Analysis and Design of Adaptive Synchronization of a Complex Dynamical Network with Time-Delayed Nodes and Coupling Delays

This paper is devoted to the study of synchronization problems in uncertain dynamical networks with time-delayed nodes and coupling delays. First, a complex dynamical network model with time-delayed nodes and coupling delays is given. Second, for a complex dynamical network with known or unknown but bounded nonlinear couplings, an adaptive controller is designed, which can ensure that the state of a dynamical network asymptotically synchronizes at the individual node state locally or globally in an arbitrary specified network. Then, the Lyapunov-Krasovskii stability theory is employed to estimate the network coupling parameters. The main results provide sufficient conditions for synchronization under local or global circumstances, respectively. Finally, two typical examples are given, using the M-G system as the nodes of the ring dynamical network and second-order nodes in the dynamical network with time-varying communication delays and switching communication topologies, which illustrate the effectiveness of the proposed controller design methods

To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design

Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. However, practical FL deployment over resource constrained mobile devices confronts multiple challenges. For example, it is not clear how to establish an effective wireless network architecture to support FL over mobile devices. Besides, as modern machine learning models are more and more complex, the local on-device training/intermediate model update in FL is becoming too power hungry/radio resource intensive for mobile devices to afford. To address those challenges, in this paper, we try to bridge another recent surging technology, 5G, with FL, and develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices. Briefly, the 5G featured high data rate helps to relieve the severe communication concern, and the multi-access edge computing (MEC) in 5G provides a perfect network architecture to support FL. Under MEC architecture, we develop flexible weight quantization schemes to facilitate the on-device local training over heterogeneous 5G mobile devices. Observed the fact that the energy consumption of local computing is comparable to that of the model updates via 5G transmissions, we formulate the energy efficient FL problem into a mixed-integer programming problem to elaborately determine the quantization strategies and allocate the wireless bandwidth for heterogeneous 5G mobile devices. The goal is to minimize the overall FL energy consumption (computing + 5G transmissions) over 5G mobile devices while guaranteeing learning performance and training latency. Generalized Benders' Decomposition is applied to develop feasible solutions and extensive simulations are conducted to verify the effectiveness of the proposed scheme.Comment: submitted to MOBIHO

Local-Global Context Aware Transformer for Language-Guided Video Segmentation

We explore the task of language-guided video segmentation (LVS). Previous algorithms mostly adopt 3D CNNs to learn video representation, struggling to capture long-term context and easily suffering from visual-linguistic misalignment. In light of this, we present Locater (local-global context aware Transformer), which augments the Transformer architecture with a finite memory so as to query the entire video with the language expression in an efficient manner. The memory is designed to involve two components -- one for persistently preserving global video content, and one for dynamically gathering local temporal context and segmentation history. Based on the memorized local-global context and the particular content of each frame, Locater holistically and flexibly comprehends the expression as an adaptive query vector for each frame. The vector is used to query the corresponding frame for mask generation. The memory also allows Locater to process videos with linear time complexity and constant size memory, while Transformer-style self-attention computation scales quadratically with sequence length. To thoroughly examine the visual grounding capability of LVS models, we contribute a new LVS dataset, A2D-S+, which is built upon A2D-S dataset but poses increased challenges in disambiguating among similar objects. Experiments on three LVS datasets and our A2D-S+ show that Locater outperforms previous state-of-the-arts. Further, we won the 1st place in the Referring Video Object Segmentation Track of the 3rd Large-scale Video Object Segmentation Challenge, where Locater served as the foundation for the winning solution. Our code and dataset are available at: https://github.com/leonnnop/LocaterComment: Accepted by TPAMI. Code, data: https://github.com/leonnnop/Locate

Strong Photoluminescence Enhancement of MoS2 through Defect Engineering and Oxygen Bonding

We report on a strong photoluminescence (PL) enhancement of monolayer MoS2 through defect engineering and oxygen bonding. Micro- PL and Raman images clearly reveal that the PL enhancement occurs at cracks/defects formed during high temperature vacuum annealing. The PL enhancement at crack/defect sites could be as high as thousands of times after considering the laser spot size. The main reasons of such huge PL enhancement include: (1) the oxygen chemical adsorption induced heavy p doping and the conversion from trion to exciton; (2) the suppression of non-radiative recombination of excitons at defect sites as verified by low temperature PL measurements. First principle calculations reveal a strong binding energy of ~2.395 eV for oxygen molecule adsorbed on an S vacancy of MoS2. The chemical adsorbed oxygen also provides a much more effective charge transfer (0.997 electrons per O2) compared to physical adsorbed oxygen on ideal MoS2 surface. We also demonstrate that the defect engineering and oxygen bonding could be easily realized by oxygen plasma irradiation. X-ray photoelectron spectroscopy further confirms the formation of Mo-O bonding. Our results provide a new route for modulating the optical properties of two dimensional semiconductors. The strong and stable PL from defects sites of MoS2 may have promising applications in optoelectronic devices.Comment: 23 pages, 9 figures, to appear in ACS Nan

Efficient and durable uranium extraction from uranium mine tailings seepage water via a photoelectrochemical method

Current photocatalytic uranium (U) extraction methods have intrinsic obstacles, such as the recombination of charge carriers, and the deactivation of catalysts by extracted U. Here we show that, by applying a bias potential on the photocatalyst, the photoelectrochemical (PEC) method can address these limitations. We demonstrate that, owing to efficient spatial charge-carriers separation driven by the applied bias, the PEC method enables efficient and durable U extraction. The effects of multiple operation conditions are investigated. The U extraction proceeds via single-step one-electron reduction, resulting in the formation of pentavalent U, which can facilitate future studies on this often-overlooked U species. In real seepage water the PEC method achieves an extraction capacity of 0.67 gU m(-3).h(-1) without deactivation for 156 h continuous operation, which is 17 times faster than the photocatalytic method. This work provides an alternative tool for U resource recovery and facilitates future studies on U(V) chemistry