Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

Open intent detection is a significant problem in natural language understanding, which aims to detect the unseen open intent with the prior knowledge of only known intents. Current methods have two core challenges in this task. On the one hand, they have limitations in learning friendly representations to detect the open intent. On the other hand, there lacks an effective approach to obtaining specific and compact decision boundaries for known intents. To address these issues, this paper introduces an original framework, DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection. Specifically, we first leverage distance information to enhance the distinguishing capability of the intent representations. Then, we design a novel loss function to obtain appropriate decision boundaries by balancing both empirical and open space risks. Extensive experiments show the effectiveness of distance-aware and boundary learning strategies. Compared with the state-of-the-art methods, our method achieves substantial improvements on three benchmark datasets. It also yields robust performance with different proportions of labeled data and known categories. The full data and codes are available at https://github.com/thuiar/TEXTOIRComment: 13 pages, 7 figure

MIntRec2.0: A Novel Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations

Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. However, most existing multimodal intent benchmark datasets are limited in scale and suffer from handling out-of-scope samples that arise in multi-turn conversational interactions. In this paper, we introduce MIntRec2.0, a novel benchmark dataset for multimodal intent recognition in multi-party conversations. It comprises of 1,245 dialogues collected from three TV series, encompassing 15,040 high-quality samples with text, video, and audio information. We expand the existing intent taxonomy to 30 fine-grained intent classes and annotate over 9,300 in-scope and 5,700 out-of-scope instances. This allows for evaluating the effectiveness of both in-scope intent recognition as well as robustness in detecting out-of-scope samples. Moreover, the dataset provides information about the different speakers involved in each dialogue, enabling both single-turn and multi-turn conversational multimodal intent recognition. To demonstrate the efficacy of utilizing multimodal information in conversational intent recognition, we employ classic multimodal fusion methods as benchmark methods. Furthermore, evaluation benchmarks are built with ChatGPT and humans, revealing a substantial performance gap between large language models and humans. To the best of our knowledge, MIntRec2.0 is the first large-scale multimodal dataset for intent recognition and out-of-scope detection, providing a pioneering foundation for further research in this field. The dataset and codes can be accessed through the links provided in the supplementary materials

Inhibition of TNBC Cell Growth by Paroxetine: Induction of Apoptosis and Blockage of Autophagy Flux

The strategy of drug repurposing has gained traction in the field of cancer therapy as a means of discovering novel therapeutic uses for established pharmaceuticals. Paroxetine (PX), a selective serotonin reuptake inhibitor typically utilized in the treatment of depression, has demonstrated promise as an agent for combating cancer. Nevertheless, the specific functions and mechanisms by which PX operates in the context of triple-negative breast cancer (TNBC) remain ambiguous. This study aimed to examine the impact of PX on TNBC cells in vitro as both a standalone treatment and in conjunction with other pharmaceutical agents. Cell viability was measured using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, apoptosis was assessed through flow cytometry, and the effects on signaling pathways were analyzed using RNA sequencing and Western blot techniques. Furthermore, a subcutaneous tumor model was utilized to assess the in vivo efficacy of combination therapy on tumor growth. The results of our study suggest that PX may activate the Ca2+-dependent mitochondria-mediated intrinsic apoptosis pathway in TNBC by potentially influencing the PI3K/AKT/mTOR pathway as well as by inducing cytoprotective autophagy. Additionally, the combination of PX and chemotherapeutic agents demonstrated moderate inhibitory effects on 4T1 tumor growth in an in vivo model. These findings indicate that PX may exert its effects on TNBC through modulation of critical molecular pathways, offering important implications for improving chemosensitivity and identifying potential therapeutic combinations for clinical use

Zanieczyszczenie wód powierzchniowych i podziemnych metalami ciężkimi w kopalni Gejiu Tin, południowo-zachodnie Chiny i jej okolicach

Heavy metal contamination due to mining activity is a global major concern because of its potential health risks to local inhabitants. The heavy metal contamination of surface water and ground water by mining activities in Gejiu tin-polymetallic mining area, Southwest China, was studied. Surface water and ground water were sampled and analyzed using AAS for Cr6+, Cd, As, Hg, Cu, Pb, Zn, Se, Fe and Mn. Analysis of HCO3-, Cl-, SO42-, F-and NO3-in water samples was also undertaken by ion chromatography. It was shown that none of water samples exceeded the guideline of Cr6+, Se and Hg, while the contamination degree of heavy metals was Mn > Fe > Cd > Zn > Pb > As > Cu, all of which were serious contamination except mild contamination for Cu. The ground waters were polluted much worse than surface water.Zanieczyszczenie metalami ciężkimi spowodowane działalnością kopalń jest światowym problemem z powodu ryzyka utraty zdrowia przez okolicznych mieszkańców. Zbadano zanieczyszczenie wód powierzchniowych oraz wód podziemnych wywołane działalnością kopalni cyny w rejonie górniczym Gejiu, południowo-zachodnie Chiny. Pobrano próbki wody powierzchniowej oraz podziemnej i zanalizowano je ze względu na obecność Cd, As, Hg, Cu, Pb, Zn, Se, Fe oraz Mn. Analizy HCO3–, Cl–, SO42–, F– oraz NO3– w próbkach wody również zostały przeprowadzone za pomocą chromatografii jonowej. Ukazano, że żadna z badanych wód nie przekroczyła dopuszczalnych poziomów dla Cr6+, Se oraz Hg. Jednakże poziom zanieczyszczenia metali ciężkimi tj. Mn > Fe > Cd > Zn > Pb > As > Cu był wysoki, za wyjątkiem niewielkiego przekroczenia norm dla Cu. Wody podziemne były znacznie bardziej zanieczyszczone niż wody powierzchniowe.Web of Science1989