Bright nonblinking photoluminescence with blinking lifetime from a nanocavity-coupled quantum dot

Colloidal semiconductor quantum dots (QDs) are excellent luminescent nanomaterials for a broad range of optoelectronic applications. Their photoluminescence blinking, however, hinders their practical use in many aspects. It has been shown that coupling QDs to plasmonic nanostructures may provide a viable way to suppress blinking. Nevertheless, the underlying mechanism of blinking suppression remains unclear and debated. Here, by deterministically coupling a single QD to a plasmonic nanocavity, we clarify the mechanism of blinking suppression, and demonstrate unprecedentedly bright emission from a single colloidal QD. In particular, we report for the first time that the coupled system exhibits nonblinking photoluminescence with blinking lifetime, which shows that the elimination of photoluminescence blinking originates from enhanced quantum yield of the charged states. We identify that the radiative decay rate is boosted from (48 ns)-1 to (0.7 ns)-1, which outcompetes Auger processes and enables similar quantum yields for charged and neutral excitons. Moreover, we demonstrate ultrabright photoluminescence of up to 17 million detected photons per second from a single QD. This work sheds new light on the goal of achieving ultrabright nonblinking QDs and may benefit a variety of QD-based applications.Comment: 17 pages; 3 figures

Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Speaker adaptation techniques provide a powerful solution to customise automatic speech recognition (ASR) systems for individual users. Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors. To address these issues, a set of compact and data efficient speaker-dependent (SD) parameter representations are used to facilitate both speaker adaptive training and test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR systems. The sensitivity to supervision quality is reduced using a confidence score-based selection of the less erroneous subset of speaker-level adaptation data. Two lightweight confidence score estimation modules are proposed to produce more reliable confidence scores. The data sparsity issue, which is exacerbated by data selection, is addressed by modelling the SD parameter uncertainty using Bayesian learning. Experiments on the benchmark 300-hour Switchboard and the 233-hour AMI datasets suggest that the proposed confidence score-based adaptation schemes consistently outperformed the baseline speaker-independent (SI) Conformer model and conventional non-Bayesian, point estimate-based adaptation using no speaker data selection. Similar consistent performance improvements were retained after external Transformer and LSTM language model rescoring. In particular, on the 300-hour Switchboard corpus, statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute (9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also obtained on the AMI development and evaluation sets.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin

Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems

Fundamental modelling differences between hybrid and end-to-end (E2E) automatic speech recognition (ASR) systems create large diversity and complementarity among them. This paper investigates multi-pass rescoring and cross adaptation based system combination approaches for hybrid TDNN and Conformer E2E ASR systems. In multi-pass rescoring, state-of-the-art hybrid LF-MMI trained CNN-TDNN system featuring speed perturbation, SpecAugment and Bayesian learning hidden unit contributions (LHUC) speaker adaptation was used to produce initial N-best outputs before being rescored by the speaker adapted Conformer system using a 2-way cross system score interpolation. In cross adaptation, the hybrid CNN-TDNN system was adapted to the 1-best output of the Conformer system or vice versa. Experiments on the 300-hour Switchboard corpus suggest that the combined systems derived using either of the two system combination approaches outperformed the individual systems. The best combined system obtained using multi-pass rescoring produced statistically significant word error rate (WER) reductions of 2.5% to 3.9% absolute (22.5% to 28.9% relative) over the stand alone Conformer system on the NIST Hub5'00, Rt03 and Rt02 evaluation data.Comment: It' s accepted to ISCA 202

Study on torque algorithm of switched reluctance motor

To solve the torque ripple problem of switched reluctance motor under the traditional control method, a direct torque control method for switched reluctance motor is proposed. Direct torque algorithm controls flux magnitude and direction by querying appropriate voltage vector in switch list. Taking torque as direct control variable can reduce the torque ripple of the motor, which broadens the application fields of switched reluctance motor. Starting with the theory of direct torque algorithm, based on MATLAB/Simulink platform, direct torque control and chopped current control system simulation model are designed. Under the condition that switched reluctance motor model and its load are consistent, it is compared with chopped current algorithm. At last, the feasibility of direct torque algorithm is verified through the platform of hardware experiments. It demonstrates that using direct torque algorithm can make the torque ripple be controlled effectively, which provides a wider application field for the switched reluctance motor

Is payoff necessarily weighted by probability when making a risky choice? Evidence from functional connectivity analysis

How people make decisions under risk remains an as-yet-unresolved but fundamental question. Mainstream theories about risky decision making assume that the core processes involved in reaching a risky decision include weighting each payoff or reward magnitude by its probability and then summing the outcomes. However, recently developed theories question whether payoffs are necessarily weighted by probability when making a risky choice. Using functional connectivity analysis, we aimed to provide neural evidence to answer whether this key assumption of computing expectations holds when making a risky choice. We contrasted a trade-off instruction choice that required participants to integrate probability and payoff information with a preferential choice that did not. Based on the functional connectivity patterns between regions in which activity was detected during both of the decision-making tasks, we classified the regions into two networks. One network includes primarily the left and right lateral prefrontal cortices and posterior parietal cortices, which were found to be related to probability in previous reports, and the other network is composed of the bilateral basal ganglia, which have been implicated in payoff. We also found that connectivity between the payoff network and some regions in the probability network (including the left lateral prefrontal cortices and bilateral inferior parietal lobes) were stronger during the trade-off instruction choice task than during the preferential choice task. This indicates that the functional integration between the probability and payoff networks during preferential choice was not as strong as the integration during trade-off instruction choice. Our results provide neural evidence that the weighting process uniformly predicted by the mainstream theory is unnecessary during preferential choice. Thus, our functional integration findings can provide a new direction for the investigation of the principles of risky decision making