arXiv subjects

Bo Lu

Publications and source records attributed to Bo Lu.

At least 55 records · Page 3Linked to original sources

arXiv2023

Phase-tunable multiple Andreev reflections in a quantum spin Hall strip

A quantum spin Hall strip where different edges are contacted by $s$-wave superconductors with a phase difference $ϕ$ supports Majorana bound states protected by time-reversal symmetry. We study signatures of these states in a four-terminal setup where two Josephson junctions are built on opposite edges of the strip and the phase difference between superconductors can be controlled by an external flux. Applying a voltage bias across the quantum spin Hall strip results in a sequence of conductance peaks from multiple Andreev reflections. We find that this so-called subharmonic gap structure is very sensitive to the phase difference and displays a phase-controlled even-odd effect, where all odd spikes disappear when the Majorana states are formed for $ϕ=π$. Moreover, the remaining even spikes split when the superconductors forming the junction have different gap size. We explain these features by showing that any midgap bound states enhance the transmission of the even order multiple Andreev reflections, while the reduced density of states at the gap edges suppresses the odd order ones.

cond-mat.mes-hall↗

arXiv2023

Tunable Josephson diode effect on the surface of topological insulators

The Josephson rectification effect, where the resistance is finite in one direction while zero in the other, has been recently realized experimentally. The resulting Josephson diode has many potential applications on superconducting devices, including quantum computers. Here, we theoretically show that a superconductor-normal metal-superconductor Josephson junction diode on the two-dimensional surface of a topological insulator has large tunability. The magnitude and sign of the diode quality factor strongly depend on the external magnetic field, gate voltage, and the length of the junction. Such rich properties stem from the interplay between different current-phase relations for the multiple transverse transport channels, and can be used for designing realistic superconducting diode devices.

cond-mat.supr-con↗

arXiv2023

Model-Free 3D Shape Control of Deformable Objects Using Novel Features Based on Modal Analysis

Shape control of deformable objects is a challenging and important robotic problem. This paper proposes a model-free controller using novel 3D global deformation features based on modal analysis. Unlike most existing controllers using geometric features, our controller employs a physically-based deformation feature by decoupling 3D global deformation into low-frequency mode shapes. Although modal analysis is widely adopted in computer vision and simulation, it has not been used in robotic deformation control. We develop a new model-free framework for modal-based deformation control under robot manipulation. Physical interpretation of mode shapes enables us to formulate an analytical deformation Jacobian matrix mapping the robot manipulation onto changes of the modal features. In the Jacobian matrix, unknown geometry and physical properties of the object are treated as low-dimensional modal parameters which can be used to linearly parameterize the closed-loop system. Thus, an adaptive controller with proven stability can be designed to deform the object while online estimating the modal parameters. Simulations and experiments are conducted using linear, planar, and solid objects under different settings. The results not only confirm the superior performance of our controller but also demonstrate its advantages over the baseline method.

cs.RO↗

arXiv2023

Ramsey interferometry with arbitrary coherent-population-trapping pulse sequence

Coherent population trapping (CPT) is a multi-level quantum coherence phenomenon of promising applications in atomic clocks and magnetometers. Particularly, multi-pulse CPT-Ramsey interferometry is a powerful tool for improving the performance of CPT atomic clocks. Most studies on multi-pulse CPT-Ramsey interferometry consider periodic pulse sequence and time-independent detuning. However, to further improve the accuracy and precision, one may modify the spectrum symmetry which involves pulse sequence with time-dependent detuning or phase shift. Here, we theoretically analyze the multi-pulse CPT-Ramsey interferometry under arbitrary pulse sequences of time-dependent detuning and obtain a general analytical formula. Using our formula, we analyze the popular CPT-Ramsey interferometry schemes such as two-pulse symmetric and antisymmetric spectroscopy, and multi-pulse symmetric and antisymmetric spectroscopy. Moreover, we quantitatively obtain the influences of pulse width, pulse period, pulse number, and Rabi frequency under periodic pulses. Our theoretical results can guide the experimental design to improve the performance of atomic clocks via multi-pulse CPT-Ramsey interferometry.

physics.atom-ph↗

arXiv2023

Bayesian optimization of Bose-Einstein condensation via evaporative cooling model

To achieve Bose-Einstein condensation, one may implement evaporative cooling by dynamically regulating the power of laser beams forming the optical dipole trap. We propose and experimentally demonstrate a protocol of Bayesian optimization of Bose-Einstein condensation via the evaporative cooling model. Applying this protocol, pure Bose-Einstein condensate of 87Rb with 2.4X10e4 atoms can be produced via evaporative cooling from the initial stage when the number of atoms is 6.0X10e5 at a temperature of 12μK. In comparison with Bayesian optimization via blackbox experiment, our protocol only needs a few experiments required to verify some close-to-optimal curves for optical dipole trap laser powers, therefore it greatly saves experimental resources.

physics.atom-ph↗

arXiv2022

Theory of diode effect in d-wave superconductor junctions on the surface of topological insulator

Nonreciprocal responses of noncentrosymmetric quantum materials attract recent intensive interests, which is essential for the rectification function in diodes. A recent breakthrough is the discovery of superconducting diode effect. The principle to enlarge rectification effect is highly desired to guide the design of superconducting diode. Here, we study theoretically the Josephson junction S/FI/S (S: $d$-wave superconductor, FI: ferromagnetic insulator) on the surface of a topological insulator (TI). The simultaneous existence of $\sinφ$, $\cosφ$ and $\sin 2φ$ terms with almost the same order in Josephson current $I(φ)$ is essential to get larger values of $Q$ factor given by $Q=(I_{c}^{+} - \mid I_{c}^{-} \mid)/ (I_{c}^{+} + \mid I_{c}^{-} \mid)$ with $I_{c}^{+}={\rm max}(I(φ))$ and the negative one $I_{c}^{-}$ for macroscopic phase difference $φ$ of two superconductors on TI. We find that it can show a very large diode effect by tuning the crystal axes of $d$-wave superconductors and the magnetization of FI. The difference of the maximum Josephson currents $I_{c}$'s between the positive and negative directions can be about factor 2, where the current-phase relation is modified largely from the conventional one. The relevance of the zero energy Majorana bound states at the interface is also revealed. This result can pave a way to realize an efficient superconducting diode with low energy cost.

cond-mat.supr-con↗

arXiv2022

Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery

Objective: The computation of anatomical information and laparoscope position is a fundamental block of surgical navigation in Minimally Invasive Surgery (MIS). Recovering a dense 3D structure of surgical scene using visual cues remains a challenge, and the online laparoscopic tracking primarily relies on external sensors, which increases system complexity. Methods: Here, we propose a learning-driven framework, in which an image-guided laparoscopic localization with 3D reconstructions of complex anatomical structures is obtained. To reconstruct the 3D structure of the whole surgical environment, we first fine-tune a learning-based stereoscopic depth perception method, which is robust to the texture-less and variant soft tissues, for depth estimation. Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction. To estimate poses of new laparoscope views, we achieve a coarse-to-fine localization method, which incorporates our reconstructed 3D model. Results: We evaluate the reconstruction method and the localization module on three datasets, namely, the stereo correspondence and reconstruction of endoscopic data (SCARED), the ex-vivo phantom and tissue data collected with Universal Robot (UR) and Karl Storz Laparoscope, and the in-vivo DaVinci robotic surgery dataset, where the reconstructed 3D structures have rich details of surface texture with an accuracy error under 1.71 mm and the localization module can accurately track the laparoscope with only images as input. Conclusions: Experimental results demonstrate the superior performance of the proposed method in 3D anatomy reconstruction and laparoscopic localization. Significance: The proposed framework can be potentially extended to the current surgical navigation system.

cs.CV↗

arXiv2022

FBG-Based Online Learning and 3-D Shape Control of Unmodeled Continuum and Soft Robots in Unstructured Environments

In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and control technologies are crucial for continuum robots to perform the tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive controller by leveraging FBG shape feedback that can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study by using two continuum robots both integrated with multi-core FBGs, including a robotic-assisted colonoscope and multi-section extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments as well as phantom experiments.

cs.RO↗

arXiv2022

AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy

Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. This is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. The dataset is available at https://autolaparo.github.io.

cs.CV↗

arXiv2022

Matched Design for Marginal Causal Effect on Restricted Mean Survival Time in Observational Studies

Investigating the causal relationship between exposure and the time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using the hazard ratio as a marginal causal effect measure due to its noncollapsibility property. In this paper, we advocate using the restricted mean survival time (RMST) difference as the marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is more robust to outcome model misspecification. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased and the corresponding variance estimator is calculated by accounting for the correlation due to matching. The simulation study also demonstrates that our method has adequate empirical performance and outperforms many competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival.

stat.ME↗

arXiv2022

Tunable Majorana corner modes by orbital-dependent exchange interaction in a two-dimensional topological superconductor

We theoretically study the effect of orbital-dependent exchange field in the formation of second order topological superconductors. We demonstrate that changing the orbital difference can induce topological transition and the Majorana corner modes therein can be manipulated. We further propose to detect the corner modes via a normal probe terminal. The conductance quantization is found to be robust to changes of the relevant system parameters.

cond-mat.supr-con↗

arXiv2022

Identifying Majorana bound states at quantum spin Hall edges using a metallic probe

We study the conductance afforded by a normal-metal probe which is directly contacting the helical edge modes of a quantum spin Hall insulator (QSHI). We show a $2e^2/h$ conductance peak at zero temperature in QSHI-based superconductor--ferromagnet hybrids due to the formation of a single Majorana bound state (MBS). In a corresponding Josephson junction hosting a pair of MBSs, a $4e^2/h$ conductance peak is found at zero temperature. The conductance quantization is robust to changes of the relevant system parameters and, remarkably, remains unaltered with increasing the distance between probe and MBSs. In the low temperature limit, the conductance peak is robust as the probe is placed within the localization length of MBSs. Our findings can therefore provide an effective way to detect the existence of MBSs in QSHI systems.

cond-mat.mes-hall↗

arXiv2022

3D Perception based Imitation Learning under Limited Demonstration for Laparoscope Control in Robotic Surgery

Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking without considering information hidden in surgical scenes are not intelligent enough, while the latest supervised imitation learning (IL)-based methods require expensive sensor data and suffer from distribution mismatch issues caused by limited demonstrations. In this paper, we propose a novel Imitation Learning framework for Laparoscope Control (ILLC) with reinforcement learning (RL), which can efficiently learn the control policy from limited surgical video clips. Specially, we first extract surgical laparoscope trajectories from unlabeled videos as the demonstrations and reconstruct the corresponding surgical scenes. To fully learn from limited motion trajectory demonstrations, we propose Shape Preserving Trajectory Augmentation (SPTA) to augment these data, and build a simulation environment that supports parallel RGB-D rendering to reinforce the RL policy for interacting with the environment efficiently. With adversarial training for IL, we obtain the laparoscope control policy based on the generated rollouts and surgical demonstrations. Extensive experiments are conducted in unseen reconstructed surgical scenes, and our method outperforms the previous IL methods, which proves the feasibility of our unified learning-based framework for laparoscope control.

cs.RO↗

arXiv2022

PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks

In this work, we study the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the ability to plan a goal-directed procedure from real-world videos. Learning structured and plannable state and action spaces directly from unstructured videos is the key technical challenge of our task. There are two problems: first, the appearance gap between the training and validation datasets could be large for unstructured videos; second, these gaps lead to decision errors that compound over the steps. We address these limitations with Planning Transformer (PlaTe), which has the advantage of circumventing the compounding prediction errors that occur with single-step models during long model-based rollouts. Our method simultaneously learns the latent state and action information of assigned tasks and the representations of the decision-making process from human demonstrations. Experiments conducted on real-world instructional videos and an interactive environment show that our method can achieve a better performance in reaching the indicated goal than previous algorithms. We also validated the possibility of applying procedural tasks on a UR-5 platform. We make our code publicly available and support academic research purposes.

cs.RO↗

arXiv2022

Constrained Motion Planning of A Cable-Driven Soft Robot With Compressible Curvature Modeling

A cable-driven soft-bodied robot with redundancy can conduct the trajectory tracking task and in the meanwhile fulfill some extra constraints, such as tracking through an end-effector in designated orientation, or get rid of the evitable manipulator-obstacle collision. Those constraints require rational planning of the robot motion. In this work, we derived the compressible curvature kinematics of a cable-driven soft robot which takes the compressible soft segment into account. The motion planning of the soft robot for a trajectory tracking task in constrained conditions, including fixed orientation end-effector and manipulator-obstacle collision avoidance, has been investigated. The inverse solution of cable actuation was formulated as a damped least-square optimization problem and iteratively computed off-line. The performance of trajectory tracking and the obedience to constraints were evaluated via the simulation we made open-source, as well as the prototype experiments. The method can be generalized to the similar multisegment cable-driven soft robotic systems by customizing the robot parameters for the prior motion planning of the manipulator.

cs.RO↗

arXiv2021

Data-driven Holistic Framework for Automated Laparoscope Optimal View Control with Learning-based Depth Perception

Laparoscopic Field of View (FOV) control is one of the most fundamental and important components in Minimally Invasive Surgery (MIS), nevertheless, the traditional manual holding paradigm may easily bring fatigue to surgical assistants, and misunderstanding between surgeons also hinders assistants to provide a high-quality FOV. Targeting this problem, we here present a data-driven framework to realize an automated laparoscopic optimal FOV control. To achieve this goal, we offline learn a motion strategy of laparoscope relative to the surgeon's hand-held surgical tool from our in-house surgical videos, developing our control domain knowledge and an optimal view generator. To adjust the laparoscope online, we first adopt a learning-based method to segment the two-dimensional (2D) position of the surgical tool, and further leverage this outcome to obtain its scale-aware depth from dense depth estimation results calculated by our novel unsupervised RoboDepth model only with the monocular camera feedback, hence in return fusing the above real-time 3D position into our control loop. To eliminate the misorientation of FOV caused by Remote Center of Motion (RCM) constraints when moving the laparoscope, we propose a novel distortion constraint using an affine map to minimize the visual warping problem, and a null-space controller is also embedded into the framework to optimize all types of errors in a unified and decoupled manner. Experiments are conducted using Universal Robot (UR) and Karl Storz Laparoscope/Instruments, which prove the feasibility of our domain knowledge and learning enabled framework for automated camera control.

cs.RO↗

arXiv2021

SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

Autonomous surgical execution relieves tedious routines and surgeon's fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promising performance for dexterous manipulation, which usually requires the simulation to collect data efficiently and reduce the hardware cost. The existing learning-based simulation platforms for medical robots suffer from limited scenarios and simplified physical interactions, which degrades the real-world performance of learned policies. In this work, we designed SurRoL, an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK). The designed SurRoL integrates a user-friendly RL library for algorithm development and a real-time physics engine, which is able to support more PSM/ECM scenarios and more realistic physical interactions. Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.

cs.RO↗

arXiv2021

Eigen Microstates and Their Evolution of Global Ozone at Different Geopotential Heights

Studies on stratospheric ozone have attracted much attention due to its serious impacts on climate changes and its important role as a tracer of Earth's global circulation. Tropospheric ozone as a main atmospheric pollutant damages human health as well as the growth of vegetation. Yet there is still a lack of a theoretical framework to fully describe the variation of ozone. To understand ozone's spatiotemporal variance, we introduce the eigen microstate method to analyze the global ozone mass mixing ratio (OMMR) between 1979-01-01 and 2020-06-30 at 37 pressure layers. We find that eigen microstates at different geopotential heights can capture different climate phenomena and modes. Without deseasonalization, the first eigen microstates capture the seasonal effect and reveal that the phase of the intra-annual cycle moves with the geopotential heights. After deseasonalization, by contrast, the collective patterns from the overall trend, ENSO, QBO, and tropopause pressure are identified by the first few significant eigen microstates. The theoretical framework proposed here can also be applied to other complex Earth systems.

nlin.AO↗