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

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

Recent progress in flexible piezoelectric devices toward human-machine interactions

Human-machine interactions are becoming increasingly required for intelligent sensing and effective manipulation. Recent developments in flexible piezoelectric sensors with short response time and high force-electric interconversion efficiency present a tendency toward facilitating diverse human-machine interactive applications. Here, we review the development of flexible piezoelectric human-machine interactions in the context of robotic control, the Internet of Things, sports coaching and acoustic therapeutics. The synthesis of unique materials, the distinct design of device structures, the typical applications of piezoelectric human-machine interactions and the integration of cutting-edge technologies are elaborated in detail based on recent research. Finally, we highlight the current challenges and directions for the development of piezoelectric human-machine interactions for more advanced application scenarios

Open Circuit Fault Diagnosis and Fault Tolerance of Three-Phase Bridgeless Rectifier

Bridgeless rectifiers are widely used in many applications due to a unity power factor, lower conduction loss and high efficiency, which does not need bidirectional energy transmission. In this case, the potential failures are threatening the reliability of these converters in critical applications such as power supply and electric motor driver. In this paper, open circuit fault is analyzed, taking a three-phase bridgeless as an example. Interference on both the input and output side are considered. Then, the fault diagnosis method including detection and location, and fault tolerance through additional switches are proposed. At last, simulation and experiments based on the hardware in loop technology are used to validate the feasibility of fault diagnosis and fault tolerance methodology

Evaluation and Comprehensive Comparison of H-Bridge-Based Bidirectional Rectifier and Unidirectional Rectifiers

This paper presents an evaluation and comprehensive comparison for the topologies which are applied to the front stage of transformer-less cascaded multilevel converter (TCMC). The topologies investigated are targeted at the bidirectional cascaded H-bridge rectifier and three unidirectional rectifiers, including the diode H-bridge cascaded boost rectifier, cascaded bridgeless rectifier and cascaded VIENNA rectifier. First, the operation principles of the unidirectional rectifiers are discussed. Then the performances of these topologies such as power losses, efficiency, device current stress, cost, and total harmonic distortions are analyzed and evaluated respectively. Finally, advantages and disadvantages for each topology are discussed and highlighted. The evaluation and comparison methods presented in this paper and their results are feasible and effective for selecting the appropriate topology in practical applications under different operating conditions

Tamping effect during additive manufacturing of copper coating by cold spray: A comprehensive molecular dynamics study

The tamping effect is of great importance for the microstructure evolution, surface roughness, and mechanical performance of additively manufactured coatings by cold spray. A good understanding of the advantages and disadvantages of the tamping effect is therefore crucial for producing high-quality coatings, unfortunately, very few studies along this direction have been conducted. In this study, a multi-particle impact model was proposed to investigate the tamping effect on the deformation behavior and morphology of Cu particles in cold spray. It was found that the tamping effect could induce a traceable change in the flattening ratio of the first coating layer, which served as a reliable indicator for identifying the critical velocity. By continuously flattening the particles, the tamping effect led to a denser microstructure of the under-layer coating, although such a densification effect would become weaker with the increase of coating thickness. At a high impact velocity, the obstruction and extrusion of surrounding particles were not ignorable, as these interactions could significantly affect the thickness and shape of the central coating and thus affect the overview surface quality of additively manufactured copper coatings. In addition, the particles with a higher impact velocity exhibited a stronger tamping effect during cold spray, which resulted in a higher risk to penetrate the coating with potential damage to the substrate

Investigation of a Novel Hydrogen Depressurization Structure Constituted by an Orifice Plate with Tesla-Type Channels

A hydrogen depressurization system is required to supply the hydrogen to the fuel cell stack from the storage. In this study, a Tesla-type depressurization construction is proposed. Parallel Tesla-type channels are integrated with the traditional orifice plate structure. A computational fluid dynamics (CFD) model is applied to simulate high-pressure hydrogen flow through the proposed structure, using a commercial software package, ANSYS-Fluent (version 19.2, ANSYS, Inc. Southpointe, Canonsburg, PA, USA). The Peng–Robinson (PR) equation of state (EoS) is incorporated into the CFD model to provide an accurate thermophysical property estimation. The construction is optimized by the parametric analysis. The results show that the pressure reduction performance is improved greatly without a significant increase in size. The flow impeding effect of the Tesla-type orifice structure is primarily responsible for the pressure reduction improvement. To enhance the flow impeding effect, modifications are introduced to the Tesla-type channel and the pressure reduction performance has been further improved. Compared to a standard orifice plate, the Tesla-type orifice structure can improve the pressure reduction by 237%. Under low inlet mass flow rates, introduction of a secondary Tesla-type orifice construction can achieve better performance of pressure reduction. Additionally, increasing parallel Tesla-type channels can effectively reduce the maximum Mach number. To further improve the pressure reduction performance, a second set of Tesla-type channels can be introduced to form a two-stage Tesla-type orifice structure. The study provides a feasible structure design to achieve high-efficiency hydrogen depressurization in hydrogen fuel cell vehicles (HFCVs)