Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task

In a realistic dialogue system, the input information from users is often subject to various types of input perturbations, which affects the slot-filling task. Although rule-based data augmentation methods have achieved satisfactory results, they fail to exhibit the desired generalization when faced with unknown noise disturbances. In this study, we address the challenges posed by input perturbations in slot filling by proposing Noise-BERT, a unified Perturbation-Robust Framework with Noise Alignment Pre-training. Our framework incorporates two Noise Alignment Pre-training tasks: Slot Masked Prediction and Sentence Noisiness Discrimination, aiming to guide the pre-trained language model in capturing accurate slot information and noise distribution. During fine-tuning, we employ a contrastive learning loss to enhance the semantic representation of entities and labels. Additionally, we introduce an adversarial attack training strategy to improve the model's robustness. Experimental results demonstrate the superiority of our proposed approach over state-of-the-art models, and further analysis confirms its effectiveness and generalization ability.Comment: Accepted by ICASSP 202

Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery

Generalized intent discovery aims to extend a closed-set in-domain intent classifier to an open-world intent set including in-domain and out-of-domain intents. The key challenges lie in pseudo label disambiguation and representation learning. Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn. In this paper, we propose a decoupled prototype learning framework (DPL) to decouple pseudo label disambiguation and representation learning. Specifically, we firstly introduce prototypical contrastive representation learning (PCL) to get discriminative representations. And then we adopt a prototype-based label disambiguation method (PLD) to obtain pseudo labels. We theoretically prove that PCL and PLD work in a collaborative fashion and facilitate pseudo label disambiguation. Experiments and analysis on three benchmark datasets show the effectiveness of our method.Comment: Accepted at ACL2023 main conferenc

Faceptor: A Generalist Model for Face Perception

With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack task extensibility and application efficiency. To tackle this issue, we focus on the unified model structure, exploring a face generalist model. As an intuitive design, Naive Faceptor enables tasks with the same output shape and granularity to share the structural design of the standardized output head, achieving improved task extensibility. Furthermore, Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture, allowing task-specific queries to represent new-coming semantics. This design enhances the unification of model structure while improving application efficiency in terms of storage overhead. Additionally, we introduce Layer-Attention into Faceptor, enabling the model to adaptively select features from optimal layers to perform the desired tasks. Through joint training on 13 face perception datasets, Faceptor achieves exceptional performance in facial landmark localization, face parsing, age estimation, expression recognition, binary attribute classification, and face recognition, achieving or surpassing specialized methods in most tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition. The code and models will be made publicly available at https://github.com/lxq1000/Faceptor

APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection

Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system. Previous OOD detection studies generally work on the assumption that plenty of labeled IND intents exist. In this paper, we focus on a more practical few-shot OOD setting where there are only a few labeled IND data and massive unlabeled mixed data that may belong to IND or OOD. The new scenario carries two key challenges: learning discriminative representations using limited IND data and leveraging unlabeled mixed data. Therefore, we propose an adaptive prototypical pseudo-labeling (APP) method for few-shot OOD detection, including a prototypical OOD detection framework (ProtoOOD) to facilitate low-resource OOD detection using limited IND data, and an adaptive pseudo-labeling method to produce high-quality pseudo OOD\&IND labels. Extensive experiments and analysis demonstrate the effectiveness of our method for few-shot OOD detection

Deterministic processes dominate microbial assembly mechanisms in the gut microbiota of cold-water fish between summer and winter

Exploring the effects of seasonal variation on the gut microbiota of cold-water fish plays an important role in understanding the relationship between seasonal variation and cold-water fish. Gut samples of cold-water fish and environmental samples were collected during summer and winter from the lower reaches of the Yalong River. The results of the 16S rRNA sequencing showed that significant differences were identified in the composition and diversity of gut bacteria of cold-water fish. Co-occurrence network complexity of the gut bacteria of cold-water fish was higher in summer compared to winter (Sum: nodes: 256; edges: 20,450; Win: nodes: 580; edges: 16,725). Furthermore, from summer to winter, the contribution of sediment bacteria (Sum: 5.3%; Win: 23.7%) decreased in the gut bacteria of cold-water fish, while the contribution of water bacteria (Sum: 0%; Win: 27.7%) increased. The normalized stochastic ratio (NST) and infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP) showed that deterministic processes played a more important role than stochastic processes in the microbial assembly mechanism of gut bacteria of cold-water fish. From summer to winter, the contribution of deterministic processes to gut bacteria community assembly mechanisms decreased, while the contribution of stochastic processes increased. Overall, these results demonstrated that seasonal variation influenced the gut bacteria of cold-water fish and served as a potential reference for future research to understand the adaptation of fish to varying environments

MicroRNA-192 targeting retinoblastoma 1 inhibits cell proliferation and induces cell apoptosis in lung cancer cells

microRNAs play an important roles in cell growth, differentiation, proliferation and apoptosis. They can function either as tumor suppressors or oncogenes. We found that the overexpression of miR-192 inhibited cell proliferation in A549, H460 and 95D cells, and inhibited tumorigenesis in a nude mouse model. Both caspase-7 and the PARP protein were activated by the overexpression of miR-192, thus suggesting that miR-192 induces cell apoptosis through the caspase pathway. Further studies showed that retinoblastoma 1 (RB1) is a direct target of miR-192. Over-expression of miR-192 decreased RB1 mRNA and protein levels and repressed RB1-3′-UTR reporter activity. Knockdown of RB1 using siRNA resulted in a similar cell morphology as that observed for overexpression of miR-192. Additionally, RB1-siRNA treatment inhibited cell proliferation and induced cell apoptosis in lung cancer cells. Analysis of miRNA expression in clinical samples showed that miR-192 is significantly downregulated in lung cancer tissues compared to adjacent non-cancerous lung tissues. In conclusion, our results demonstrate that miR-192 is a tumor suppressor that can target the RB1 gene to inhibit cell proliferation and induce cell apoptosis in lung cancer cells. Furthermore, miR-192 was expressed at low levels in lung cancer samples, indicating that it might be a promising therapeutic target for lung cancer treatment

Effect of salinity on the decomposition of soil organic carbon in a tidal wetland

PurposeClimate warming and sea level rise have the potential to change the salt level of soil in tidal wetlands. And it is important to clarify the process and the mechanism of decomposition of soil organic carbon in a tidal wetland under varying salinities. The aim of this study was to evaluate the impacts of soil salinity on the decomposition rate of organic carbon (DROC) and dissolved organic carbon (DOC) in a tidal wetland.Materials and methodsTwo types of soil (surface soil in Suaeda salsa and bare tidal flat) were collected, air-dried, and homogenized. After adding different content of salt (0g/L, 3g/L, 6g/L, 9g/L, and 12g/L), the soils were incubated for 28days at stable room temperature (252 degrees C) and added by deionized water to maintain the stability of soil moisture. The gases (CO2 and CH4) emission rates of each salt treatment were measured during 28-day incubation. DROC was determined by the sum of daily CO2-C emission rates and daily CH4-C emission rates in this study.Results and discussionSalt addition inhibited the process of gas emissions and DROC. Gases emission rates and DROC of two types of soil showed similar temporal trends, with distinctive drop in the beginning of experiment and no significant decrease followed. Significant difference of DOC among salt treatments was found in the bare tidal flat soil. Variations of partial correlation between DROC and soil salinity and DOC showed similar trends (e.g., in days 9-18, the positive effect of DOC on DROC was greatly promoted (R-2=0.80, p<0.001), and the negative effect of soil salinity was highly improved (R-2=0.93, p<0.001)). Soil properties, in particular DOC, may be primary factors accounting for the discrepancy of gases emission rates and DROC of two types of soil.Conclusions p id=Par4 Increased soil salinity had a negative effect on DROC during 28-day incubation. The impact of soil salinity and DOC on DROC were varied in different phases of laboratory experiment (soil salinity generally had increasingly negative relationship with DROC, but DOC showed most significantly positive relationship in the middle stage of incubation). Both the formation and consumption of DOC may be valuable for more detail research regarding to decomposition of soil organic carbon