Feasibility and effect of para-right bundle branch pacing in patients with atrial fibrillation and complete atrioventricular block

Background: Chronic right ventricular apex (RVA) pacing can induce negative clinical effects. The aim of the present study was to compare RVA pacing with para-right bundle branch (para-RBB) pacing in terms of electrocardiogram (ECG) and echocardiographic (ECHO) features. Methods: Forty-one consecutive persistent atrial fibrillation patients with an indication for permanent pacing treatment due to complete atrioventricular block were randomly assigned to receive a screw-in lead either in the RVA (n = 22) or at the para-RBB (n = 19). Para-RBB pacing leads were located according to the RBB potential recorded by electrophysiology catheter. ECG was recorded before and after implantation. All patients underwent the pacemaker pro­gramming at 1 day, 6 months, 12 months and 24 months after implantation. ECHO examina­tion was performed during follow-up at 6, 12 and 24 months after implantation to assess the heart function and synchronism. Results: There was no significant difference in pacing lead parameters between para-RBB pacing group and RVA pacing group. Compared with RVA pacing group, the para-RBB pacing group obtained a narrower QRS complex, more synchronic ventricular systole, and less nega­tive effect on heart function (p < 0.05). Conclusions: Para-RBB pacing has potential clinical benefits and may be a physiological pacing site.

Higher-order solutions to non-Markovian quantum dynamics via hierarchical functional derivative

Solving realistic quantum systems coupled to an environment is a challenging task. Here we develop a hierarchical functional derivative (HFD) approach for efficiently solving the non-Markovian quantum trajectories of an open quantum system embedded in a bosonic bath. An explicit expression for arbitrary order HFD equation is derived systematically. Moreover, it is found that for an analytically solvable model, this hierarchical equation naturally terminates at a given order and thus becomes exactly solvable. This HFD approach provides a systematic method to study the non-Markovian quantum dynamics of an open system coupled to a bosonic environment.Comment: 5 pages, 2 figure

Unsupervised Domain Adaptation via Discriminative Manifold Propagation

Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. Though deep learning and adversarial strategy made a significant breakthrough in the adaptability of features, there are two issues to be further studied. First, hard-assigned pseudo labels on the target domain are arbitrary and error-prone, and direct application of them may destroy the intrinsic data structure. Second, batch-wise training of deep learning limits the characterization of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability simultaneously. For the first issue, this framework establishes a probabilistic discriminant criterion on the target domain via soft labels. Based on pre-built prototypes, this criterion is extended to a global approximation scheme for the second issue. Manifold metric alignment is adopted to be compatible with the embedding space. The theoretical error bounds of different alignment metrics are derived for constructive guidance. The proposed method can be used to tackle a series of variants of domain adaptation problems, including both vanilla and partial settings. Extensive experiments have been conducted to investigate the method and a comparative study shows the superiority of the discriminative manifold learning framework.Comment: To be published in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Dynamical invariants in non-Markovian quantum state diffusion equation

We find dynamical invariants for open quantum systems described by the non-Markovian quantum state diffusion (QSD) equation. In stark contrast to closed systems where the dynamical invariant can be identical to the system density operator, these dynamical invariants no longer share the equation of motion for the density operator. Moreover, the invariants obtained with from bi-orthonormal basis can be used to render an exact solution to the QSD equation and the corresponding non-Markovian dynamics without using master equations or numerical simulations. Significantly we show that we can apply these dynamic invariants to reverse-engineering a Hamiltonian that is capable of driving the system to the target state, providing a novel way to design control strategy for open quantum systems.Comment: 6 pages, 2 figure