Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts

Sentence-level relation extraction aims to identify the relation between two entities for a given sentence. The existing works mostly focus on obtaining a better entity representation and adopting a multi-label classifier for relation extraction. A major limitation of these works is that they ignore background relational knowledge and the interrelation between entity types and candidate relations. In this work, we propose a new paradigm, Contrastive Learning with Descriptive Relation Prompts(CTL-DRP), to jointly consider entity information, relational knowledge and entity type restrictions. In particular, we introduce an improved entity marker and descriptive relation prompts when generating contextual embedding, and utilize contrastive learning to rank the restricted candidate relations. The CTL-DRP obtains a competitive F1-score of 76.7% on TACRED. Furthermore, the new presented paradigm achieves F1-scores of 85.8% and 91.6% on TACREV and Re-TACRED respectively, which are both the state-of-the-art performance

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains. Existing UDA segmentation methods are not suitable for this task because they do not model local information and the cyclical consistency of heartbeat. In this paper, we introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for cardiac structure segmentation. Our GraphEcho comprises two innovative modules, the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle Consistency (TCC) module, which utilize prior knowledge of echocardiogram videos, i.e., consistent cardiac structure across patients and centers and the heartbeat cyclical consistency, respectively. These two modules can better align global and local features from source and target domains, improving UDA segmentation results. Experimental results showed that our GraphEcho outperforms existing state-of-the-art UDA segmentation methods. Our collected dataset and code will be publicly released upon acceptance. This work will lay a new and solid cornerstone for cardiac structure segmentation from echocardiogram videos. Code and dataset are available at: https://github.com/xmed-lab/GraphEchoComment: Accepted By ICCV 202

Low-complexity Resource Allocation for User Paired RSMA in Future 6G Wireless Networks

Rate-splitting multiple access (RSMA) uplink requires optimization of decoding order and power allocation, while decoding order is a discrete variable, and it is very complex to find the optimal decoding order if the number of users is large enough. This letter proposes a low-complexity user pairing-based resource allocation algorithm with the objective of minimizing the maximum latency, which significantly reduces the computational complexity and also achieves similar performance to unpaired uplink RSMA. A closed-form expression for power and bandwidth allocation is first derived, and then a bisection method is used to determine the optimal resource allocation. Finally, the proposed algorithm is compared with unpaired RSMA, paired NOMA and unpaired NOMA. The results demonstrate the effectiveness of the proposed algorithm

GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation

Cardiac structure segmentation from echocardiogram videos plays a crucial role in diagnosing heart disease. The combination of multi-view echocardiogram data is essential to enhance the accuracy and robustness of automated methods. However, due to the visual disparity of the data, deriving cross-view context information remains a challenging task, and unsophisticated fusion strategies can even lower performance. In this study, we propose a novel Gobal-Local fusion (GL-Fusion) network to jointly utilize multi-view information globally and locally that improve the accuracy of echocardiogram analysis. Specifically, a Multi-view Global-based Fusion Module (MGFM) is proposed to extract global context information and to explore the cyclic relationship of different heartbeat cycles in an echocardiogram video. Additionally, a Multi-view Local-based Fusion Module (MLFM) is designed to extract correlations of cardiac structures from different views. Furthermore, we collect a multi-view echocardiogram video dataset (MvEVD) to evaluate our method. Our method achieves an 82.29% average dice score, which demonstrates a 7.83% improvement over the baseline method, and outperforms other existing state-of-the-art methods. To our knowledge, this is the first exploration of a multi-view method for echocardiogram video segmentation. Code available at: https://github.com/xmed-lab/GL-FusionComment: Accepted By MICCAI 202

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.Municipal Science & Technology Commission. Beijing Natural Science Foundation (Grants 3102028 and 3122034)General Logistics Science Foundation (Grant CWS11C108)National Institutes of Health (U.S.) (National Institute of General Medical Sciences (U.S.). Grant R01- EB001659)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) Cooperative Agreement U01- EB-008577

Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.Comment: Accepted as CIKM2023 Short Pape

Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC

This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning, data augmentation, and adversarial training strategies to enhance the model generalization and robustness. For further improvement, we integrate POS information and word semantic representation using a Mixture-of-Experts (MoE) approach. The experimental results show that MoE can overcome the feature overuse issue and combine the context, POS, and word semantic features well. Additionally, we use a model ensemble method for the final prediction, which has been proven effective by many research works

Preparation and imaging of intravascular high-frequency transducer

Intravascular ultrasound (IVUS) imaging is by far the most favorable imaging modality for coronary artery evaluation. IVUS transducer design and fabrication, a key technology for intravascular ultrasound imaging, has a significant impact on the performance of the imaging results. Herein, a 35-MHz side-looking IVUS transducer probe was developed. With a small aperture of 0.40 mm × 0.40 mm, the transducer exhibited a very wide -6 dB bandwidth of 85% and a very low insertion loss of -12 dB. Further, the in vitro IVUS imaging of a porcine coronary artery was performed to clearly display the vessel wall structure while the corresponding color-coded graph was constructed successfully to distinguish necrotic core and fibrous plaque via image processing. The results demonstrated that the imaging performance of the optimized design transducer performs favorably

Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques

In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl.github.io/}. Our solution focuses on enhancing the ranking stage, where we fine-tune pre-trained multilingual transformer-based models with MIRACL dataset. Our model improvement is mainly achieved through diverse data engineering techniques, including the collection of additional relevant training data, data augmentation, and negative sampling. Our fine-tuned model effectively determines the semantic relevance between queries and documents, resulting in a significant improvement in the efficiency of the multilingual information retrieval process. Finally, Our team is pleased to achieve remarkable results in this challenging competition, securing 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard

Effects of the stem extracts of Schisandra glaucescens Diels on collagen-induced arthritis in Balb/c mice

Ethnopharmacological relevance Schisandra glaucescens Diels (SGD) is used in a subclass of traditional Chinese medicine known as “Tujia drugs”. It has been long used for the treatment of rheumatoid arthritis (RA), cough with dyspnea, spontaneous sweating, night sweating, chronic diarrhea, and neurasthenia. As a woody liana growing in mountain jungles at the altitudes of 750–1800 m, it is mainly distributed in Sichuan and Hubei Provinces of China. Aim of the study To evaluate the antiarthritic activity of acetate (EA) and n-butanol (Bu) fractions of SGD extract on a collagen-induced arthritis mice model. Materials and methods Acute toxicity of EA and Bu fractions of SGD extract was evaluated by gavage on normal mice. Pharmacological investigations were conducted on arthritis male Balb/c mice. The animal model was induced by immunization with type II bovine collagen (CII) on the 1st and the 14th day of the experimental schedule. EA fraction (104, 312, 936 mg/kg), Bu fraction (156, 469, 1407 mg/kg) of SGD extract was orally administered every two days since the 15th day for 3 weeks. Progression of edema in the paws was measured using a vernier caliper every 3 days since the 10th day. At the end of the experiment, the spleen index and histological changes of the hind knee joints were investigated. Additionally, to explore the possible antirheumatic mechanisms of the EA and Bu fractions, ELISA was carried out to analyze TNF-α, IL-10, IL-6 and IL-1β in the serum. Results The half lethal doses of both EA and Bu fractions were much higher than the dose administered in the pharmacological investigations. Oral administration of EA fraction and Bu fraction of SGD extract significantly and does-dependently inhibited type ІІ collagen induced arthritis (CIA) in mice, as indicated by the effects on paws swelling and spleen index. Histopathological examinations demonstrated that SGD effectively protected the bones and cartilages of knee joints from erosion, lesion and deformation. Besides, the serum concentrations of cytokines TNF-α, IL-1β and IL-6 were significantly lower than the ones from the vehicle control group. Respectively, while cytokine IL-10 was remarkably higher compare with the vehicle control group. Conclusions SGD might be a safe and effective candidate for the treatment of RA, and deserves further investigation on the chemical components in both EA and Bu fractions of SGD extract