Robust interface between flying and topological qubits

Hybrid architectures, consisting of conventional and topological qubits, have recently attracted much attention due to their capability in consolidating the robustness of topological qubits and the universality of conventional qubits. However, these two kinds of qubits are normally constructed in significantly different energy scales, and thus this energy mismatch is a major obstacle for their coupling that supports the exchange of quantum information between them. Here, we propose a microwave photonic quantum bus for a direct strong coupling between the topological and conventional qubits, in which the energy mismatch is compensated by the external driving field via the fractional ac Josephson effect. In the framework of tight-binding simulation and perturbation theory, we show that the energy splitting of the topological qubits in a finite length nanowire is still robust against local perturbations, which is ensured not only by topology, but also by the particle-hole symmetry. Therefore, the present scheme realizes a robust interface between the flying and topological qubits. Finally, we demonstrate that this quantum bus can also be used to generate multipartitie entangled states with the topological qubits.Comment: Accepted for publication in Scientific Report

Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition

Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulates ASR for each source language into many small ASR tasks and meta-learns a model initialization on all tasks from different source languages to access fast adaptation on unseen target languages. However, for different source languages, the quantity and difficulty vary greatly because of their different data scales and diverse phonological systems, which leads to task-quantity and task-difficulty imbalance issues and thus a failure of multilingual meta-learning ASR (MML-ASR). In this work, we solve this problem by developing a novel adversarial meta sampling (AMS) approach to improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language. Specifically, for each source language, if the query loss is large, it means that its tasks are not well sampled to train ASR model in terms of its quantity and difficulty and thus should be sampled more frequently for extra learning. Inspired by this fact, we feed the historical task query loss of all source language domain into a network to learn a task sampling policy for adversarially increasing the current query loss of MML-ASR. Thus, the learnt task sampling policy can master the learning situation of each language and thus predicts good task sampling probability for each language for more effective learning. Finally, experiment results on two multilingual datasets show significant performance improvement when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS to other low-resource speech tasks and transfer learning ASR approaches.Comment: accepted in AAAI202

Intertwined eco-morphodynamic evolution of salt marshes and tidal channels cutting through them

The formation and development of tidal channels and salt marshes are controlled by complex interactions between hydrodynamics, sediment transport, and vegetation dynamics. Tidal channels affect and, at the same time, are affected by the growth of salt marshes fringing them. The coupled evolution of these morphological units is thus a key ingredient for simulating the typical behaviour of tidal environments. We developed a mathematical model accounting for vegetation-induced flow resistance and wetting-drying processes typical of tidal environments, to investigate the eco-morphodynamic evolution of intertidal areas fringing a main channel and of the tidal creeks cutting through them. Model results indicate that vegetation promotes the development of channel networks, leading to more complex channel structures and higher drainage efficiency. Vegetation encroachment influences sediment deposition patterns by trapping sediment in the seaward and middle intertidal areas, while reducing the amount of sediment delivered to landward areas. In the presence of sea level rise, this deficit of sediment enhances the landward-decreasing trend of the intertidal platform and leads to more isolated vegetation patches. Overall, sea level rise restricts the extension of salt marshes and consequently reduces the effect of vegetation on channel development

Intertwined Eco‐Morphodynamic Evolution of Salt Marshes and Emerging Tidal Channel Networks

The formation and development of tidal channels and salt marshes are controlled by complex interactions between hydrodynamics, sediment transport, and vegetation dynamics. Tidal channels affect and, at the same time, are affected by the growth of salt marshes fringing them. The coupled evolution of these morphological units, mediated by vegetation growth, is thus a key ingredient for simulating the behavior of tidal environments. Considering these two factors, we developed a mathematical model to investigate the eco-morphodynamic evolution of intertidal areas fringing a main channel and of the tidal creeks cutting through them. Model results indicate that vegetation promotes the development of channel networks, leading to more complex channel structures and higher drainage efficiency. Vegetation encroachment influences sediment deposition patterns by trapping sediment in the seaward and middle intertidal areas, while reducing the amount of sediment delivered to landward areas. In the presence of sea level rise, this deficit of sediment enhances the landward-decreasing trend of the intertidal platform and leads to more isolated vegetation patches. Overall, sea level rise restricts the extension of salt marshes and consequently reduces the effect of vegetation on channel network form and function

Discriminative Feature Learning with Foreground Attention for Person Re-Identification

The performance of person re-identification (Re-ID) has been seriously effected by the large cross-view appearance variations caused by mutual occlusions and background clutters. Hence learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches