96 research outputs found

    A variational autoencoder application for real-time anomaly detection at CMS

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    Despite providing invaluable data in the field of High Energy Physics, towards higher luminosity runs the Large Hadron Collider (LHC) will face challenges in discovering interesting results through conventional methods used in previous run periods. Among the proposed approaches, the one we focus on in this thesis work – in collaboration with CERN teams, involves the use of a joint variational autoencoder (JointVAE) machine learning model, trained on known physics processes to identify anomalous events that correspond to previously unidentified physics signatures. By doing so, this method does not rely on any specific new physics signatures and can detect anomalous events in an unsupervised manner, complementing the traditional LHC search tactics that rely on model-dependent hypothesis testing. The algorithm produces a list of anomalous events, which experimental collaborations will examine and eventually confirm as new physics phenomena. Furthermore, repetitive event topologies in the dataset can inspire new physics model building and experimental searches. Implementing this algorithm in the trigger system of LHC experiments can detect previously unnoticed anomalous events, thus broadening the discovery potential of the LHC. This thesis presents a method for implementing the JointVAE model, for real-time anomaly detection in the Compact Muon Solenoid (CMS) experiment. Among the challenges of implementing machine learning models in fast applications, such as the trigger system of the LHC experiments, low latency and reduced resource consumption are essential. Therefore, the JointVAE model has been studied for its implementation feasibility in Field-Programmable Gate Arrays (FPGAs), utilizing a tool based on High-Level Synthesis (HLS) named HLS4ML. The tool, combined with the quantization of neural networks, will reduce the model size, latency, and energy consumption

    Technology and readout for scaling up superconducting nanowire single-photon detectors

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    This work presents three advances to scale SNSPDs from few-pixel devices to large detector arrays: atomic layer deposition for the fabrication of uniform superconducting niobium nitride films of few-nanometer thickness, a frequency-multiplexing scheme to operate multiple detectors with a reduced number of lines, and the integration of SNSPDs with free-form polymer structures to achieve efficient optical coupling onto the active area of the detectors

    Network-Wide Monitoring And Debugging

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    Modern networks can encompass over 100,000 servers. Managing such an extensive network with a diverse set of network policies has become more complicated with the introduction of programmable hardwares and distributed network functions. Furthermore, service level agreements (SLAs) require operators to maintain high performance and availability with low latencies. Therefore, it is crucial for operators to resolve any issues in networks quickly. The problems can occur at any layer of stack: network (load imbalance), data-plane (incorrect packet processing), control-plane (bugs in configuration) and the coordination among them. Unfortunately, existing debugging tools are not sufficient to monitor, analyze, or debug modern networks; either they lack visibility in the network, require manual analysis, or cannot check for some properties. These limitations arise from the outdated view of the networks, i.e., that we can look at a single component in isolation. In this thesis, we describe a new approach that looks at measuring, understanding, and debugging the network across devices and time. We also target modern stateful packet processing devices: programmable data-planes and distributed network functions as these becoming increasingly common part of the network. Our key insight is to leverage both in-network packet processing (to collect precise measurements) and out-of-network processing (to coordinate measurements and scale analytics). The resulting systems we design based on this approach can support testing and monitoring at the data center scale, and can handle stateful data in the network. We automate the collection and analysis of measurement data to save operator time and take a step towards self driving networks

    Conflict detection in software-defined networks

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    The SDN architecture facilitates the flexible deployment of network functions. While promoting innovation, this architecture induces yet a higher chance of conflicts compared to conventional networks. The detection of conflicts in SDN is the focus of this work. Restrictions of the formal analytical approach drive our choice of an experimental approach, in which we determine a parameter space and a methodology to perform experiments. We have created a dataset covering a number of situations occurring in SDN. The investigation of the dataset yields a conflict taxonomy composed of various classes organized in three broad types: local, distributed and hidden conflicts. Interestingly, hidden conflicts caused by side-effects of control applications‘ behaviour are completely new. We introduce the new concept of multi-property set, and the ·r (“dot r”) operator for the effective comparison of SDN rules. With these capable means, we present algorithms to detect conflicts and develop a conflict detection prototype. The evaluation of the prototype justifies the correctness and the realizability of our proposed concepts and methodologies for classifying as well as for detecting conflicts. Altogether, our work establishes a foundation for further conflict handling efforts in SDN, e.g., conflict resolution and avoidance. In addition, we point out challenges to be explored. Cuong Tran won the DAAD scholarship for his doctoral research at the Munich Network Management Team, Ludwig-Maximilians-Universität München, and achieved the degree in 2022. He loves to do research on policy conflicts in networked systems, IP multicast and alternatives, network security, and virtualized systems. Besides, teaching and sharing are also among his interests

    Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

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    The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

    Blockchain-Based Digitalization of Logistics Processes—Innovation, Applications, Best Practices

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    Blockchain technology is becoming one of the most powerful future technologies in supporting logistics processes and applications. It has the potential to destroy and reorganize traditional logistics structures. Both researchers and practitioners all over the world continuously report on novel blockchain-based projects, possibilities, and innovative solutions with better logistic service levels and lower costs. The idea of this Special Issue is to provide an overview of the status quo in research and possibilities to effectively implement blockchain-based solutions in business practice. This Special Issue reprint contained well-prepared research reports regarding recent advances in blockchain technology around logistics processes to provide insights into realized maturity

    Grading Goal Four

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    Grading Goal Four explores the tensions, threats and opportunities within Sustainable Development Goal 4 on quality education, aiming to support its rights-based implementation and to build capacity for strengthened monitoring and analysis of the goal.; Readership: All interested in the threats and opportunities within SDG 4 on quality education, including researchers, students and education activists in academia, civil society, government policy-making and international educational diplomacy
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