Partial 3D Shape Retrieval by Reeb Pattern Unfolding

This paper presents a novel approach for fast and efficient partial shape retrieval on a collection of 3D shapes. Each shape is represented by a Reeb graph associated with geometrical signatures. Partial similarity between two shapes is evaluated by computing a variant of their maximum common sub-graph.
By investigating Reeb graph theory, we take advantage of its intrinsic properties at two levels. First, we show that the segmentation of a shape by a Reeb graph provides charts with disk or annulus topology only. This topology control enables the computation of concise and efficient sub-part geometrical signatures based on parameterisation techniques. Secondly, we introduce the notion of Reeb pattern on a Reeb graph along with its structural signature. We show this information discards Reeb graph structural distortion and still depicts the topology of the related sub-parts. The number of combinations to evaluate in the matching process is then dramatically reduced by only considering the combinations of topology equivalent Reeb patterns.
The proposed framework is invariant against rigid transformations and robust against non-rigid transformations and surface noise. It queries the collection in interactive time (from 4 to 30 seconds for the largest queries). It outperforms the competing methods of the SHREC 2007 contest in term of NDCG vector and provides, respectively, a gain of 14.1% and 40.9% on the approaches by Biasotti et al. [ BMSF06 ] and Cornea et al. [ CDS*05 ].
As an application, we present an intelligent modelling-by-example system which enables a novice user to rapidly create new 3D shapes by composing shapes of a collection having similar sub-parts.     

The role of algorithm and result comprehensibility of automated scheduling on complacency

Several studies have stressed that even expert operators who are aware of a machine's limits could adopt its proposals without questioning them (i.e., the complacency phenomenon). In production scheduling for manufacturing, this is a significant problem, as it is often suggested that the machine be allowed to build the production schedule, confining the human role to that of rescheduling. This article evaluates the characteristics of scheduling algorithms on human rescheduling performance, the quality of which was related to complacency. It is suggested that scheduling algorithms be characterized as having result comprehensibility (the result respects the scheduler's expectations in terms of the discourse rules of the information display) or algorithm comprehensibility (the complexity of the algorithm hides some important constraints). The findings stress, on the one hand, that result comprehensibility is necessary to achieve good production performance and to limit complacency. On the other hand, algorithm comprehensibility leads to poor performance due to the very high cost of understanding the algorithm. © 2008 Wiley Periodicals, Inc.     

An efficient and generic extension to ITK to process arbitrary shaped regions of interest

The paper describes a software method to extend ITK (Insight ToolKit, supported by the National Library of Medicine), leading to ITK++. This method, which is based on the extension of the iterator design pattern, allows the processing of regions of interest with arbitrary shapes, without modifying the existing ITK code. We experimentally evaluate this work by considering the practical case of the liver vessel segmentation from CT-scan images, where it is pertinent to constrain processings to the liver area. Experimental results clearly prove the interest of this work: for instance, the anisotropic filtering of this area is performed in only 16 s with our proposed solution, while it takes 52 s using the native ITK framework. A major advantage of this method is that only add-ons are performed: this facilitates the further evaluation of ITK++ while preserving the native ITK framework.     

Audio indexing: primary components retrieval

This work addresses the soundtrack indexing of multimedia documents. Our purpose is to detect and locate sound unity to structure the audio dataflow in program broadcasts (reports). We present two audio classification tools that we have developed. The first one, a speech music classification tool, is based on three original features: entropy modulation, stationary segment duration (with a Forward–Backward Divergence algorithm) and number of segments. They are merged with the classical 4 Hz modulation energy. It is divided into two classifications (speech/non-speech and music/non-music) and provides more than 90% of accuracy for speech detection and 89% for music detection. The other system, a jingle identification tool, uses an Euclidean distance in the spectral domain to index the audio data flow. Results show that is efficient: among 132 jingles to recognize, we have detected 130. Systems are tested on TV and radio corpora (more than 10 h). They are simple, robust and can be improved on every corpus without training or adaptation.

Fast attack detection using correlation and summarizing of security alerts in grid computing networks

Due to the extensive growth of grid computing networks, security is becoming a challenge. Usual solutions are not enough to prevent sophisticated attacks fabricated by multiple users especially when the number of nodes connected to the network is changing over the time. Attackers can use multiple nodes to launch DDoS attacks which generate a large amount of security alerts. On the one hand, this large number of security alerts degrades the overall performance of the network and creates instability in the operation of the security management solutions. On the other hand, they can help in camouflaging other real attacks. To address these issues, a?correlation mechanism is proposed which reduces the security alerts and continue detecting attacks in grid computing networks. To obtain the more accurate results, a?major portion of the experiments are performed by launching DDoS and Brute Force (BF) attacks in real grid environment, i.e., the Grid??5000 (G5K) network.     

Shear Stress in Smooth Rectangular Open-Channel Flows

The average bed and sidewall shear stresses in smooth rectangular open-channel flows are determined after solving the continuity and momentum equations. The analysis shows that the shear stresses are function of three components: (1) gravitational; (2) secondary flows; and (3) interfacial shear stress. An analytical solution in terms of series expansion is obtained for the case of constant eddy viscosity without secondary currents. In comparison with laboratory measurements, it slightly overestimates the average bed shear stress measurements but underestimates the average sidewall shear stress by 17% when the width–depth ratio becomes large. A second approximation is formulated after introducing two empirical correction factors. The second approximation agrees very well (R2>0.99 and average relative error less than 6%) with experimental measurements over a wide range of width–depth ratios.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

虚拟群体与动态视频场景的在线实时融合

将虚拟人群与实拍视频场景融合实现虚拟人群对现实场景中动态目标的合理避让,是增强现实技术有待解决的一个新问题.文中首先对实拍视频流进行分析,获取真实动态目标在三维空间中的运动信息;然后将动态目标的运动转化为每个虚拟角色的约束条件,实时更新虚拟角色的运动决策,实现其对现实场景中动态目标的避让以及虚拟角色之间的避让.为了清晰地展示现实世界中多个目标的空间位置关系,还对视频画面中的目标体做了精确的实时分割,以产生正确的遮挡关系和目标定位.最后通过实验结果表明了该算法的有效性.     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.