Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training

We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference. Our method incorporates a gating mechanism and LID loss, enabling transformer experts to learn language-specific information. By combining gated transformer experts with shared transformer layers, we construct multilingual transformer blocks and utilize linear experts to effectively regularize the joint network. The curriculum training scheme leverages LID to guide the gated experts in improving their respective language performance. Experimental results on a bilingual task involving English and Spanish demonstrate significant improvements, with average relative word error reductions of 12.5% and 7.3% compared to the baseline bilingual and monolingual models, respectively. Notably, our method achieves performance comparable to the upper-bound model trained and inferred with oracle LID. Extending our approach to trilingual, quadrilingual, and pentalingual models reveals similar advantages to those observed in the bilingual models, highlighting its ease of extension to multiple languages

Janus monolayers of transition metal dichalcogenides.

Structural symmetry-breaking plays a crucial role in determining the electronic band structures of two-dimensional materials. Tremendous efforts have been devoted to breaking the in-plane symmetry of graphene with electric fields on AB-stacked bilayers or stacked van der Waals heterostructures. In contrast, transition metal dichalcogenide monolayers are semiconductors with intrinsic in-plane asymmetry, leading to direct electronic bandgaps, distinctive optical properties and great potential in optoelectronics. Apart from their in-plane inversion asymmetry, an additional degree of freedom allowing spin manipulation can be induced by breaking the out-of-plane mirror symmetry with external electric fields or, as theoretically proposed, with an asymmetric out-of-plane structural configuration. Here, we report a synthetic strategy to grow Janus monolayers of transition metal dichalcogenides breaking the out-of-plane structural symmetry. In particular, based on a MoS2 monolayer, we fully replace the top-layer S with Se atoms. We confirm the Janus structure of MoSSe directly by means of scanning transmission electron microscopy and energy-dependent X-ray photoelectron spectroscopy, and prove the existence of vertical dipoles by second harmonic generation and piezoresponse force microscopy measurements

Roles of MSH2 and MSH6 in cadmium-induced G2/M checkpoint arrest in Arabidopsis roots

DNA mismatch repair (MMR) proteins have been implicated in sensing and correcting DNA damage, and in governing cell cycle progression in the presence of structurally anomalous nucleotide lesions induced by different stresses in mammalian cells. Here, Arabidopsis seedlings were grown hydroponically on 0.5 × MS media containing cadmium (Cd) at 0–4.0 mg L−1 for 5 d. Flow cytometry results indicated that Cd stress induced a G2/M cell cycle arrest both in MLH1-, MSH2-, MSH6-deficient, and in WT roots, associated with marked changes of G2/M regulatory genes, including ATM, ATR, SOG1, BRCA1, WEE1, CYCD4; 1, MAD2, CDKA;1, CYCB1; 2 and CYCB1; 1. However, the Cd-induced G2/M phase arrest was markedly diminished in the MSH2- and MSH6-deficient roots, while a lack of MLH1 had no effect on Cd-induced G2 phase arrest relative to that in the wild type roots under the corresponding Cd stress. Expression of the above G2/M regulatory genes was altered in MLH1, MSH2 and MSH6-deficient roots in response to Cd treatment. Furthermore, Cd elicited endoreplication in MSH2- and MSH6-deficient roots, but not in MLH1-deficient Arabidopsis roots. Results suggest that MSH2 and MSH6 may act as direct sensors of Cd-mediated DNA damage. Taken together, we conclude that MSH2 and MSH6, but not MLH1, components of the MMR system are involved in the G2 phase arrest and endoreplication induced by Cd stress in Arabidopsis roots

SD-ARX modeling and robust MPC with variable feedback gain for nonlinear systems

As a generalized input-output model, the state-dependent exogenous variable autoregressive (SD-ARX) model has been intensively utilized to model complex nonlinear systems. Considering that more freedom can be provided by the state feedback control with variable feedback gain for constructing robust controllers, we propose a robust model predictive control (RMPC) algorithm with variable feedback gain on the basis of the SD-ARX model. First, the polytopic state space models (SSMs) of the system are constructed and the prediction accuracy of the SSMs is further improved by using the parameter variation rate information of the SD-ARX model. Then, an RMPC algorithm with variable feedback gain is synthesized for increasing the design freedom and enhancing the control performance. Two simulation examples, that is, the modeling and control of a continuous stirred tank reactor (CSTR) and a water tank system, are provided to demonstrate the feasibility and effectiveness of the proposed RMPC algorithm

An Efficient Feature Weighting Method for Support Vector Regression

Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time

A Self-Powered Hybrid SSHI Circuit with a Wide Operation Range for Piezoelectric Energy Harvesting

This paper presents a piezoelectric (PE) energy harvesting circuit, which integrates a Synchronized Switch Harvesting on Inductor (SSHI) circuit and a diode bridge rectifier. A typical SSHI circuit cannot transfer the power from a PE cantilever into the load when the rectified voltage is higher than a certain voltage. The proposed circuit addresses this problem. It uses the two resonant loops for flipping the capacitor voltage and energy transfer in each half cycle. One resonant loop is typically used for the parallel SSHI scheme, and the other for the series SSHI scheme. The hybrid SSHI circuit using the two resonant loops enables the proposed circuit’s output voltage to no longer be limited. The circuit is self-powered and has the capability of starting without the help of an external battery. Eleven simple discrete components prototyped the circuit. The experimental results show that, compared with the full-bridge (FB) circuit, the amount of power harvested from a PE cantilever and the Voltage Range of Interest (VRI) of the proposed circuit is increased by 2.9 times and by 4.4 times, respectively. A power conversion efficiency of 83.2% is achieved

Lensless Computational Imaging Technology Using Deep Convolutional Network

Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction

Trust on the Ratee: A Trust Management System for Social Internet of Vehicles

The integration of social networking concepts with Internet of Vehicles (IoV) has led to the novel paradigm “Social Internet of Vehicles (SIoV),” which enables vehicles to establish social relationships autonomously to improve traffic conditions and service discovery. There is a growing requirement for effective trust management in the SIoV, considering the critical consequences of acting on misleading information spread by malicious nodes. However, most existing trust models are rater-based, where the reputation information of each node is stored in other nodes it has interacted with. This is not suitable for vehicular environment due to the ephemeral nature of the network. To fill this gap, we propose a Ratee-based Trust Management (RTM) system, where each node stores its own reputation information rated by others during past transactions, and a credible CA server is introduced to ensure the integrality and the undeniability of the trust information. RTM is built based on the concept of SIoV, so that the relationships established between nodes can be used to increase the accuracy of the trustworthiness. Experimental results demonstrate that our scheme achieves faster information propagation and higher transaction success rate than the rater-based method, and the time cost when calculating trustworthiness can meet the demand of vehicular networks