1,107 research outputs found

    Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

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    <p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

    IPTV 2.0 from Triple Play to social TV

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    International audienceThe great success of social technologies is transforming the Internet into a collaborative community. With a vision of IPTV 2.0, this paper presents our research work towards the exploitation of social phenomena in the domain of TV. Based on the advantage of IP Multimedia Subsystem (IMS) service architecture, the current IPTV service is extended from two aspects: TV-enriched communication and sociability-enhanced TV. Two applications namely TV Buddy and Social Electronic Program Guide (EPG) are proposed to demonstrate them respectively. Finally, we developed a prototype system on Ericsson IMS Software Development Studio (SDS)

    Quality of Service optimisation framework for Next Generation Networks

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    Within recent years, the concept of Next Generation Networks (NGN) has become widely accepted within the telecommunication area, in parallel with the migration of telecommunication networks from traditional circuit-switched technologies such as ISDN (Integrated Services Digital Network) towards packet-switched NGN. In this context, SIP (Session Initiation Protocol), originally developed for Internet use only, has emerged as the major signalling protocol for multimedia sessions in IP (Internet Protocol) based NGN. One of the traditional limitations of IP when faced with the challenges of real-time communications is the lack of quality support at the network layer. In line with NGN specification work, international standardisation bodies have defined a sophisticated QoS (Quality of Service) architecture for NGN, controlling IP transport resources and conventional IP QoS mechanisms through centralised higher layer network elements via cross-layer signalling. Being able to centrally control QoS conditions for any media session in NGN without the imperative of a cross-layer approach would result in a feasible and less complex NGN architecture. Especially the demand for additional network elements would be decreased, resulting in the reduction of system and operational costs in both, service and transport infrastructure. This thesis proposes a novel framework for QoS optimisation for media sessions in SIP-based NGN without the need for cross-layer signalling. One key contribution of the framework is the approach to identify and logically group media sessions that encounter similar QoS conditions, which is performed by applying pattern recognition and clustering techniques. Based on this novel methodology, the framework provides functions and mechanisms for comprehensive resource-saving QoS estimation, adaptation of QoS conditions, and support of Call Admission Control. The framework can be integrated with any arbitrary SIP-IP-based real-time communication infrastructure, since it does not require access to any particular QoS control or monitoring functionalities provided within the IP transport network. The proposed framework concept has been deployed and validated in a prototypical simulation environment. Simulation results show MOS (Mean Opinion Score) improvement rates between 53 and 66 percent without any active control of transport network resources. Overall, the proposed framework comes as an effective concept for central controlled QoS optimisation in NGN without the need for cross-layer signalling. As such, by either being run stand-alone or combined with conventional QoS control mechanisms, the framework provides a comprehensive basis for both the reduction of complexity and mitigation of issues coming along with QoS provision in NGN

    Multi-protocol correlation : data record analyses and correlator design

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    This thesis has two main goals. The first one is to design a user configurable multiprotocol correlator and implement a prototype of said design. The second goal is to identify and propose a method to match different data records from different protocols. In essence, this thesis is about correlation of records which contain information about protocols or services that are generally used in telecommunication networks. In order to reach the two main goals of this thesis, we need to combine our knowledge from the programming world with our knowledge from the networking world. Correlation can be done on multiple levels; you can correlate protocol messages, and you can correlate whole calls or transactions which allows you to perform correlation across sections of a network. We approach this problem by gathering protocol signaling data, specifications on how the protocols work, and log files with examples. With this knowledge we were able to identify many of the problem areas related to correlation of the main protocols to be used in this thesis. We designed a configurable correlator that could be configured to overcome the problem areas related to correlation provided enough data was given. The prototype correlator was tested both on correctness and performance. Then, in order to validate the correctness and preciseness of our developed prototype correlator, we compare the correlation results obtained from our tool with the results obtained using Utel System ´s STINGA NGN Monitor. The comparison shows that the correlation results from our prototype correlator are satisfactor

    Smart PIN: performance and cost-oriented context-aware personal information network

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    The next generation of networks will involve interconnection of heterogeneous individual networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore, there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches for data delivery across the heterogeneous network are also required. In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed: Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD). MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches

    Implementation and Performance Evaluation of an NGN prototype using WiMax as an Access Technology

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    Telecommunications networks have evolved to IP-based networks, commonly known as Next Generation Networks (NGN). The biggest challenge in providing high quality realtime multimedia applications is achieving a Quality of Service (QoS) consistent with user expectations. One of the key additional factors affecting QoS is the existence of different QoS mechanisms on the heterogeneous technologies used on NGN platforms. This research investigates the techniques used to achieve consistent QoS on network technologies that use different QoS techniques. Numerous proposals for solving the end-to-end QoS problem in IP networks have adopted policy-based management, use of signalling protocols for communicating applications QoS requirements across different Network Elements and QoS provisioning in Network Elements. Such solutions are dependent on the use of traffic classification and knowledge of the QoS requirements of applications and services on the networks. This research identifies the practical difficulties involved in meeting the QoS requirements of network traffic between WiMax and an IP core network. In the work, a solution based on the concept of class-of-service mapping is proposed. In the proposed solution, QoS is implemented on the two networks and the concept of class-of-service mapping is used to integrate the two QoS systems. This essentially provides consistent QoS to applications as they traverse the two network domains and hence meet end-user QoS expectations. The work is evaluated through a NGN prototype to determine the capabilities of the networks to deliver real-time media that meets user expectations

    Structured data abstractions and interpretable latent representations for single-cell multimodal genomics

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    Single-cell multimodal genomics involves simultaneous measurement of multiple types of molecular data, such as gene expression, epigenetic marks and protein abundance, in individual cells. This allows for a comprehensive and nuanced understanding of the molecular basis of cellular identity and function. The large volume of data generated by single-cell multimodal genomics experiments requires specialised methods and tools for handling, storing, and analysing it. This work provides contributions on multiple levels. First, it introduces a single-cell multimodal data standard — MuData — designed to facilitate the handling, storage and exchange of multimodal data. MuData provides interfaces that enable transparent access to multimodal annotations as well as data from individual modalities. This data structure has formed the foundation for the multimodal integration framework, which enables complex and composable workflows that can be naturally integrated with existing omics-specific analysis approaches. Joint analysis of multimodal data can be performed using integration methods. In order to enable integration of single-cell data, an improved multi-omics factor analysis model (MOFA+) has been designed and implemented building on the canonical dimensionality reduction approach for multi-omics integration. Inferring later factors that explain variation across multiple modalities of the data, MOFA+ enables the modelling of latent factors with cell group-specific patterns of activity. MOFA+ model has been implemented as part of the respective multi-omics integration framework, and its utility has been extended by software solutions that facilitate interactive model exploration and interpretation. The newly improved model for multi-omics integration of single cells has been applied to the study of gene expression signatures upon targeted gene activation. In a dataset featuring targeted activation of candidate regulators of zygotic genome activation (ZGA) — a crucial transcriptional event in early embryonic development, — modelling expression of both coding and non-coding loci with MOFA+ allowed to rank genes by their potency to activate a ZGA-like transcriptional response. With identification of Patz1, Dppa2 and Smarca5 as potent inducers of ZGA-like transcription in mouse embryonic stem cells, these findings have contributed to the understanding of molecular mechanisms behind ZGA and laid the foundation for future research of ZGA in vivo. In summary, this work’s contributions include the development of data handling and integration methods as well as new biological insights that arose from applying these methods to studying gene expression regulation in early development. This highlights how single-cell multimodal genomics can aid to generate valuable insights into complex biological systems
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