The observations of compact star inspirals from LIGO/Virgo provide a valuable tool to study the highly uncertain equation of state (EOS) of dense matter at the densities in which the compact stars reside. It is not clear whether the merging stars are
neutron stars or quark stars containing self-bound quark matter. In this work, we explore the allowed bag-model-like EOSs by assuming the merging stars are strange quark stars (SQSs) from a Bayesian analysis employing the tidal deformability observational data of the GW170817 and GW190425 binary mergers. We consider two extreme states of strange quark matter, either in nonsuperfluid or color-flavor locked (CFL) and find the results in these two cases essentially reconcile. In particular, our results indicate that the sound speed in the SQS matter is approximately a constant close to the conformal limit of $c/sqrt{3}$. The universal relations between the mass, the tidal deformability and the compactness are provided for the SQSs. The most probable values of the maximum mass are found to be $M_{rm TOV}=2.10_{-0.12}^{+0.12}~(2.15_{-0.14}^{+0.16}),M_{odot}$ for normal (CFL) SQSs at a $90%$ confidence level. The corresponding radius and tidal deformability for a $1.4,M_{odot}$ star are $R_{rm 1.4}= 11.50_{-0.55}^{+0.52}~({11.42}_{-0.44}^{+0.52})~rm km$ and $Lambda_{1.4}= {650}_{-190}^{+230}~({630}_{-150}^{+220})$, respectively. We also investigate the possibility of GW190814s secondary component $m_2$ of mass $2.59_{-0.09}^{+0.08},M_{odot}$ being an SQS, and find that it could be a CFL SQS with the pairing gap $Delta$ larger than $244~rm MeV$ and the effective bag parameter $B_{rm eff}^{1/4}$ in the range of $170$ to $192$ MeV, at a $90%$ confidence level.
Tissue deformation in ultrasound (US) imaging leads to geometrical errors when measuring tissues due to the pressure exerted by probes. Such deformation has an even larger effect on 3D US volumes as the correct compounding is limited by the inconsist
ent location and geometry. This work proposes a patient-specified stiffness-based method to correct the tissue deformations in robotic 3D US acquisitions. To obtain the patient-specified model, robotic palpation is performed at sampling positions on the tissue. The contact force, US images and the probe poses of the palpation procedure are recorded. The contact force and the probe poses are used to estimate the nonlinear tissue stiffness. The images are fed to an optical flow algorithm to compute the pixel displacement. Then the pixel-wise tissue deformation under different forces is characterized by a coupled quadratic regression. To correct the deformation at unseen positions on the trajectory for building 3D volumes, an interpolation is performed based on the stiffness values computed at the sampling positions. With the stiffness and recorded force, the tissue displacement could be corrected. The method was validated on two blood vessel phantoms with different stiffness. The results demonstrate that the method can effectively correct the force-induced deformation and finally generate 3D tissue geometries
Robotic three-dimensional (3D) ultrasound (US) imaging has been employed to overcome the drawbacks of traditional US examinations, such as high inter-operator variability and lack of repeatability. However, object movement remains a challenge as unex
pected motion decreases the quality of the 3D compounding. Furthermore, attempted adjustment of objects, e.g., adjusting limbs to display the entire limb artery tree, is not allowed for conventional robotic US systems. To address this challenge, we propose a vision-based robotic US system that can monitor the objects motion and automatically update the sweep trajectory to provide 3D compounded images of the target anatomy seamlessly. To achieve these functions, a depth camera is employed to extract the manually planned sweep trajectory after which the normal direction of the object is estimated using the extracted 3D trajectory. Subsequently, to monitor the movement and further compensate for this motion to accurately follow the trajectory, the position of firmly attached passive markers is tracked in real-time. Finally, a step-wise compounding was performed. The experiments on a gel phantom demonstrate that the system can resume a sweep when the object is not stationary during scanning.
Recently, the radius of neutron star (NS) PSR J0740+6620 was measured by NICER and an updated measurement of neutron skin thickness of ${}^{208}$Pb ($R_{rm skin}^{208}$) was reported by the PREX-II experiment. These new measurements can help us bette
r understand the unknown equation of state (EoS) of dense matter. In this work, we adopt a hybrid parameterization method, which incorporates the nuclear empirical parameterization and some widely used phenomenological parameterizations, to analyze the results of nuclear experiments and astrophysical observations. With the joint Bayesian analysis of GW170817, PSR J0030+0451, and PSR J0740+6620, the parameters that characterize the ultra dense matter EoS are constrained. We find that the slope parameter $L$ is approximately constrained to $70_{-18}^{+21}$ MeV, which predicts $R_{rm skin}^{208}=0.204^{+0.030}_{-0.026},{rm fm}$ by using the universal relation between $R_{rm skin}^{208}$ and $L$. And the bulk properties of canonical $1.4,M_odot$ NS (e.g., $R_{1.4}$ and $Lambda_{1.4}$) as well as the pressure ($P_{2rho_{rm sat}}$) at two times the nuclear saturation density are well constrained by the data, i.e., $R_{1.4}$, $Lambda_{1.4}$, and $P_{2rho_{rm sat}}$ are approximately constrained to $12.3pm0.7$ km, $330_{-100}^{+140}$, and $4.1_{-1.2}^{+1.5}times10^{34},{rm dyn,cm^{-2}}$, respectively. Besides, we find that the Bayes evidences of the hybrid star and normal NS assumptions are comparable, which indicates that current observation data are compatible with quarkyonic matter existing in the core of massive star. Finally, in the case of normal NS assumption, we obtain a constraint for the maximum mass of nonrotating NS $M_{rm TOV}=2.30^{+0.30}_{-0.18}$ $M_odot$. All of the uncertainties reported above are for 68.3% credible levels.
Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra baseline covariates in addition to the strata indicators. We propose to incorporate these extra covariates via auxiliary regressions in the estimation
and inference of unconditional QTEs under CARs. We establish the consistency, limiting distribution, and validity of the multiplier bootstrap of the regression-adjusted QTE estimator. The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional. Even when the auxiliary regression is misspecified, the proposed bootstrap inferential procedure still achieves the nominal rejection probability in the limit under the null. When the auxiliary regression is correctly specified, the regression-adjusted estimator achieves the minimum asymptotic variance. We also derive the optimal pseudo true values for the potentially misspecified parametric model that minimize the asymptotic variance of the corresponding QTE estimator. We demonstrate the finite sample performance of the new estimation and inferential methods using simulations and provide an empirical application to a well-known dataset in education.
Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiation. However, high inter-operator variability and a lack of repeatabil
ity of US image acquisition hinder the implementation of extensive screening programs. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only the real-time US imaging feedback. We first train a U-Net for real-time segmentation of the vascular structure from cross-sectional US images. Then, we represent the detected vascular structure as a 3D point cloud and use it to estimate the longitudinal axis of the target tubular structure and its mean radius by solving a constrained non-linear optimization problem. Iterating the previous processes, the US probe is automatically aligned to the orientation normal to the target tubular tissue and adjusted online to center the tracked tissue based on the spatial calibration. The real-time segmentation result is evaluated both on a phantom and in-vivo on brachial arteries of volunteers. In addition, the whole process is validated both in simulation and physical phantoms. The mean absolute radius error and orientation error ($pm$ SD) in the simulation are $1.16pm0.1~mm$ and $2.7pm3.3^{circ}$, respectively. On a gel phantom, these errors are $1.95pm2.02~mm$ and $3.3pm2.4^{circ}$. This shows that the method is able to automatically screen tubular tissues with an optimal probe orientation (i.e. normal to the vessel) and at the same to accurately estimate the mean radius, both in real-time.
The equation of state (EoS) of the neutron star (NS) matter remains an enigma. In this work we perform the Bayesian parameter inference with the gravitational wave data (GW170817) and mass-radius observations of some NSs (PSR J0030+0451, PSR J0437-47
15, and 4U 1702-429) using the phenomenologically constructed EoS models to search for a potential first-order phase transition. Our phenomenological EoS models take the advantages of current widely used parametrizing methods, which are flexible enough to resemble various theoretical EoS models. We find that the current observation data are still not informative enough to support/rule out phase transition, due to the comparable evidences for models with and without phase transition. However, the bulk properties of the canonical $1.4,M_odot$ NS and the pressure at around $2rho_{rm sat}$ are well constrained by the data, where $rho_{rm sat}$ is the nuclear saturation density. Moreover, strong phase transition at low densities is disfavored, and the $1sigma$ lower bound of transition density is constrained to $1.84rho_{rm sat}$.
Beam-splitter operations are widely used to process information encoded in bosonic modes. In hybrid quantum systems, however, it might be challenging to implement a reliable beam-splitter operation between two distinct bosonic modes. Without beam-spl
itters, some basic operations such as decoupling modes and swapping states between modes can become highly non-trivial or not feasible at all. In this work, we develop novel interference-based protocols for decoupling and swapping selected modes of a multimode bosonic system without requiring beam-splitters. Specifically, for a given generic coupler characterized by a Gaussian unitary process, we show how to decouple a single mode or swap any pair of modes with a constant depth sequence of operations, while maintaining the coupling for the remaining system. These protocols require only multiple uses of the given coupler, interleaved with single-mode Gaussian unitary operations, and thus enable efficient construction of operations crucial to quantum information science, such as high-fidelity quantum transduction. Our results are directly derived from fundamental physical properties of bosonic systems and are therefore broadly applicable to various existing platforms.
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within
each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.
Covariant codes are quantum codes such that a symmetry transformation on the logical system could be realized by a symmetry transformation on the physical system, usually with limited capability of performing quantum error correction (an important ca
se being the Eastin--Knill theorem). The need for understanding the limits of covariant quantum error correction arises in various realms of physics including fault-tolerant quantum computation, condensed matter physics and quantum gravity. Here, we explore covariant quantum error correction with respect to continuous symmetries from the perspectives of quantum metrology and quantum resource theory, establishing solid connections between these formerly disparate fields. We prove new and powerful lower bounds on the infidelity of covariant quantum error correction, which not only extend the scope of previous no-go results but also provide a substantial improvement over existing bounds. Explicit lower bounds are derived for both erasure and depolarizing noises. We also present a type of covariant codes which nearly saturates these lower bounds.
