No Arabic abstract
Beams passing through a solenoid fringe field experience x-y coupling and change of their eigen-emittances. As reported previously (C.~Xiao et al., Phys. Rev. ST Accel. Beams 044201, {bf 16} 2013) constant settings of a subsequent decoupling section can be found such that variation of the fringe field strength will not change the Twiss parameters $beta$ and $alpha$ in both transverse planes at the exit of the decoupling section. For time being this feature was understood for a generic beam line but not to the generality to which it is observed. This report is on explanation of the convenient decoupling of fringe-coupled beams by any beam line that provides decoupling. For better coherence this report includes recapitulation of previous works.
In this work, we present a stand-alone and fiber-coupled quantum-light source. The plug-and-play device is based on an optically driven quantum dot delivering single photons via an optical fiber. The quantum dot is deterministically integrated in a monolithic microlens which is precisely coupled to the core of an optical fiber via active optical alignment and epoxide adhesive bonding. The rigidly coupled fiber-emitter assembly is integrated in a compact Stirling cryocooler with a base temperature of 35 K. We benchmark our practical quantum device via photon auto-correlation measurements revealing $g^{(2)}(0)=0.07 pm 0.05$ under continuous-wave excitation and we demonstrate triggered non-classical light at a repetition rate of 80 MHz. The long-term stability of our quantum light source is evaluated by endurance tests showing that the fiber-coupled quantum dot emission is stable within 4% over several successive cool-down/warm-up cycles. Additionally, we demonstrate non-classical photon emission for a user-intervention-free 100-hour test run and stable single-photon count rates up to 11.7 kHz with a standard deviation of 4%.
Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.
Fringe fields in multipole magnets can have a variety of effects on the linear and nonlinear dynamics of particles moving along an accelerator beamline. An accurate model of an accelerator must include realistic models of the magnet fringe fields. Fringe fields for dipoles are well understood and can be modelled at an early stage of accelerator design in such codes as MAD8, MADX or ELEGANT. However, usually it is not until the final stages of a design project that it is possible to model fringe fields for quadrupoles or higher order multipoles. Even then, existing techniques rely on the use of a numerical field map, which will usually not be available until the magnet design is well developed. Substitutes for the full field map exist but these are typically based on expansions about the origin and rely heavily on the assumption that the beam travels more or less on axis throughout the beam line. In some types of machine (for example, a non-scaling FFAG such as EMMA) this is not a good assumption. In this paper, a method for calculating fringe fields based on analytical expressions is presented, which allows fringe field effects to be included at the start of an accelerator design project. The magnetostatic Maxwell equations are solved analytically and a solution that fits all orders of multipoles derived. Quadrupole fringe fields are considered in detail as these are the ones that give the strongest effects. Two examples of quadrupole fringe fields are presented. The first example is a magnet in the LHC inner triplet, which consists of a set of four quadrupoles providing the final focus to the beam, just before the interaction point. Quadrupoles in EMMA provide the second example. In both examples, the analytical expressions derived in this paper for quadrupole fringe fields provide a good approximation to the field maps obtained from a numerical magnet modelling code.
Convolutions are a fundamental building block of modern computer vision systems. Recent approaches have argued for going beyond convolutions in order to capture long-range dependencies. These efforts focus on augmenting convolutional models with content-based interactions, such as self-attention and non-local means, to achieve gains on a number of vision tasks. The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions. In developing and testing a pure self-attention vision model, we verify that self-attention can indeed be an effective stand-alone layer. A simple procedure of replacing all instances of spatial convolutions with a form of self-attention applied to ResNet model produces a fully self-attentional model that outperforms the baseline on ImageNet classification with 12% fewer FLOPS and 29% fewer parameters. On COCO object detection, a pure self-attention model matches the mAP of a baseline RetinaNet while having 39% fewer FLOPS and 34% fewer parameters. Detailed ablation studies demonstrate that self-attention is especially impactful when used in later layers. These results establish that stand-alone self-attention is an important addition to the vision practitioners toolbox.
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the stand-alone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations. We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model. Our solution outperforms the previous state-of-the-art result by 12.8% absolute F0.5 score.