Learning with Silver Standard Data for Zero-shot Relation Extraction

The superior performance of supervised relation extraction (RE) methods heavily relies on a large amount of gold standard data. Recent zero-shot relation extraction methods converted the RE task to other NLP tasks and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of RE annotation data. A potentially valuable by-product of these methods is the large-scale silver standard data. However, there is no further investigation on the use of potentially valuable silver standard data. In this paper, we propose to first detect a small amount of clean data from silver standard data and then use the selected clean data to finetune the pretrained model. We then use the finetuned model to infer relation types. We also propose a class-aware clean data detection module to consider class information when selecting clean data. The experimental results show that our method can outperform the baseline by 12% and 11% on TACRED and Wiki80 dataset in the zero-shot RE task. By using extra silver standard data of different distributions, the performance can be further improved.Comment: 21 pages, 6 figure

Network Traffic Classification Based on External Attention by IP Packet Header

As the emerging services have increasingly strict requirements on quality of service (QoS), such as millisecond network service latency ect., network traffic classification technology is required to assist more advanced network management and monitoring capabilities. So far as we know, the delays of flow-granularity classification methods are difficult to meet the real-time requirements for too long packet-waiting time, whereas the present packet-granularity classification methods may have problems related to privacy protection due to using excessive user payloads. To solve the above problems, we proposed a network traffic classification method only by the IP packet header, which satisfies the requirements of both user's privacy protection and classification performances. We opted to remove the IP address from the header information of the network layer and utilized the remaining 12-byte IP packet header information as input for the model. Additionally, we examined the variations in header value distributions among different categories of network traffic samples. And, the external attention is also introduced to form the online classification framework, which performs well for its low time complexity and strong ability to enhance high-dimensional classification features. The experiments on three open-source datasets show that our average accuracy can reach upon 94.57%, and the classification time is shortened to meet the real-time requirements (0.35ms for a single packet).Comment: 12 pages, 5 figure

Adaptive Policy with Wait-kk Model for Simultaneous Translation

Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-$k$ policy coupled with a standalone wait-$k$ translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-$k$ model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-$k$ model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.Comment: Accept to EMNLP 2023 main conference. 17 pages, 12 figures, 5 table

Expression of ALCAM in clinical colon cancer and relationship with patients' treatment responses

Background/Aim: Activated leukocyte cell adhesion molecule (ALCAM) plays an important role in cancer via its homotypical and heterotypical interactions with ALCAM or other proteins and can also mediate cell-cell interactions. The present study investigated the expression of ALCAM in relation to epithelial–to–mesenchymal transition (EMT) markers and its downstream signal proteins including Ezrin-Moesin-Radixin (ERM), in clinical colon cancer and in the progression of the disease. Materials and Methods: Expression of ALCAM was determined in a clinical colon cancer cohort and assessed against the clinical pathological factors and outcome, together with the expression patterns of the ERM family and EMT markers. ALCAM protein was detected using immunohistochemistry. Cell line models, with ALCAM knock-down and over-expression, were established and used to test cells’ responses to drugs. Results: Tumours from patients who had distant metastasis and died of colon cancer had low levels of ALCAM. Dukes B and C tumours also had lower ALCAM expression than Dukes A tumours. Patients with high levels of ALCAM had a significantly longer overall and disease-free survival than those with lower ALCAM levels (p=0.040 and p=0.044). ALCAM is not only significantly correlated with SNAI1 and TWIST, also positively correlated with SNAI2. ALCAM enhanced the adhesiveness of colorectal cancer, an effect inhibited by both sALCAM and SRC inhibitors. Finally, high ALCAM expression rendered cells resistant, especially to 5-fluorouracil. Conclusion: Reduced expression of ALCAM in colon cancer is an indicator of disease progression and a poor prognostic indicator for patient’s survival. However, ALCAM can enhance the adhesion ability of cancer cells and render them resistant to chemotherapy drugs

Fluorescently conjugated annular fibrin clot for multiplexed real-time digestion analysis

Impaired fibrinolysis has long been considered as a risk factor for venous thromboembolism. Fibrin clots formed at physiological concentrations are promising substrates for monitoring fibrinolytic performance as they offer clot microstructures resembling in vivo. Here we introduce a fluorescently labeled fibrin clot lysis assay which leverages a unique annular clot geometry assayed using a microplate reader. A physiologically relevant fibrin clotting formulation was explored to achieve high assay sensitivity while minimizing labeling impact as fluorescence isothiocyanate (FITC)-fibrin(ogen) conjugations significantly affect both fibrin polymerization and fibrinolysis. Clot characteristics were examined using thromboelastography (TEG), turbidity, scanning electron microscopy, and confocal microscopy. Sample fibrinolytic activities at varying plasmin, plasminogen, and tissue plasminogen activator (tPA) concentrations were assessed in the present study and results were compared to an S2251 chromogenic assay. The optimized physiologically relevant clot substrate showed minimal reporter-conjugation impact with nearly physiological clot properties. The assay demonstrated good reproducibility, wide working range, kinetic read ability, low limit of detection, and the capability to distinguish fibrin binding-related lytic performance. In combination with its ease for multiplexing, it also has applications as a convenient platform for assessing patient fibrinolytic potential and screening thrombolytic drug activities in personalized medical applications