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Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track in a 35m * 35m space with a top speed of 14m/s. Flight tests are performed on two quadrotors passing through 3 waypoints in a 4m * 2m flight arena to demonstrate the feasibility of the proposed method in the real world. Both simulations and real-world flight tests show that the proposed method can generate the optimal aggressive trajectories for a swarm of autonomous racing drones. The method can also be easily transferred to other types of robot swarms.

Supplemental material, sj-pdf-1-imr-10.1177_03000605231162784 for Renal pseudoaneurysm after holmium laser lithotripsy with flexible ureteroscopy: an unusual case report and literature review by Cong Yin, Fengzhi Chen, Jiahao Jiang, Jinming Xu and Bentao Shi in Journal of International Medical Research

Supplemental material, sj-pdf-1-imr-10.1177_03000605231162784 for Renal pseudoaneurysm after holmium laser lithotripsy with flexible ureteroscopy: an unusual case report and literature review by Cong Yin, Fengzhi Chen, Jiahao Jiang, Jinming Xu and Bentao Shi in Journal of International Medical Research

Energy management strategy (EMS) is a key technology for plug-in hybrid electric vehicles (PHEVs). The energy management of PHEVs needs to output continuous variables such as engine torque, as well as discrete variables such as clutch engagement or disengagement. This type of problem is a mixed-integer programming problem. In addition, the hybrid powertrain system is highly nonlinear and complex. Designing an efficient EMS is a challenging task. We establish a control-oriented mathematical model for a BYD DM-i hybrid powertrain system from the perspective of mixed-integer programming. Then, an EMS based on continuous-discrete reinforcement learning is introduced, which can output both continuous and discrete variables simultaneously. Finally, the effectiveness of the proposed control strategy is verified by comparing EMS based on charge-depleting charge-sustaining (CD-CS) and Dynamic Programming (DP). The simulation results show that the reinforcement learning EMS can improve energy efficiency by 10.08% compared to the CD-CS EMS, and the fuel economy gap is about 6.4% compared with the benchmark global optimum based on DP.

Additional file 2. FIGURE S1 IC50 values of 32 drugs between two subclusters.

Many optimal control problems require the simultaneous output of continuous and discrete control variables. Such problems are usually formulated as mixed-integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space. Numerical methods such as branch-and-bound are computationally expensive and unsuitable for real-time control. This paper proposes a novel continuous-discrete reinforcement learning (CDRL) algorithm, twin delayed deep deterministic actor-Q (TD3AQ), for MIOC problems. TD3AQ combines the advantages of both actor-critic and Q-learning methods, and can handle the continuous and discrete action spaces simultaneously. The proposed algorithm is evaluated on a hybrid electric vehicle (HEV) energy management problem, where real-time control of the continuous variable engine torque and discrete variable gear ratio is essential to maximize fuel economy while satisfying driving constraints. Simulation results on different drive cycles show that TD3AQ can achieve near-optimal solutions compared to dynamic programming (DP) and outperforms the state-of-the-art discrete RL algorithm Rainbow, which is adopted for MIOC by discretizing continuous actions into a finite set of discrete values., Comment: 12 pages, 12 figures

Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose a Robust Fully-Asynchronous Stochastic Gradient Tracking method (R-FAST), where each device performs local computation and communication at its own pace without any form of synchronization. Different from existing asynchronous distributed algorithms, R-FAST can eliminate the impact of data heterogeneity across devices and allow for packet losses by employing a robust gradient tracking strategy that relies on properly designed auxiliary variables for tracking and buffering the overall gradient vector. More importantly, the proposed method utilizes two spanning-tree graphs for communication so long as both share at least one common root, enabling flexible designs in communication architectures. We show that R-FAST converges in expectation to a neighborhood of the optimum with a geometric rate for smooth and strongly convex objectives; and to a stationary point with a sublinear rate for general non-convex settings. Extensive experiments demonstrate that R-FAST runs 1.5-2 times faster than synchronous benchmark algorithms, such as Ring-AllReduce and D-PSGD, while still achieving comparable accuracy, and outperforms existing asynchronous SOTA algorithms, such as AD-PSGD and OSGP, especially in the presence of stragglers.

We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios. The framework hinges on the introduction of an augmented graph consisting of nodes modeling the samples and edges modeling both the inter-device communication and intra-device stochastic gradient computation. By designing properly the topology of the augmented graph, we are able to recover as special cases the renowned Local-SGD and DSGD algorithms, and provide a unified perspective for variance-reduction (VR) and gradient-tracking (GT) methods such as SAGA, Local-SVRG and GT-SAGA. We also provide a unified convergence analysis for smooth and (strongly) convex objectives relying on a proper structured Lyapunov function, and the obtained rate can recover the best known results for many existing algorithms. The rate results further reveal that VR and GT methods can effectively eliminate data heterogeneity within and across devices, respectively, enabling the exact convergence of the algorithm to the optimal solution. Numerical experiments confirm the findings in this paper., Comment: This work will appear in ICML 2022

In this paper, we investigate the problem of coordination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs. Since multiple energy sources are coupled through energy hubs (EHs), the supply-demand constraints are nonconvex. To deal with this issue, we propose a linearization method to transform the coordination problem to a convex social welfare optimization one. Then a decentralized algorithm based on parallel Alternating Direction Method of Multipliers (ADMM) and dynamic average tracking protocol is developed, where each agent could only make decisions based on information from their neighbors. Moreover, by using variational inequality and Lyapunov-based techniques, we show that our algorithm could always converge to the global optimal solution. Finally, a case study on the modified IEEE 14-bus network verifies the feasibility and effectiveness of our algorithm., Comment: The paper has been submitted to 2021 60th IEEE Conference on Decision and Control (CDC)

Supplemental material, sj-pdf-1-imr-10.1177_03000605211049946 for Role of hypermethylated-lncRNAs in the prognosis of bladder cancer patients by Junhua Luo, Jinming Xu, Longhua Ou, Yingchen Zhou, Haichao Yun, Yu Yang, Xionghui Wu and Yan Wang in Journal of International Medical Research

Supplemental material, sj-pdf-1-imr-10.1177_03000605211049946 for Role of hypermethylated-lncRNAs in the prognosis of bladder cancer patients by Junhua Luo, Jinming Xu, Longhua Ou, Yingchen Zhou, Haichao Yun, Yu Yang, Xionghui Wu and Yan Wang in Journal of International Medical Research

This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and gossip communications. The algorithms can also employ acceleration on the computation and communications. We provide a unified analysis of their convergence rate, measured in terms of the Bregman distance associated to the saddle point reformation of the distributed optimization problem. When acceleration is employed, the rate is shown to be optimal, in the sense that it matches (under the proposed metric) existing complexity lower bounds of distributed algorithms applicable to such a class of problem and using only gradient information and gossip communications. Preliminary numerical results on distributed least-square regression problems show that the proposed algorithm compares favorably on existing distributed schemes., Comment: final version for AISTATS 2020

e20016 Background: Accurate lymph node assessment is crucial for clinical patient management and prognostic prediction. However, the procedures may not always be performed preoperatively due to time, cost or risk of procedure itself. Present study aimed to investigate the real world clinical practice of preoperative N staging in surgically resected pathologic T1 lung cancer patients in China. Methods: This is a multicenter retrospective study. Real world data on clinical and pathologic N staging was retrieved from electronic medical records in 10 thoracic surgery centers (LinkDoc Database). Kappa index was calculated to determine the agreement between clinical and pathologic N stage. Results: A total of 11360 patients who underwent surgical resection for pT1 lung cancer were identified during January 2014 to September 2017. Clinical N staging was available for 6057 (53.3%) patients. Of which, 959 (15.8%) patients were achieved by invasive procedures. Considering the 5795 patients with clinical and pathologic N staging data, the overall agreement was 89.0% with a kappa index of 0.4220 (95%CI: 0.3842-0.4598). This was 96.4% for N0, 22.1% for N1 and 39.8% for N2 when considering pathologic N stage as gold standard (Table). In general, upstaging (7.3%) was more common in our population. Conclusions: Over 50% of pathologic T1 patients had preoperative N staging in our clinical practice. Clinical N staging showed low accuracy for predicting lymph node status. The findings of our study highlight the increasing attention on clinical N staging and procedure optimization in China. [Table: see text]

We consider distributed optimization problems in which a group of agents are to collaboratively seek the global optimum through peer-to-peer communication networks. The problem arises in various application areas, such as resource allocation, sensor fusion and distributed learning. We propose a general efficient distributed algorithm--termed Distributed Forward-Backward Bregman Splitting (D-FBBS)--to simultaneously solve the above primal problem as well as its dual based on Bregman method and operator splitting. The proposed algorithm allows agents to communicate asynchronously and thus lends itself to stochastic networks. This algorithm belongs to the family of general proximal point algorithms and is shown to have close connections with some existing well-known algorithms when dealing with fixed networks. However, we will show that it is generally different from the existing ones due to its effectiveness in handling stochastic networks. With proper assumptions, we establish a non-ergodic convergence rate of o(1/k) in terms of fixed point residuals over fixed networks both for D-FBBS and its inexact version (ID-FBBS) which is more computationally efficient and an ergodic convergence rate of O(1/k) for D-FBBS over stochastic networks respectively. We also apply the proposed algorithm to sensor fusion problems to show its superior performance compared to existing methods., Comment: 14 pages,9 figures

Primary non-Hodgkin’s lymphoma of the breast (PBNHL) is uncommon. There have been only a few reports of breast lymphoma in the radiology literature. In this case report, we describe mammographic, sonographic and MRI findings, in addition to surgical and histopathologic findings, in a patient with PBNHL. In particular, the diffusion-weighted imaging sequence of MRI in a patient with PBNHL has yet to be reported in the published literature. Mammographic and sonographic imaging findings are limited in the diagnosis of this rare disease. MRI may play an important role in the assessment of multicentric lesions and staging of mammary malignancies preoperation. MRI may also be useful in the follow-up of breast lymphoma patients to monitor response to chemotherapy and radiotherapy and to diagnose disease recurrence.

e20527Background: Radical resection is preferred for treatment of patients with T1 lung cancer, and patient prognoses could be significantly affected by tumor pathological types. Since the clinical...

As the socialist market economy has matured, basic problems such as training systems, resource allocation, and training models have become "bottlenecks" to the further development of higher education teacher training. It is therefore imperative that training systems be reformed, resource allocation be optimized, and new training models be introduced.