A high performance surface acoustic wave visible light sensor using novel materials: Bi2S3 nanobelts

Low dimensional Bi2S3 materials are excellent for use in photodetectors with excellent stability and fast response time. In this work, we developed a visible light sensor with good performance based on surface acoustic wave (SAW) devices using Bi2S3 nanobelts as the sensing materials. The SAW delay-line sensor was fabricated on ST-cut quartz with a designed wavelength of 15.8 microns using conventional photolithography techniques. The measured center frequency was 200.02 MHz. The Bi2S3 nanobelts prepared by a facile hydrothermal process were deposited onto SAW sensors by spin-coating. Under irradiation of 625 nm visible light with a power intensity of 170 μW cm−2, the sensor showed a fast and large response with a frequency upshift of 7 kHz within 1 s. The upshift of the frequency of the SAW device is mainly attributed to the mass loading effect caused by the desorption of oxygen from the Bi2S3 nanobelts under visible light radiation

A high performance surface acoustic wave visible light sensor using novel materials: Bi2S3 nanobelts

Low dimensional Bi2S3 materials are excellent for use in photodetectors with excellent stability and fast response time. In this work, we developed a visible light sensor with good performance based on surface acoustic wave (SAW) devices using Bi2S3 nanobelts as the sensing materials. The SAW delay-line sensor was fabricated on ST-cut quartz with a designed wavelength of 15.8 microns using conventional photolithography techniques. The measured center frequency was 200.02 MHz. The Bi2S3 nanobelts prepared by a facile hydrothermal process were deposited onto SAW sensors by spin-coating. Under irradiation of 625 nm visible light with a power intensity of 170 μW cm−2, the sensor showed a fast and large response with a frequency upshift of 7 kHz within 1 s. The upshift of the frequency of the SAW device is mainly attributed to the mass loading effect caused by the desorption of oxygen from the Bi2S3 nanobelts under visible light radiation

Meeting Action Item Detection with Regularized Context Modeling

Meetings are increasingly important for collaborations. Action items in meeting transcripts are crucial for managing post-meeting to-do tasks, which usually are summarized laboriously. The Action Item Detection task aims to automatically detect meeting content associated with action items. However, datasets manually annotated with action item detection labels are scarce and in small scale. We construct and release the first Chinese meeting corpus with manual action item annotations. In addition, we propose a Context-Drop approach to utilize both local and global contexts by contrastive learning, and achieve better accuracy and robustness for action item detection. We also propose a Lightweight Model Ensemble method to exploit different pre-trained models. Experimental results on our Chinese meeting corpus and the English AMI corpus demonstrate the effectiveness of the proposed approaches.Comment: 5 pages, 2 figures. Paper accepted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes, Greec

Three dimensional spider-web-like superconducting filamentary paths in KxFe2−ySe2K_xFe_{2-y}Se_2 single crystals

Since the discovery of high temperature superconductivity in F-doped LaFeAsO, many new iron based superconductors with different structures have been fabricated2. The observation of superconductivity at about 32 K in KxFe2-ySe2 with the iso-structure of the FeAs-based 122 superconductors was a surprise and immediately stimulated the interests because the band structure calculation8 predicted the absence of the hole pocket which was supposed to be necessary for the theoretical picture of S+- pairing. Soon later, it was found that the material may separate into the insulating antiferromagnetic K2Fe4Se5 phase and the superconducting phase. It remains unresolved that how these two phases coexist and what is the parent phase for superconductivity. In this study we use different quenching processes to produce the target samples with distinct microstructures, and apply multiple measuring techniques to reveal a close relationship between the microstructures and the global appearance of superconductivity. In addition, we clearly illustrate three dimensional spider-web-like superconducting filamentary paths, and for the first time propose that the superconducting phase may originate from a state with one vacancy in every eight Fe-sites with the root8*root10 parallelogram structure.Comment: 22 pages, 7 figure

ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset

Improving BERT with Hybrid Pooling Network and Drop Mask

Transformer-based pre-trained language models, such as BERT, achieve great success in various natural language understanding tasks. Prior research found that BERT captures a rich hierarchy of linguistic information at different layers. However, the vanilla BERT uses the same self-attention mechanism for each layer to model the different contextual features. In this paper, we propose a HybridBERT model which combines self-attention and pooling networks to encode different contextual features in each layer. Additionally, we propose a simple DropMask method to address the mismatch between pre-training and fine-tuning caused by excessive use of special mask tokens during Masked Language Modeling pre-training. Experiments show that HybridBERT outperforms BERT in pre-training with lower loss, faster training speed (8% relative), lower memory cost (13% relative), and also in transfer learning with 1.5% relative higher accuracies on downstream tasks. Additionally, DropMask improves accuracies of BERT on downstream tasks across various masking rates.Comment: 7 pages, 2 figure

Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling

Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural models have greatly promoted the development of long document topic segmentation, but leaving the deeper relationship between coherence and topic segmentation underexplored. Therefore, this paper enhances the ability of supervised models to capture coherence from both logical structure and semantic similarity perspectives to further improve the topic segmentation performance, proposing Topic-aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSL). Specifically, the TSSP task is proposed to force the model to comprehend structural information by learning the original relations between adjacent sentences in a disarrayed document, which is constructed by jointly disrupting the original document at topic and sentence levels. Moreover, we utilize inter- and intra-topic information to construct contrastive samples and design the CSSL objective to ensure that the sentences representations in the same topic have higher similarity, while those in different topics are less similar. Extensive experiments show that the Longformer with our approach significantly outperforms old state-of-the-art (SOTA) methods. Our approach improve $F_1$ of old SOTA by 3.42 (73.74 -> 77.16) and reduces $P_k$ by 1.11 points (15.0 -> 13.89) on WIKI-727K and achieves an average relative reduction of 4.3% on $P_k$ on WikiSection. The average relative $P_k$ drop of 8.38% on two out-of-domain datasets also demonstrates the robustness of our approach.Comment: Accepted by EMNLP 2023. Codes is available at https://github.com/alibaba-damo-academy/SpokenNLP

Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings

Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis reveals that the sentence embeddings from BERT suffer from a bias towards uninformative words, limiting the performance in semantic textual similarity (STS) tasks. To address this bias, we propose a simple and efficient unsupervised approach, Diagonal Attention Pooling (Ditto), which weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. Ditto can be easily applied to any pre-trained language model as a postprocessing operation. Compared to prior sentence embedding approaches, Ditto does not add parameters nor requires any learning. Empirical evaluations demonstrate that our proposed Ditto can alleviate the anisotropy problem and improve various pre-trained models on STS tasks.Comment: 8 pages, accepted by EMNLP 2023 short paper, the source code can be found at https://github.com/alibaba-damo-academy/SpokenNLP/tree/main/ditt