Ground vibrations couple to the longitudinal and angular motion of the aLIGO test masses and limit the observatory sensitivity below 30,Hz. Novel inertial sensors have the potential to improve the aLIGO sensitivity in this band and simplify the lock
acquisition of the detectors. In this paper, we experimentally study a compact 6D seismometer that consists of a mass suspended by a single wire. The position of the mass is interferometrically read out relative to the platform that supports the seismometer. We present the experimental results, discuss limitations of our metallic prototype, and show that a compact 6D seismometer made out of fused silica and suspended with a fused silica fibre has the potential to improve the aLIGO low frequency noise.
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images. However, these edited results often suffer from artifacts or incor
rect feature rendering, especially when there is a large discrepancy between the image to be edited and the desired feature set. Therefore, we propose a new approach to mapping the latent vectors of the generative model to the scaling factors through solving a set of multivariate linear equations. The coefficients of the equations are the eigenvectors of the weight parameters of the pre-trained model, which form the basis of a hyper coordinate system. The qualitative and quantitative results both show that the proposed method outperforms the baseline in terms of image diversity. In addition, the method is much more time-efficient because you can obtain synthesised images with desirable features directly from the latent vectors, rather than the former process of editing randomly generated images requiring many processing steps.
We report on the experimental demonstration of atomically thin molybdenum disulfide (MoS2)-graphene van der Waals (vdW) heterostructure nanoelectromechanical resonators with ultrawide frequency tuning. With direct electrostatic gate tuning, these vdW
resonators exhibit exceptional tunability, in general, {Delta}f/f0 >200%, for continuously tuning the same device and the same mode (e.g., from ~23 to ~107MHz), up to {Delta}f/f0 = 370%, the largest fractional tuning range in such resonators to date. This remarkable electromechanical resonance tuning is investigated by two different analytical models and finite element simulations. Further, we carefully perform clear control experiments and simulations to elucidate the difference in frequency tuning between heterostructure and single-material resonators. At a given initial strain level, the tuning range depends on the two-dimensional (2D) Youngs moduli of the constitutive crystals; devices built on materials with lower 2D moduli show wider tuning ranges. This study exemplifies that vdW heterostructure resonators can retain unconventionally broad, continuous tuning, which is promising for voltage-controlled, tunable nanosystems.
The low-energy excitations of graphene are relativistic massless Dirac fermions with opposite chiralities at valleys K and K. Breaking the chiral symmetry could lead to gap opening in analogy to dynamical mass generation in particle physics. Here we
report direct experimental evidences of chiral symmetry breaking (CSB) from both microscopic and spectroscopic measurements in a Li-intercalated graphene. The CSB is evidenced by gap opening at the Dirac point, Kekule-O type modulation, and chirality mixing near the gap edge. Our work opens up opportunities for investigating CSB related physics in a Kekule-ordered graphene.
A controlled surface wrinkling pattern has been widely used in diverse applications, such as stretchable electronics, smart windows, and haptics. Here, we focus on hexagonal wrinkling patterns because of their great potentials in realizing anisotropi
c and tunable friction and serving as a dynamical template for making non-flat thin films through self-assembling processes. We employ large-scale finite element simulations of a bilayer neo-Hookean solid (e.g., a film bonded on a substrate) to explore mechanical principles that govern the formation of hexagonal wrinkling patterns and strategies for making nearly perfect hexagonal patterns. In our model, the wrinkling instabilities are driven by the confined film expansion. Our results indicate robust hexagonal patterns exist at a relatively small modulus mismatch (on the order of 10) between the film and substrate. Besides, the film expansion should not exceed the onset of wrinkling value too much to avoid post-buckling patterns. By harnessing the imperfection insensitivity of one-dimension sinusoidal wrinkles, we apply a sequential loading to the bilayer structure to produce the nearly perfect hexagonal patterns. Lastly, we discuss the connection between the simple bilayer model and the gradient structures commonly existed in experiments.
Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates,
may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional forecasts of patient demand are commonly available as a Poisson random variable, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the `second wave of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased, unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.
Gravitational waves from binary neutron star inspirals have been detected along with the electromagnetic transients coming from the aftermath of the merger in GW170817. However, much is still unknown about the post-merger dynamics that connects these
two sets of observables. This includes if, and when, the post-merger remnant star collapses to a black hole, and what are the necessary conditions to power a short gamma-ray burst, and other observed electromagnetic counterparts. Observing the collapse of the post-merger neutron star would shed led on these questions, constraining models for the short gamma-ray burst engine and the hot neutron star equation of state. In this work, we explore the scope of using gravitational wave detectors to measure the timing of the collapse either indirectly, by establishing the shut-off of the post-merger gravitational emission, or---more challengingly---directly, by detecting the collapse signal. For the indirect approach, we consider a kilohertz high-frequency detector design that utilises a previously studied coupled arm cavity and signal recycling cavity resonance. This design would give a signal-to-noise ratio of 0.5,-,8.6 (depending on the variation of waveform parameters) for a collapse gravitational wave signal occurring at 10,ms post-merger of a binary at 50,Mpc and with total mass $2.7 M_odot$. For the direct approach, we propose a narrow-band detector design, utilising the sensitivity around the frequency of the arm cavity free spectral range. The proposed detector achieves a signal-to-noise ratio of 0.3,-,1.9, independent of the collapse time. This detector is limited by both the fundamental classical and quantum noise with the arm cavity power chosen as 10,MW.
Quantum noise limits the sensitivity of laser interferometric gravitational-wave detectors. Given the state-of-the-art optics, the optical losses define the lower bound of the best possible quantum-limited detector sensitivity. In this work, we come
up with a broadband signal recycling scheme which gives potential solution to approaching this lower bound by converting the signal recycling cavity to be a broadband signal amplifier using an active optomechanical filter. We will show the difference and advantage of such a scheme compared with the previous white light cavity scheme using the optomechanical filter in [Phys.Rev.Lett.115.211104 (2015)]. The drawback is that the new scheme is more susceptible to the thermal noise of the mechanical oscillator.
We convert the Chinese medical text attributes extraction task into a sequence tagging or machine reading comprehension task. Based on BERT pre-trained models, we have not only tried the widely used LSTM-CRF sequence tagging model, but also other seq
uence models, such as CNN, UCNN, WaveNet, SelfAttention, etc, which reaches similar performance as LSTM+CRF. This sheds a light on the traditional sequence tagging models. Since the aspect of emphasis for different sequence tagging models varies substantially, ensembling these models adds diversity to the final system. By doing so, our system achieves good performance on the task of Chinese medical text attributes extraction (subtask 2 of CCKS 2019 task 1).
Precision measurements using traditional heterodyne readout suffer a 3dB quantum noise penalty compared with homodyne readout. The extra noise is caused by the quantum fluctuations in the image vacuum. We propose a two-carrier gravitational-wave dete
ctor design that evades the 3dB quantum penalty of heterodyne readout. We further propose a new way of realising frequency-dependent squeezing utilising two-mode squeezing in our scheme. It naturally achieves more precise audio frequency signal measurements with radio frequency squeezing. In addition, the detector is compatible with other quantum nondemolition techniques.
