The Equations of Motion of the Post-Newtonian Compact Binary Inspirals As Gravitational Radiation Sources Under The Effective Field Theory Formalism

The success of advanced LIGO/VIRGO detections of gravitational wave signals beginning in 2015 has opened a new window on the universe. Since April 2019, LIGO’s third observing run has identified binary merger candidates with a rate of roughly one per week. In order to understand the properties of all the candidates, it is necessary to construct large template banks of gravitational waveforms. Future upgrades of the LIGO detectors and the next generation detectors with better sensitivity post challenges to the current calculations of waveform solutions. The improvement of the systematic and statistical uncertainties calls for higher accuracy in waveform modeling. It is also crucial to include more physical effects and cover the full parameter space for the future runs. This thesis focuses on the equations of motion of the post-Newtonian compact binary inspirals as gravitational wave sources. The second post-Newtonian order corrections to the radiation reaction is calculated using the Effective Field Theory formalism. The analytical solutions to the equations of motion and spin precession equations are obtained using the dynamical renormalization group method up to the leading order in spin-orbit effects and radiation reaction

Spin Effects in the Effective Field Theory Approach to Post-Minkowskian Conservative Dynamics

Building upon the worldline effective field theory (EFT) formalism for spinning bodies developed for the Post-Newtonian regime, we generalize the EFT approach to Post-Minkowskian (PM) dynamics to include rotational degrees of freedom in a manifestly covariant framework. We introduce a systematic procedure to compute the total change in momentum and spin in the gravitational scattering of compact objects. For the special case of spins aligned with the orbital angular momentum, we show how to construct the radial action for elliptic-like orbits using the Boundary-to-Bound correspondence. As a paradigmatic example, we solve the scattering problem to next-to-leading PM order with linear and bilinear spin effects and arbitrary initial conditions, incorporating for the first time finite-size corrections. We obtain the aligned-spin radial action from the resulting scattering data, and derive the periastron advance and binding energy for circular orbits. We also provide the (square of the) center-of-mass momentum to ${\cal O}(G^2)$, which may be used to reconstruct a Hamiltonian. Our results are in perfect agreement with the existent literature, while at the same time extend the knowledge of the PM dynamics of compact binaries at quadratic order in spins.Comment: 41 pages. 1 ancillary file (wl format

Learning Feature Descriptors for Pre- and Intra-operative Point Cloud Matching for Laparoscopic Liver Registration

Purpose: In laparoscopic liver surgery (LLS), pre-operative information can be overlaid onto the intra-operative scene by registering a 3D pre-operative model to the intra-operative partial surface reconstructed from the laparoscopic video. To assist with this task, we explore the use of learning-based feature descriptors, which, to our best knowledge, have not been explored for use in laparoscopic liver registration. Furthermore, a dataset to train and evaluate the use of learning-based descriptors does not exist. Methods: We present the LiverMatch dataset consisting of 16 preoperative models and their simulated intra-operative 3D surfaces. We also propose the LiverMatch network designed for this task, which outputs per-point feature descriptors, visibility scores, and matched points. Results: We compare the proposed LiverMatch network with anetwork closest to LiverMatch, and a histogram-based 3D descriptor on the testing split of the LiverMatch dataset, which includes two unseen pre-operative models and 1400 intra-operative surfaces. Results suggest that our LiverMatch network can predict more accurate and dense matches than the other two methods and can be seamlessly integrated with a RANSAC-ICP-based registration algorithm to achieve an accurate initial alignment. Conclusion: The use of learning-based feature descriptors in LLR is promising, as it can help achieve an accurate initial rigid alignment, which, in turn, serves as an initialization for subsequent non-rigid registration. We will release the dataset and code upon acceptance

Gravitational radiation from inspiralling compact objects: Spin effects to fourth Post-Newtonian order

The linear- and quadratic-in-spin contributions to the binding potential and gravitational-wave flux from binary systems are derived to next-to-next-to-leading order in the Post-Newtonian (PN) expansion of general relativity, including finite-size and tail effects. The calculation is carried out through the worldline effective field theory framework. We find agreement in the overlap with the available PN literature and test-body limit. As a direct application, we complete the knowledge of spin effects in the evolution of the orbital phase for aligned-spin circular orbits to fourth PN order. We estimate the impact of the new results in the number of accumulated gravitational-wave cycles. We find they will play an important role in providing reliable physical interpretation of gravitational-wave signals from spinning binaries with future detectors such as LISA and the Einstein Telescope.Comment: 5 pages + supplemental material and references. 4 figures. 1 ancillary fil

Radiation Reaction for Non-Spinning Bodies at 4.5PN in the Effective Field Theory Approach

We calculate the 2 post-Newtonian correction to the radiation reaction acceleration for non-spinning binary systems, which amounts to the 4.5 post-Newtonian correction to Newtonian acceleration. The calculation is carried out completely using the effective field theory approach. The center-of-mass corrections to the results are complicated and are discussed in detail. Non-trivial consistency checks are performed and we compare with corresponding results in the literature. Analytic results are supplied in the supplementary materials.Comment: 23 pages. 1 ancillary file (wl format

Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion

We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution, Cerberus, for legged robots that estimates position precisely on various terrains in real time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we also perform online kinematic parameter calibration and contact outlier rejection to substantially reduce position drift. Hardware experiments in various indoor and outdoor environments validate that calibrating kinematic parameters within the Cerberus can reduce estimation drift to lower than 1% during long distance high speed locomotion. Our drift results are better than any other state estimation method using the same set of sensors reported in the literature. Moreover, our state estimator performs well even when the robot is experiencing large impacts and camera occlusion. The implementation of the state estimator, along with the datasets used to compute our results, are available at https://github.com/ShuoYangRobotics/Cerberus.Comment: 7 pages, 6 figures, submitted to IEEE ICRA 202

Phonemic Adversarial Attack against Audio Recognition in Real World

Recently, adversarial attacks for audio recognition have attracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the instance level to generate adversarial noises, which leads to expensive generation time costs and weak universal attacking ability. Motivated by the observations that all audio speech consists of fundamental phonemes, this paper proposes a phonemic adversarial tack (PAT) paradigm, which attacks the fine-grain audio features at the phoneme level commonly shared across audio instances, to generate phonemic adversarial noises, enjoying the more general attacking ability with fast generation speed. Specifically, for accelerating the generation, a phoneme density balanced sampling strategy is introduced to sample quantity less but phonemic features abundant audio instances as the training data via estimating the phoneme density, which substantially alleviates the heavy dependency on the large training dataset. Moreover, for promoting universal attacking ability, the phonemic noise is optimized in an asynchronous way with a sliding window, which enhances the phoneme diversity and thus well captures the critical fundamental phonemic patterns. By conducting extensive experiments, we comprehensively investigate the proposed PAT framework and demonstrate that it outperforms the SOTA baselines by large margins (i.e., at least 11X speed up and 78% attacking ability improvement)