The edge rings of compact graphs

We define a simple graph as compact if it lacks even cycles and satisfies the odd-cycle condition. Our focus is on classifying all compact graphs and examining the characteristics of their edge rings. Let $G$ be a compact graph and $\mathbb{K}[G]$ be its edge ring. Specifically, we demonstrate that the Cohen-Macaulay type and the projective dimension of $\mathbb{K}[G]$ are both equal to the number of induced cycles of $G$ minus one, and that the regularity of $\mathbb{K}[G]$ is equal to the matching number of $G_0$. Here, $G_0$ is obtained from $G$ by removing the vertices of degree one successively, resulting in a graph where every vertex has a degree greater than 1.Comment: 26 page

Towards LLM-driven Dialogue State Tracking

Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. In this study, we conduct an initial examination of ChatGPT's capabilities in DST. Our evaluation uncovers the exceptional performance of ChatGPT in this task, offering valuable insights to researchers regarding its capabilities and providing useful directions for designing and enhancing dialogue systems. Despite its impressive performance, ChatGPT has significant limitations including its closed-source nature, request restrictions, raising data privacy concerns, and lacking local deployment capabilities. To address these concerns, we present LDST, an LLM-driven DST framework based on smaller, open-source foundation models. By utilizing a novel domain-slot instruction tuning method, LDST achieves performance on par with ChatGPT. Comprehensive evaluations across three distinct experimental settings, we find that LDST exhibits remarkable performance improvements in both zero-shot and few-shot setting compared to previous SOTA methods. The source code is provided for reproducibility.Comment: Accepted at EMNLP 202

How Good Are Large Language Models at Out-of-Distribution Detection?

Out-of-distribution (OOD) detection plays a vital role in enhancing the reliability of machine learning (ML) models. The emergence of large language models (LLMs) has catalyzed a paradigm shift within the ML community, showcasing their exceptional capabilities across diverse natural language processing tasks. While existing research has probed OOD detection with smaller encoder-based Transformers like BERT and RoBERTa, the stark differences in scales, pre-training objectives, and inference paradigms call into question the applicability of these findings to LLMs. This paper embarks on a pioneering empirical investigation of OOD detection in the domain of LLMs, focusing on LLaMA series ranging from 7B to 65B in size. We thoroughly evaluate commonly-used OOD detectors, scrutinizing their performance in both zero-grad and fine-tuning scenarios. Notably, we alter previous discriminative in-distribution fine-tuning into generative fine-tuning, aligning the pre-training objective of LLMs with downstream tasks. Our findings unveil that a simple cosine distance OOD detector demonstrates superior efficacy, outperforming other OOD detectors. We provide an intriguing explanation for this phenomenon by highlighting the isotropic nature of the embedding spaces of LLMs, which distinctly contrasts with the anisotropic property observed in smaller BERT family models. The new insight enhances our understanding of how LLMs detect OOD data, thereby enhancing their adaptability and reliability in dynamic environments.Comment: Work in progres

Psychosocial interventions for suicidal and self-injurious-related behaviors among adolescents: a systematic review and meta-analysis of Chinese practices

BackgroundSuicidal and self-injurious-related behaviors (SSIRBs) are a serious public health challenge in China. However, a comprehensive systematic review of psychosocial interventions for SSIRBs among Chinese adolescents has not been performed. To fill this gap, this systematic review and meta-analysis aimed to examine psychosocial interventions for SSIRBs among Chinese adolescents.MethodsEight international (PubMed, EMBASE, Cochrane Library, ScienceDirect, Clinical Trial, CINAHL, PsycINFO, and Web of Science) and four Chinese (Wanfang, SinoMed, CEPS, and CNKI) databases were searched from inception to 31 January 2023. Data extraction and quality assessment were independently conducted by two groups of researchers. Qualitative synthesis and meta-analysis were both used.ResultsThe initial search yielded 16,872 titles. Of the 649 full texts reviewed, 19 intervention articles focusing on SSIRBs met the inclusion criteria. Thirteen out of the 19 included studies involved cognitive–behavioral therapy (CBT). Seven non-suicidal self-injury (NSSI) studies assessing self-injurious behaviors were included (six short-term studies and three long-term studies). Compared with long-term interventions [−1.30 (95% CI: –1.84, −0.76)], short-term psychosocial interventions had a higher standardized mean difference (SMD) value [1.86 (95% CI: –2.72, −0.99)]. Meta-regression showed an inverse relationship between the treatment response and sample size (slope = 0.068, Z = 2.914, p = 0.004) and proportion of females (slope = 1.096, Z = 5.848, p < 0.001). Subgroup analyses showed that compared with the “less than 1 month” group [−0.494 (−0.783, −0.205)], in the “immediate postintervention” group, the pooled estimate was significantly lower [−2.800 (−4.050, −1.550), p < 0.001].ConclusionOur review systematically summarized the key characteristics and effectiveness of existing psychosocial interventions for SSIRBs among Chinese adolescents. Short-term psychosocial interventions for NSSI were significantly effective in reducing self-injurious behavior scores, especially in the immediate postintervention period. More favorable treatment responses could be observed in both male and small samples

AlphaTracker: a multi-animal tracking and behavioral analysis tool

Computer vision has emerged as a powerful tool to elevate behavioral research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics