesting that the AA genotype is a protective factor against premature CAD