Electron beam profile imaging in the presence of coherent optical radiation effects

High-brightness electron beams with low energy spread at existing and future x-ray free-electron lasers are affected by various collective beam self-interactions and microbunching instabilities. The corresponding coherent optical radiation effects, e.g., coherent optical transition radiation, render electron beam profile imaging impossible and become a serious issue for all kinds of electron beam diagnostics using imaging screens. Furthermore, coherent optical radiation effects can also be related to intrinsically ultrashort electron bunches or the existence of ultrashort spikes inside the electron bunches. In this paper, we discuss methods to suppress coherent optical radiation effects both by electron beam profile imaging in dispersive beamlines and by using scintillation imaging screens in combination with separation techniques. The suppression of coherent optical emission in dispersive beamlines is shown by analytical calculations, numerical simulations, and measurements. Transverse and longitudinal electron beam profile measurements in the presence of coherent optical radiation effects in non-dispersive beamlines are demonstrated by applying a temporal separation technique.Comment: 12 pages, 11 figures, submitted to Phys. Rev. ST Accel. Beam

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale

Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification) and energy function minimizers that inherit desirable inductive biases and interpretability. However, scaling GNN architectures constructed in this way remains challenging, in part because the convergence of the forward pass may involve models with considerable depth. To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence guarantees in certain settings. We also instantiate a full GNN architecture based on these designs, and the model achieves competitive accuracy and scalability when applied to the largest publicly-available node classification benchmark exceeding 1TB in size

Involvement of C2H2 zinc finger proteins in the regulation of epidermal cell fate determination in Arabidopsis

Cell fate determination is a basic developmental process during the growth of multicellular organisms. Trichomes and root hairs of Arabidopsis are both readily accessible structures originating from the epidermal cells of the aerial tissues and roots respectively, and they serve as excellent models for understanding the molecular mechanisms controlling cell fate determination and cell morphogenesis. The regulation of trichome and root hair formation is a complex program that consists of the integration of hormonal signals with a large number of transcriptional factors, including MYB and bHLH transcriptional factors. Studies during recent years have uncovered an important role of C2H2 type zinc finger proteins in the regulation of epidermal cell fate determination. Here in this minireview we briefly summarize the involvement of C2H2 zinc finger proteins in the control of trichome and root hair formation in Arabidopsis .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109574/1/jipb12221.pd

On the Perturbation of Synchrotron Motion in the Micro-Bunching Instability

The self-interaction of short electron bunches with their own radiation field can have a significant impact on the longitudinal beam dynamics in a storage ring. While higher bunch currents increase the power of the emitted CSR which can be provided to dedicated experiments, it simultaneously amplifies the strength of the self-interaction. Eventually, this leads to the formation of dynamically changing micro-structures within the bunch and thus fluctuating CSR emission, a phenomenon that is generally known as micro-bunching or micro-wave instability. The underlying longitudinal dynamics can be simulated by solving the VFP equation, where the CSR self-interaction can be added as a perturbation to the Hamiltonian. In this contribution, we focus on the perturbation of the synchrotron motion that is caused by introducing this additional wake field. Therefore, we adopt the perspective of a single particle and eventually comment on its implications for collective motion. We explicitly show how the shape of the parallel plates CSR wake potential breaks homogeneity in the longitudinal phase space and propose a quadrupole-like mode as potential seeding mechanism of the micro-bunching instability. Moreover, we consider synchrotron motion above the instability threshold and thereby motivate an approach to control of the occurring micro-bunching dynamics. Using dynamically adjusted RF amplitude modulations we can directly address the continuous CSR-induced perturbation at the timescale of its occurrence, which allows for substantial control over the longitudinal charge distribution. While the approach is not limited to this particular application, we demonstrate how this can significantly mitigate the micro-bunching dynamics directly above the instability threshold. The gained insights are supported and verified using the VFP solver Inovesa and put into context with measurements at KARA

DGI: Easy and Efficient Inference for GNNs

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. On the other hand, layer-wise inference avoids the neighbor explosion problem by conducting inference layer by layer such that the nodes only need their one-hop neighbors in each layer. However, implementing layer-wise inference requires substantial engineering efforts because users need to manually decompose a GNN model into layers for computation and split workload into batches to fit into device memory. In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution. DGI is general for various GNN models and different kinds of inference requests, and supports out-of-core execution on large graphs that cannot fit in CPU memory. Experimental results show that DGI consistently outperforms layer-wise inference across different datasets and hardware settings, and the speedup can be over 1,000x.Comment: 10 pages, 10 figure

Excitation of Micro-Bunching in Short Electron Bunches Using RF Amplitude Modulation

In its short-bunch operation mode, the KIT storage ring KARA provides picosecond-long electron bunches, which emit coherent synchrotron radiation (CSR) up to the terahertz frequency range. Due to the high spatial compression under these conditions, the self-interaction of the bunch with its own emitted CSR induces a wake-field, which significantly influences the longitudinal charge distribution. Above a given threshold current, this leads to the formation of dynamically evolving micro-structures within the bunch and is thus called micro-bunching instability. As CSR is emitted at wavelengths corresponding to the spatial dimension of the emitter, these small structures lead to an increased emission of CSR at higher frequencies. The instability is therefore deliberately induced at KARA to provide intense THz radiation to dedicated experiments. To further increase the emitted power in the desired frequency range, we consider the potential of RF amplitude modulations to intentionally excite this form of micro-bunching in short electron bunches