PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable

Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Although effective, they may suffer from significant human effort, knowledge update and domain change in a long run. Therefore, in this article, we focus on exploring the task itself without using any external knowledge. We find all existing works ignore two significant characteristics of ESC. (a) Abundant prior knowledge exists in historical conversations, such as the responses to similar cases and the general order of support strategies, which has a great reference value for current conversation. (b) There is a one-to-many mapping relationship between context and support strategy, i.e.multiple strategies are reasonable for a single context. It lays a better foundation for the diversity of generations. Taking into account these two key factors, we propose Prior Knowledge Enhanced emotional support model with latent variable, PoKE. The proposed model fully taps the potential of prior knowledge in terms of exemplars and strategy sequence and then utilizes a latent variable to model the one-to-many relationship of strategy. Furthermore, we introduce a memory schema to incorporate the encoded knowledge into decoder. Experiment results on benchmark dataset show that our PoKE outperforms existing baselines on both automatic evaluation and human evaluation. Compared with the model using external knowledge, PoKE still can make a slight improvement in some metrics. Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations

NetGPT: Generative Pretrained Transformer for Network Traffic

Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as traffic classification, attack detection, resource scheduling, protocol analysis, and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. Expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks, and outperform state-of-the-art baselines by a wide margin

GCRE-GPT: A Generative Model for Comparative Relation Extraction

Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality). The extracted comparative relations form the basis of further opinion analysis.Existing solutions formulate this task as a sequence labeling task, to extract targets and aspects. However, they cannot directly extract comparative relation(s) from text. In this paper, we show that comparative relations can be directly extracted with high accuracy, by generative model. Based on GPT-2, we propose a Generation-based Comparative Relation Extractor (GCRE-GPT). Experiment results show that \modelname achieves state-of-the-art accuracy on two datasets.Comment: 6 pages, 6 tables, 1 figur

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset (i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art performance on complicated quotation extraction.Comment: Accepted by COLING 202

Spectral-Based Graph Neural Networks for Complementary Item Recommendation

Modeling complementary relationships greatly helps recommender systems to accurately and promptly recommend the subsequent items when one item is purchased. Unlike traditional similar relationships, items with complementary relationships may be purchased successively (such as iPhone and Airpods Pro), and they not only share relevance but also exhibit dissimilarity. Since the two attributes are opposites, modeling complementary relationships is challenging. Previous attempts to exploit these relationships have either ignored or oversimplified the dissimilarity attribute, resulting in ineffective modeling and an inability to balance the two attributes. Since Graph Neural Networks (GNNs) can capture the relevance and dissimilarity between nodes in the spectral domain, we can leverage spectral-based GNNs to effectively understand and model complementary relationships. In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs. We make the first observation that complementary relationships consist of low-frequency and mid-frequency components, corresponding to the relevance and dissimilarity attributes, respectively. Based on this spectral observation, we design spectral graph convolutional networks with low-pass and mid-pass filters to capture the low-frequency and mid-frequency components. Additionally, we propose a two-stage attention mechanism to adaptively integrate and balance the two attributes. Experimental results on four e-commerce datasets demonstrate the effectiveness of our model, with SComGNN significantly outperforming existing baseline models.Comment: Accepted by AAAI-2

FLM-101B: An Open LLM and How to Train It with $100K Budget

Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks, among others. Despite these successes, two main challenges remain in developing LLMs: (i) high computational cost, and (ii) fair and objective evaluations. In this paper, we report a solution to significantly reduce LLM training cost through a growth strategy. We demonstrate that a 101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US dollars. Inspired by IQ tests, we also consolidate an additional range of evaluations on top of existing evaluations that focus on knowledge-oriented abilities. These IQ evaluations include symbolic mapping, rule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model, named FLM-101B, trained with a budget of 100K US dollars, achieves performance comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B, especially on the additional range of IQ evaluations. The checkpoint of FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B

A New Allele of the SPIKE1 Locus Reveals Distinct Regulation of Trichome and Pavement Cell Development and Plant Growth

The single-celled trichomes of Arabidopsis thaliana have long served as an elegant model for elucidating the mechanisms of cell differentiation and morphogenesis due to their unique growth patterns. To identify new components in the genetic network that governs trichome development, we carried out exhaustive screens for additional Arabidopsis mutants with altered trichome morphology. Here, we report one mutant, aberrantly branched trichome1-1 (abt1-1), with a reduced trichome branching phenotype. After positional cloning, a point mutation in the SPIKE1 (SPK1) gene was identified in abt1-1. Further genetic complementation experiments confirmed that abt1-1 is a new allele of SPK1, so abt1-1 was renamed as spk1-7 according to the literatures. spk1-7 and two other spk1 mutant alleles, covering a spectrum of phenotypic severity, highlighted the distinct responses of developmental programs to different SPK1 mutations. Although null spk1 mutants are lethal and show defects in plant stature, trichome and epidermal pavement cell development, only trichome branching is affected in spk1-7. Surprisingly, we found that SPK1 is involved in the positioning of nuclei in the trichome cells. Lastly, through double mutant analysis, we found the coordinated regulation of trichome branching between SPK1 and two other trichome branching regulators, ANGUSTIFOLIA (AN) and ZWICHEL (ZWI). SPK1 might serve for the precise positioning of trichome nuclei, while AN and ZWI contribute to the formation of branch points through governing the cMTs dynamics. In summary, this study presented a fully viable new mutant allele of SPK1 and shed new light on the regulation of trichome branching and other developmental processes by SPK1

Clinical features and outcomes in kidney transplant recipients with COVID-19 pneumonia: a single center retrospective cohort study

ObjectiveThis retrospective cohort study aimed to assess the clinical features, treatment outcomes, and short-term prognosis in kidney transplant recipients (KTRs) with concurrent coronavirus disease 2019 (COVID-19) pneumonia.MethodsKTRs with COVID-19 pneumonia who were admitted to our hospital from December 28, 2022, to March 28, 2023 were included in the study. Their clinical symptoms, responses to antiviral medications, and short-term prognosis were analyzed.ResultsA total of 64 KTRs with initial diagnosis of COVID-19 pneumonia were included in this study. The primary symptoms were fever, cough, and myalgia, with an incidence of 79.7%, 89.1%, and 46.9%, respectively. The administration of antiviral drugs (paxlovid or molnupiravir) within 1–5 days and for over 5 days demonstrated a statistically significant reduction in viral shedding time compared to the group without antiviral medication (P=0.002). Both the paxlovid and molnupiravir treatment groups exhibited a significantly shorter duration of viral shedding time in comparison to the group without antiviral drugs (P=0.002). After 6 months of recovery, there was no significantly negative impact on transplant kidney function (P=0.294).ConclusionFever, cough, and myalgia remain common initial symptoms of concurrent COVID-19 pneumonia in KTRs. Early use of antiviral drugs (paxlovid or molnupiravir) is associated with better therapeutic outcomes. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had a limited impact on the short-term renal function of the KTRs with concurrent moderate or severe COVID-19 pneumonia

A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM

Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS