Diagnostics of longitudinal bunch instabilities at KARA

KARA, the Karlsruhe Research Accelerator, can be operated in different modes, including a short-bunch mode. During this mode, where the dispersion is stretched in order to reduce the momentum-compaction factor, micro-bunching instability can occur. At KARA, several measurement setups and techniques are used to investigate this instability further with the long-term perspective to suppress and control it. In this contribution, we give an overview about the different setups and the results achieved during the past years

Novel P-in-N Si-Sensor technology for high resolution and high repetition-rate experiments at accelerator facilities

Linear array detectors with high spatial resolution and MHz frame-rates are essential for high-rate experiments at accelerator facilities. KALYPSO, a line array detector with 1024 pixels operating over 1 Mfps has been developed. To improve the spatial resolution and sensitivity at different wavelengths, novel p-in-n Si microstrip sensors based on have been developed with a pitch of 25 micrometer. The efficiency of the sensor has been improved with the use of anti reflecting coating layers optimized for near infrared, visible and near ultraviolet. In this contribution the detector system and the sensors will be presented

Electro-Optical Diagnostics at KARA and FLUTE – Results and Prospects

Electro-optical (EO) methods are nowadays well-proven diagnostic tools, which are utilized to detect THz fields in countless experiments. The world’s first near-field EO sampling monitor at an electron storage ring was developed and installed at the KIT storage ring KARA (Karlsruhe Research Accelerator) and optimized to detect longitudinal bunch profiles. This experiment with other diagnostic techniques builds a distributed, synchronized sensor network to gain comprehensive data about the phase-space of electron bunches as well as the produced coherent synchrotron radiation (CSR). These measurements facilitate studies of physical conditions to provide, at the end, intense and stable CSR in the THz range. At KIT, we also operate FLUTE (Ferninfrarot Linac- und Test-Experiment), a new compact versatile linear accelerator as a test facility for novel techniques and diagnostics. There, EO diagnostics will be implemented to open up possibilities to evaluate and compare new techniques for longitudinal bunch diagnostics. In this contribution, we will give an overview of results achieved, the current status of the EO diagnostic setups at KARA and FLUTE and discuss future prospects

Implementing Electro-Optical Diagnostics for Measuring the CSR far-field at KARA

For measuring the temporal profile of the coherent synchrotron radiation (CSR) at the KIT storage ring KARA (Karlsruhe Research Accelerator) an experimental setup based on electro-optical spectral decoding (EOSD) is currently being implemented. The EOSD technique allows single-shot, phase-sensitive measurements of the far-field radiation on a turn-by-turn basis at rates in the MHz range. Therefore, the resulting THz radiation from the dynamics of the bunch evolution, e.g. the microbunching, can be observed with high temporal resolution. This far-field setup is part of the distributed sensor network at KARA. Additionally to the information acquired from the near-field EOSD spectral decoding and the horizontal bunch profile monitor, it enables to monitor the longitudinal phase-space of the bunch. In this contribution, the characterization of the far-field setup is summarized and its implementation is discussed

Molecular complex detection in protein interaction networks through reinforcement learning

Abstract Background Proteins often assemble into higher-order complexes to perform their biological functions. Such protein–protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein–protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks. Results The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling. Conclusions Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018