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Central π-core engineering of non-fullerene small molecule acceptors (NF-SMAs) is effective in boosting the performance of organic solar cells (OSCs). Especially, selenium (Se) functionalization of NF-SMAs is considered a promising strategy but the structure-performance relationship remains unclear. Here, we synthesize two isomeric alkylphenyl-substituted selenopheno[3,2-b]thiophene-based NF-SMAs named mPh4F-TS and mPh4F-ST with different substitution positions, and contrast them with the thieno[3,2-b]thiophene-based analogue, mPh4F-TT. When placing Se atoms at the outer positions of the π-core, mPh4F-TS shows the most red-shifted absorption and compact molecular stacking. The PM6 : mPh4F-TS devices exhibit excellent absorption, high charge carrier mobility, and reduced energy loss. Consequently, PM6 : mPh4F-TS achieves more balanced photovoltaic parameters and yields an efficiency of 18.05 %, which highlights that precisely manipulating selenium functionalization is a practicable way toward high-efficiency OSCs.

Summary The growth of the internet of things (IoT) is creating a demand for convenient energy sources, like organic photovoltaics, to power various small IoT devices. Here, we report a highly crystalline small molecular acceptor (named FCC-Cl) with an optical band gap of 1.71 eV suitable for indoor applications. The important design rationale of FCC-Cl is the combination of a weak electron-donating core and a moderate electron-withdrawing end group, which leads to needed band gap and high crystallinity. The OPVs based on D18:FCC-Cl achieved a high external quantum efficiency up to 85% and a high fill factor of 80% due to the high absorption coefficient and strong crystallinity of FCC-Cl. Consequently, an impressive power conversion efficiency of 28.8% was achieved under a 2,600 K LED lamp at 500 lux. It was also demonstrated that PM6:FCC-Cl-based devices can achieve high efficiencies over a wide range of active-layer thicknesses, which is a feature necessary for large-scale roll-to-roll printing processes.

Side-chain engineering has been shown to be an important strategy to optimize Y-series nonfullerene acceptors (NFAs). Most previous reports were focusing on changing the branching positions and size of the alkyl side chains on Y6. In this paper, we investigate the influence of the orientation of side chains on the properties of NFAs and the performance of the organic solar cells (OSCs). Three isomeric NFAs named o-BTP-PhC6, m-BTP-PhC6, and p-BTP-PhC6 are designed by changing the substitution positions and thus orientations of the side chains attached to the central core. Our studies show that the optimal side-chain orientation can be achieved by the meta-positioned hexylphenyl group (of the m-BTP-PhC6 molecule), which introduces significant beneficial effects on optical absorption, intermolecular packing and phase separation of the NFAs. By pairing a donor polymer PTQ10 with m-BTP-PhC6, device efficiencies of 17.7% can be achieved, which is among the best values for PTQ10-based nonfullerene OSC devices so far. These results reveal that regulating side-chain orientations of Y-series NFAs is a promising strategy to achieve favorable morphology, and high charge mobility and solar cell performances.

In this paper, we demonstrate a solution-processed MoSe2 quantum dots/PEDOT:PSS bilayer hole extraction layer (HEL) for non-fullerene organic solar cells (OSCs). It is found that the introduction of MoSe2 QDs can alter the work function and phase separation of PEDOT:PSS, thus affecting the morphology of the active layer and improving the performance of OSCs. The MoSe2 QDs/PEDOT:PSS bilayer HEL can improve the fill factor (FF), short-circuit current density (Jsc) and power conversion efficiency (PCE) of OSCs based on different active layers. The best PCE of up to 17.08% was achieved based on a recently reported active layer binary system named SZ2:N3, which is among the highest reported values to date for OSCs using 2D materials as an interface modifier. Our study indicates that this simple and solution-processed MoSe2 QDs/PEDOT:PSS bilayer thin film could be a potential alternative HEL to the commonly used PEDOT:PSS conducting polymers.

The field of organic solar cells has experienced paradigm-shifting changes in recent years because of the emergence of nonfullerene acceptors (NFAs). It is critically important to gain more insight...

There remain few data about the role of homeostatic compaction in hepatic polarization. A previous study has found that mechanical compaction can accelerate hepatocyte polarization; however, the cellular mechanism underlying the effect is mostly unclear. Hepatocyte nuclear factor 4 alpha (HNF4α) is crucial for hepatic polarization in liver morphogenesis. Therefore, we sought to identify any possible involvement of HNF4αin the process of hepatocyte polarization accelerated by mechanical compaction. We first verified in the nonhepatic cell model HEK-293T, and the hepatic cell model primary hepatocytes that the mechanical compaction on cell aggregates simulated by using transient centrifugation can directly activate the expression of HNF4αpromoters. Moreover, data using primary hepatocytes showed that the HNF4αexpression is positively associated with the levels of compaction force: 2.1-folds higher at the mRNA level and 2.1-folds higher at the protein level for 500 g vs. 0 g. Furthermore, activated HNF4αexpression is associated with the enhanced biliary canalicular formation and the increased production of albumin and urea. Pretreatment with Latrunculin B, an inhibitor of F-actin, and SHE78-7, an inhibitor of E-cadherin, which both interrupt the pathway of mechanical transduction, partially but significantly reduced the HNF4αexpression and production of albumin and urea. In conclusion, HNF4αcan be actively involved in the hepatic polarization in the context of environmental mechanical compaction.

This paper presents a front-end enhancement system for automatic speech recognition to address the cocktail party problem. Cocktail party problem is focus on recognizing the target speech when multiple speakers talk in the noisy real-environments. Many conventional techniques have been proposed. In this work, we propose a new framework to integrate the conventional blind source separation and minimum variance distortionless response beamformer for the speech enhancement and source separation of the recent CHiME-5 challenge. In our experiments, we found that the time–frequency (T–F) mask estimation strategy based on the BSS algorithm should be different for speech enhancement and source separation. The main difference is that whether we need to account for background noise as an additional class during T–F mask estimation. Experimental results showed that the proposed framework was very beneficial to improve the speech recognition performance on the Single-array-track of CHiME-5. We obtained relative 13.5% WER reduction than the official baseline system by only improving the front-end speech enhancement framework.

IntroductionMyopic maculopathy and glaucoma belong to the most common causes of irreversible blindness worldwide and, having an ocular axial elongation as one of their main risk factors, can occur together. The detection of glaucomatous optic neuropathy (GON) in highly myopic eyes is clinically and technically difficult, and there is no information available, neither about the natural course of GON or about the course of GON under intraocular pressure-lowering therapy. We therefore designed this study to explore the natural course of GON in highly myopic eyes.Methods and analysisIn this single-centred longitudinal registry cohort study, 813 highly myopic individuals will be recruited and undergo detailed ophthalmic examinations. High myopia is defined by a myopic refractive error of ≥−6 D or an axial length of ≥26.5 mm. GON is defined by a glaucomatous appearance of the optic nerve head or glaucomatous visual field (VF) defects. GON progression is defined by either change of the optic disc or VF.Ethics and disseminationEthical approval has been obtained from the ethical committee of the Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University, China (ID: 2019KYPJ079). All the participants are required to provide informed consents. Results will be disseminated through scientific meetings and published in peer-reviewed journals. The data will be deposited at the clinical research centre in ZOC using electronic data capture system, and a copy of paper files will also be kept. Only members of the project team will have access to these data.Trial registration numberNCT04302220.

Developing high-performance donor polymers is important for nonfullerene organic solar cells (NF-OSCs), as state-of-the-art nonfullerene acceptors can only perform well if they are coupled with a matching donor with suitable energy levels. However, there are very limited choices of donor polymers for NF-OSCs, and the most commonly used ones are polymers named PM6 and PM7, which suffer from several problems. First, the performance of these polymers (particularly PM7) relies on precise control of their molecular weights. Also, their optimal morphology is extremely sensitive to any structural modification. In this work, a family of donor polymers is developed based on a random polymerization strategy. These polymers can achieve well-controlled morphology and high-performance with a variety of chemical structures and molecular weights. The polymer donors are D-A1-D-A2-type random copolymers in which the D and A1 units are monomers originating from PM6 or PM7, while the A2 unit comprises an electron-deficient core flanked by two thiophene rings with branched alkyl chains. Consequently, multiple cases of highly efficient NF-OSCs are achieved with efficiencies between 16.0% and 17.1%. As the electron-deficient cores can be changed to many other structural units, the strategy can easily expand the choices of high-performance donor polymers for NF-OSCs.

Thiophene and its derivatives have been extensively used in organic electronics, particularly in the field of polymer solar cells (PSCs). Significant research efforts have been dedicated to modifying thiophene-based units by attaching electron-donating or withdrawing groups to tune the energy levels of conjugated materials. Herein, we report the design and synthesis of a novel thiophene derivative, FE-T, featuring a monothiophene functionalized with both an electron-withdrawing fluorine atom (F) and an ester group (E). The FE-T unit possesses distinctive advantages of both F and E groups, the synergistic effects of which enable significant downshifting of the energy levels and enhanced aggregation/crystallinity of the resulting organic materials. Shown in this work are a series of polymers obtained by incorporating the FE-T unit into a PM6 polymer to fine-tune the energetics and morphology of this high-performance PSC material. The optimal polymer in the series shows a downshifted HOMO and an improved morphology, leading to a high PCE of 16.4% with a small energy loss (0.53 eV) enabled by the reduced non-radiative energy loss (0.23 eV), which are among the best values reported for non-fullerene PSCs to date. This work shows that the FE-T unit is a promising building block to construct donor polymers for high-performance organic photovoltaic cells.

The small aperture microphone array becomes more and more popular in the consumer electronics. However, the small aperture usually limits the performance of the traditional DoA estimation methods. The differential microphone array (DMA) has attracted much attention, recently. The DMA has the frequency-independent beampatterns owing to the small size and the dipole is one of the basic types. In this paper, we investigate the relationship between the direction-of-arrival (DoA) and the dipole beampatterns. It shows that the DoA can be directly yielded by an orthogonal dipole pair for the small aperture microphone array. Based on this relationship, we propose a speaker DoA estimation method with orthogonal dipoles (OD). The OD exhibits a good performance to DoA estimation. Nevertheless, it is vulnerable to the axial directions in the reverberant environment. To increase the robustness to the axial directions, we introduce the anti-reverberation function in OD and propose the improved OD method. Both simulations and experiments show that the proposed methods not only significantly outperform the traditional methods but also are much more computationally efficient without the spatial spectrum search.

There remain few data about the role of homeostatic compaction in hepatic polarization. A previous study has found that mechanical compaction can accelerate hepatocyte polarization; however, the cellular mechanism underlying the effect is mostly unclear. Hepatocyte nuclear factor 4 alpha (HNF4α) is crucial for hepatic polarization in liver morphogenesis. Therefore, we sought to identify any possible involvement of HNF4α in the process of hepatocyte polarization accelerated by mechanical compaction. We first verified in the nonhepatic cell model HEK-293T, and the hepatic cell model primary hepatocytes that the mechanical compaction on cell aggregates simulated by using transient centrifugation can directly activate the expression of HNF4α promoters. Moreover, data using primary hepatocytes showed that the HNF4α expression is positively associated with the levels of compaction force: 2.1-folds higher at the mRNA level and 2.1-folds higher at the protein level for 500 g vs. 0 g. Furthermore, activated HNF4α expression is associated with the enhanced biliary canalicular formation and the increased production of albumin and urea. Pretreatment with Latrunculin B, an inhibitor of F-actin, and SHE78-7, an inhibitor of E-cadherin, which both interrupt the pathway of mechanical transduction, partially but significantly reduced the HNF4α expression and production of albumin and urea. In conclusion, HNF4α can be actively involved in the hepatic polarization in the context of environmental mechanical compaction.

The Transformer has shown impressive performance in automatic speech recognition. It uses the encoder-decoder structure with self-attention to learn the relationship between the high-level representation of the source inputs and embedding of the target outputs. In this paper, we propose a novel decoder structure that features a self-and-mixed attention decoder (SMAD) with a deep acoustic structure (DAS) to improve the acoustic representation of Transformer-based LVCSR. Specifically, we introduce a self-attention mechanism to learn a multi-layer deep acoustic structure for multiple levels of acoustic abstraction. We also design a mixed attention mechanism that learns the alignment between different levels of acoustic abstraction and its corresponding linguistic information simultaneously in a shared embedding space. The ASR experiments on Aishell-1 shown that the proposed structure achieves CERs of 4.8% on the dev set and 5.1% on the test set, which are the best results obtained on this task to the best of our knowledge., Comment: Accepted by INTERSPEECH 2020

Although the field of all-polymer solar cells (all-PSCs) has seen rapid progress in device efficiencies during the past few years, there are limited choices of polymer acceptors that exhibit strong ...

Differential microphone array (DMA) designed in the STFT domain has attracted many researches efforts recently for its flexibility and small size. Theoretically, a DMA can achieve a frequency invariant beampattern that is very helpful for many applications. But in practice, the mismatch between the designed beampattern and the ideal DMA pattern is out of control. In this paper, we propose a new measure on the beampattern misalignment, and deduce a convex solution based on constrained least square (CLS) and second-order cone (SOC) optimization. It is verified that the CLS method can provide almost ideal DMA pattern with controllable white noise gain (WNG). We also show that with more microphones, the overall system performance can be further improved.

In the task of mispronunciation detection, the cross-speaker degradation and some other confusing nuisances are the challenging problems demanding prompt solution. In this paper, we will attempt to remove the non-pronunciation variations in the GLDS-SVM expansion space by using nuisance attribute projection strategy, in order to increase the separating capacity between different phoneme instances. Moreover, different kinds of score normalization methods with softmax, posterior probability vector (PPV), Z-norm and T-norm are comparatively discussed. The experiments on three kinds of speech corpora demonstrate the effectiveness of the above methods, and the performance improvement is not very significant, but sustainable.

In automatic speech grading systems, rare research is followed through addressing the issue of GOR (Goodness Of pRosody). In this paper we propose a novel method by taking the advantage of our QBH (Query By Humming) techniques in 2008 MIREX evaluation task. A set of standard samples related to the top-cream students are initially picked up as templates, a cascade QBH structure is then taken from two metrics: the MOMEL stylization followed by DTW distance; the Fujisaki model followed by EMD distance. Sentence GOR is obtained by the fused confidence between target and each template, and forms a weighted sum as the goodness in the passage level. Experiment results indicate that performance increases with the count of template, and Fujisaki-EMD metric outperforms MOMEL-DTW one in terms of correlation. Their combination can be treated as template based GOR score, compensated with our previous feature based GOR score, the approach can achieve 0.432 in correlation and 17.90% in EER in our corpus. Index Terms: speech prosody, query by humming

Automatic assessment of word stress error is an integral part for oral language grading system. However, problems that the property of vowels depends on its context information and the data sparseness of different vowel class are yet to be solved. This paper shall briefly introduce a hybrid method consisting of both traditional prosodic features and proposed context dependent strategies. In classification word stress is determined by weighting a bottom-up fashioned group tree with modified distributed probability score. In experiment, the overall equal error rate of our proposed system achieves 9.41%, which exhibits relative reduction and its competence of use in stress error detection system.

Mispronunciation detection is an important component in computer assisted language learning (CALL) system. In this work, we introduce an efficient GLDS-SVM based detection method, which is successfully used in language and speaker identification systems, and combine it with traditional methods. The main ideas include: extended MFCC features with normalized formant trajectory information, and then propose a novel multi-model strategy for model training to make full use of samples and solve the problem of data unbalance, finally combine GLDS-SVM method with UBM-GMM system to further improve the performance. Experiments show that GLDS-SVM is highly efficient than traditional RBF-SVM, and the fused system can achieve a significant relative improvement of 17.5% in EER reduction, compared with the baseline UBM-GMM system.

Since it is the most natural way for people to search a specific melody in large music database, query by humming/singing is attracting more and more researcherspsila attention in the field of content-based music information retrieval. In this task, note-based and frame-based similarity measures are two commonly used methods. However, in previous works, researchers always focus on one of the two methods alone. In this paper, we propose a novel scheme taking advantage of two different similarity measurements to improve not only the retrieval accuracy but also the retrieving speed. First, Earth Moverpsilas Distance (EMD), which is note-based and much faster, is adopted to eliminate most unlikely candidate. Then, Dynamic Time Warping (DTW), which is frame-based and more accurate, is executed on these surviving candidates. Finally, fusion strategies of these two similarity measurements are employed to improve the performance of whole system. Experiments show our approach can achieve 92.9% accuracy on the database used in MIREX 2006 QBH contest, which is better than those systems participated in that task.

Although researchers have made great progresses on music genre classification in recent years, the need for more accurate system is still not satisfied. In this paper, we propose a method for further reducing the classification error rate based on multiple classifier fusion. First of all, MFCCs and four features from MPEG-7 audio descriptor are extracted in every short time frame, and then a group of frames are gathered into a longer segment, in which mean and variance of these short time frames features are calculated. The segment is considered as the basic unit for training and testing module. Then random forest (RF) and multilayer perceptron neural network (MLP) are executed on such segment independently. Finally, a weighted voting fusion strategy is employed to fusion the result of the two classifiers on each segment, and the whole file decision is made by selecting the most frequently labeled genre over all the segments. Experiments showed that the approach is effective. The fusion result gets 12.4% relative reduction in error rate compared to our baseline system.

For automatic language recognition, performance can be seriously degraded due to the transfer characteristics of the communication channel. Many methods are proposed to compensate the effect of the environment for better recognition results. In this paper, we propose a histogram based double Gaussian feature normalization method for robust language recognition. Compared with the baseline system, the proposed method achieves a relative error reduction of 17.4%, which shows advantages over other common feature normalization methods in language recognition systems.

Keyword spotting (KWS) is an essential technique for speech information retrieval. When doing offline keyword query on large volume spontaneous speech data, fast and accurate KWS methods are required. In this paper, a novel phone-state matrix based vocabulary-independent KWS method is proposed, which has merits of both hidden Markov model (HMM) based and lattice-based methods. Four KWS systems are compared in our experiments on conversational telephone speech test set. Result shows that compared to the high precision HMM-based KWS system the proposed phone-state matrix system has better equal-error-rate (EER) and false-alarm (FA) performance than the other two lattice-based systems.