Superconducting cavities with low RF frequencies and heavy damping of higher order modes (HOM) are desired for the main accelerator of High Energy Photon Source (HEPS), a 6 GeV synchrotron light source promising ultralow emittance currently under con
struction in Beijing. A compact 166.6 MHz superconducting cavity was proposed adopting a quarter-wave beta=1 geometry. Based on the successful development of a proof-of-principle cavity, a HOM-damped 166.6 MHz compact superconducting cavity was subsequently designed. A ferrite damper was installed on the beam pipe to reduce HOM impedance below the stringent threshold of coupled-bunch instabilities. Being compact, RF field heating on the cavity vacuum seal was carefully examined against quenching the NbTi flange. The cavity was later dressed with a helium vessel and the tuning mechanism was also realized. Excellent RF and mechanical properties were eventually achieved. Finally, the two-cavity string was designed to ensure smooth transitions among components and proper shielding of synchrotron light. This paper presents a complete design of a fully dressed HOM-damped low-frequency beta=1 superconducting cavity for HEPS.
Weakly-supervised object detection (WSOD) has emerged as an inspiring recent topic to avoid expensive instance-level object annotations. However, the bounding boxes of most existing WSOD methods are mainly determined by precomputed proposals, thereby
being limited in precise object localization. In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset. First, we use the well-annotated auxiliary dataset to explore a series of learnable bounding box adjusters (LBBAs) in a multi-stage training manner, which is class-agnostic. Then, only LBBAs and a weakly-annotated dataset with non-overlapped classes are used for training LBBA-boosted WSOD. As such, our LBBAs are practically more convenient and economical to implement while avoiding the leakage of the auxiliary well-annotated dataset. In particular, we formulate learning bounding box adjusters as a bi-level optimization problem and suggest an EM-like multi-stage training algorithm. Then, a multi-stage scheme is further presented for LBBA-boosted WSOD. Additionally, a masking strategy is adopted to improve proposal classification. Experimental results verify the effectiveness of our method. Our method performs favorably against state-of-the-art WSOD methods and knowledge transfer model with similar problem setting. Code is publicly available at url{https://github.com/DongSky/lbba_boosted_wsod}.
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device. Our system can perform general manipulation tasks such as cloth folding, hammering and 3mm clearance peg in hole. We propose the use of non-Cartesia
n oblique coordinate frame, dynamic motion scaling and reposition of operator frames to increase the flexibility of our teleoperation system. We hypothesize that lowering the barrier of entry to teleoperation will allow for wider deployment of supervised autonomy system, which will in turn generates realistic datasets that unlock the potential of machine learning for robotic manipulation.
Bubbles, the semi-circular voids below quiescent prominences (filaments), have been extensively investigated in the past decade. However, hitherto the magnetic nature of bubbles cannot be verified due to the lack of on-disk photospheric magnetic fiel
d observations. Here for the first time, we find and investigate an on-disk prominence bubble around a filament barb on 2019 March 18 based on stereoscopic observations from NVST, SDO, and STEREO-A. In high-resolution NVST H$alpha$ images, this bubble has a sharp arch-like boundary and a projected width of $thicksim$26 Mm. Combining SDO and STEREO-A images, we further reconstruct 3D structure of the bubble boundary, whose maximum height is $thicksim$15.6 Mm. The squashing factor Q map deduced from extrapolated 3D magnetic fields around the bubble depicts a distinct arch-shaped interface with a height of $thicksim$11 Mm, which agrees well with the reconstructed 3D structure of the observed bubble boundary. Under the interface lies a set of magnetic loops, which is rooted on a surrounding photospheric magnetic patch. To be more persuasive, another on-disk bubble on 2019 June 10 is presented as a supplement. According to these results obtained from on-disk bubble observations, we suggest that the bubble boundary corresponds to the interface between the prominence dips (barb) and the underlying magnetic loops rooted nearby. It is thus reasonable to speculate that the bubble can form around a filament barb below which there is a photospheric magnetic patch.
Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias. This causes a newly trained model to act greedily and favor items that have already been engaged by users. This beh
avior is particularly harmful in personalised ads recommendations, as it can also cause new campaigns to remain unexplored. Exploration aims to address this limitation by providing new information about the environment, which encompasses user preference, and can lead to higher long-term reward. In this work, we formulate a display advertising recommender as a contextual bandit and implement exploration techniques that require sampling from the posterior distribution of click-through-rates in a computationally tractable manner. Traditional large-scale deep learning models do not provide uncertainty estimates by default. We approximate these uncertainty measurements of the predictions by employing a bootstrapped model with multiple heads and dropout units. We benchmark a number of different models in an offline simulation environment using a publicly available dataset of user-ads engagements. We test our proposed deep Bayesian bandits algorithm in the offline simulation and online AB setting with large-scale production traffic, where we demonstrate a positive gain of our exploration model.
Vertically-stacked monolayers of graphene and other atomically-thin 2D materials have attracted considerable research interest because of their potential in fabricating materials with specifically-designed properties. Chemical vapor deposition has pr
oved to be an efficient and scalable fabrication method. However, a lack of mechanistic understanding has hampered efforts to control the fabrication process beyond empirical trial-and-error approaches. In this paper, we develop a general multiscale Burton-Cabrera-Frank (BCF) type model of the vertical growth of 2D materials to predict the necessary growth conditions for vertical versus in-plane (monolayer) growth of arbitrarily-shaped layers. This extends previous work where we developed such a model assuming the layers were fully-faceted (Ye et al., ACS Nano, 11, 12780-12788, 2017). To solve the model numerically, we reformulate the system using the phase-field/diffuse domain method that enables the equations to be solved in a fixed regular domain. We use a second-order accurate, adaptive finite-difference/nonlinear multigrid algorithm to discretize and solve the discrete system. We investigate the effect of parameters, including the van der Waals interaction energies between the layers, the kinetic attachment rates, the edge-energies and the deposition flux, on layer growth and morphologies. While the conditions that favor vertical growth generally follow an analytic thermodynamic criterion we derived for circular layers, the layer boundaries may develop significant curvature during growth, consistent with experimental observations. Our approach provides a mechanistic framework for controlling and optimizing the growth multilayered 2D materials.
Experimental and theoretical studies of manganese deposition on graphene/Ni(111) shows that a thin ferromagnetic Ni3Mn layer, which is protected by the graphene overlayer, is formed upon Mn intercalation. The electronic bands of graphene are affected
by Ni3Mn interlayer formation through a slight reduction of n-type doping compared to graphene/Ni(111) and a suppression of the interface states characteristic of graphene/Ni(111). Our DFT-based theoretical analysis of interface geometric, electronic, and magnetic structure gives strong support to our interpretation of the experimental scanning tunneling microscopy, low energy electron diffraction, and photoemission results, and shows that the magnetic structure of graphene is strongly influenced by Ni3Mn formation.
In this paper, we investigate numerically a diffuse interface model for the Navier-Stokes equation with fluid-fluid interface when the fluids have different densities cite{Lowengrub1998}. Under minor reformulation of the system, we show that there is
a continuous energy law underlying the system, assuming that all variables have reasonable regularities. It is shown in the literature that an energy law preserving method will perform better for multiphase problems. Thus for the reformulated system, we design a $C^0$ finite element method and a special temporal scheme where the energy law is preserved at the discrete level. Such a discrete energy law (almost the same as the continuous energy law) for this variable density two-phase flow model has never been established before with $C^0$ finite element. A Newtons method is introduced to linearise the highly non-linear system of our discretization scheme. Some numerical experiments are carried out using the adaptive mesh to investigate the scenario of coalescing and rising drops with differing density ratio. The snapshots for the evolution of the interface together with the adaptive mesh at different times are presented to show that the evolution, including the break-up/pinch-off of the drop, can be handled smoothly by our numerical scheme. The discrete energy functional for the system is examined to show that the energy law at the discrete level is preserved by our scheme.
Systematic ab initio calculations show that the energy gap of boron nitride (BN) nanoribbons (BNNRs) with zigzag or armchair edges can be significantly reduced by a transverse electric field and completely closed at a critical field which decreases w
ith increasing ribbon width. In addition, a distinct gap modulation in the ribbons with zigzag edges is presented when a reversed electric field is applied. In a weak field, the gap reduction of the BNNRs with zigzag edges originates from the field-induced energy level shifts of the spatially separated edge-states, while the gap reduction of the BNNRs with armchair edges arises from the Stark effect. As the field gets stronger, the energy gaps of both types of the BNNRs gradually close due to the field-induced motion of nearly free electron states. Without the applied fields, the energy gap modulation by varying ribbon width is rather limited.
We report the stability and electronic structures of the boron nitride nanotubes (BNNTs) with diameters below 4 A by semi-empirical quantum mechanical molecular dynamics simulations and ab initio calculations. Among them (3,0), (3,1), (2,2), (4,0), (
4,1) and (3,2) BNNTs can be stable well over room temperature. These small BNNTs become globally stable when encapsulated in a larger BNNT. It is found that the energy gaps and work functions of these small BNNTs are strongly dependent on their chirality and diameters. The small zigzag BNNTs become desirable semiconductors and have peculiar distribution of nearly free electron states due to strong hybridization effect. When such a small BNNT is inserted in a larger one, the energy gap of the formed double-walled BNNT can even be much reduced due to the coupled effect of wall buckling difference and NFE-pi hybridization.
