Conceptual Studies of Multistage Depressed Collectors for Gyrotrons

The multistage depressed collector (MDC) shall be one of the key technologies to achieve the required gyrotron efficiency in the DEMOnstration fusion power plant. For the first time, this work presents a comprehensive study of possible gyrotron MDC concepts. Concepts, only using axisymmetric E- and B-field components are shown to be insufficient. Instead, promising concepts using the E×B drift are proposed. A detailed study of a novel MDC concept using an azimuthal electric field is presented

Investigations on Improving Broadband Boundary Conditions in Gyrotron Interaction Modelling

Gyrotrons are microwave tubes capable of providing mega-watt power at millimetric wavelengths. The microwave power is produced by the conversion of the kinetic energy of an electron beam to electromagnetic wave energy. Simulations of the beam-wave interaction in the gyrotron cavity are essential for gyrotron design, as well as theoretical and experimental studies. In the usual gyrotron operation the spectrum of the generated radiation is concentrated around the nominal frequency. For this reason, the usual simulations consider only a narrow-band output spectrum (e.g. several GHz bandwidth comparing with the working frequency in the range of 100-200 GHz). As a result, the typical existing codes use a single-frequency radiation boundary condition for the generated electromagnetic field in the cavity. This condition is matched only at one frequency. However, there are two important aspects, which motivate an advanced formulation and implementation of the cavity boundary condition. Firstly, the occurrence of broadband effects (which may be several tens of GHz) in some cases, like dynamic after-cavity-interaction or modulation side-bands, requires a broadband boundary condition. Secondly, there are reflections from inside and outside of the gyrotron, which can only be considered in the simulation through a boundary condition with user-defined, frequency-dependent reflections. This master thesis proposes an improved formulation of the broadband boundary condition in the self-consistent, beam-wave interaction code Euridice. In this new formulation, two physical variables — the wave impedance and the axial wavenumber are expanded in polynomial series in the frequency domain. Because the beam-wave interaction process is simulated transiently in the time domain, the boundary condition should be also expressed in the time domain. This involves a non-trivial inverse Fourier transform, for which two solutions are proposed, tested and validated. It has been shown that, through the newly developed formulation, the existing matched boundary condition (that should yield zero-reflection in ideal case) can be improved by 15 dB even with a first-order polynomial series. Moreover, a user-defined, frequency-dependent complex reflection coefficient can be introduced. This was not possible with the previously existing boundary condition in Euridice

Career Path Clustering via Sequential Job Embedding and Mixture Markov Models

Extracting typical career paths from large-­scale and unstructured talent profiles has recently attracted increasing research attention. However, various challenges arise in effectively analyzing self-­reported career records. Inspired by recent advances in neural networks and embedding models, we develop a novel career path clustering approach with two major components. First, we formulate an embedded Markov framework to learn job embeddings from longitudinal career records and further use them to compute dynamic embeddings of career paths. Second, to cope with heterogeneous career path clusters, we estimate a mixture of Markov models to optimize cluster-­wise job embeddings with a prior embedded space shared by multiple clusters. We conduct extensive experiments with our framework to investigate its algorithmic performance and extract meaningful patterns of career paths in the information technology (IT) industry. The results show that our approach can naturally discover distinct career path clusters and reveal valuable insights

Firm Profiling and Competition Assessment: A Heterogeneous Occupation Network–based Method

Extensive efforts have been made by both academics and practitioners to understand inter-firm competitive relationship owing to its profound impacts on multiple key business goals. However, it has never been an easy task to fully characterize firms and assess the competitive relationship among them mainly due to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and inter-firm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees’ occupation details and education attainments. Then we adopt a Methpath2Vec-based heterogeneous network embedding model to learn firms’ latent profiles (embeddings). Using the firm embeddings as input, we train multiple supervised classifiers to assess the competitive relationship among the firms. Following the logic of design as a search process, we demonstrate the utility of our IT artifact with extensive experimental study and in-depth discussions. Our study also reveals that employees’ occupation and education information significantly contribute to the identification of the focal firm’s potential competitors

Firm Profiling and Competition Assessment via Heterogeneous Occupation Network

Extensive efforts have been made by both academics and practitioners to understand interfirm competitive relationship. However, it has never been an easy task to fully characterize firms and assess their competitive relationship owing to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and interfirm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees\u27 occupation details and education attainments. Then we adopt a Methpath2Vec-­based heterogeneous network embedding model to learn firms\u27 latent profiles (embeddings). Using firm embeddings as input, we train multiple classifiers to assess the competitive relationship among the firms. We demonstrate the utility of our IT artifact with extensive experimental study and in-­depth discussions. Our study also reveals that employees’ occupation and education information significantly contribute to the identification of the focal firm\u27s potential competitors

Two-dimensional leapfrog scheme for trajectories of relativistic charged particles in static axisymmetric electric and magnetic field

A method for the calculation of two-dimensional particle trajectories is proposed in this work. It makes use of the cylindrical symmetry and the simplification of the static electric field, so that there should be no systematic error for the centered large-orbit rotations nor for the acceleration or deceleration in a uniform electric field. The method also shows a lower error level than the standard Boris method in many cases. Typical applications of this method are for example, electron microscopes, electron guns and collectors of gyro-devices as well as of other vacuum tubes, which can be described in axisymmetric cylindrical coordinates. Besides, the proposed method enforces the conservation of canonical angular momentum by construction, which is expected to show its advantages in the simulation of cusp electron guns and other components relying on non-adiabatic transitions in the externally applied static magnetic field

Predicting Temporal Sets with Deep Neural Networks

Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In practice, temporal sets prediction is much more complex than predictive modelling of temporal events and time series, and is still an open problem. Many possible existing methods, if adapted for the problem of temporal sets prediction, usually follow a two-step strategy by first projecting temporal sets into latent representations and then learning a predictive model with the latent representations. The two-step approach often leads to information loss and unsatisfactory prediction performance. In this paper, we propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective of our approach is to learn element relationship by constructing set-level co-occurrence graph and then perform graph convolutions on the dynamic relationship graphs. Moreover, we design an attention-based module to adaptively learn the temporal dependency of elements and sets. Finally, we provide a gated updating mechanism to find the hidden shared patterns in different sequences and fuse both static and dynamic information to improve the prediction performance. Experiments on real-world data sets demonstrate that our approach can achieve competitive performances even with a portion of the training data and can outperform existing methods with a significant margin.Comment: 9 pages, 6 figures, Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '2020