96 citations · 320 across the 34 of their papers we have counts for
34 papers
Measurement of the cosmic ray nickel energy spectrum from 10 GeV/n to 2 TeV/n with the DAMPE
F. Alemanno, Q. An, P. Azzarello +144
Nickel, one of the most tightly bound nuclei alongside iron, is the most abundant heavy element beyond iron in cosmic rays. With DAMPE's excellent charge resolution and broad energ…
MixLLM: Dynamic Routing in Mixed Large Language Models
Xinyuan Wang, Yanchi Liu, Wei Cheng +5
Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency. Given mixed LLMs with their own…
Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion
Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen +1
Symmetry in the parameter space of deep neural networks (DNNs) has proven beneficial for various deep learning applications. A well-known example is the permutation symmetry in Mul…
Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating
Binchi Zhang, Zhengzhang Chen, Zaiyi Zheng +2
Large Language Models (LLMs) excel in natural language processing by encoding extensive human knowledge, but their utility relies on timely updates as knowledge evolves. Updating L…
Iterative Feature Space Optimization through Incremental Adaptive Evaluation
Yanping Wu, Yanyong Huang, Zhengzhang Chen +5
Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from…
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
ChengAo Shen, Zhengzhang Chen, Dongsheng Luo +3
Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery met…
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
Minhua Lin, Zhengzhang Chen, Yanchi Liu +6
Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time s…
Online Multi-modal Root Cause Identification in Microservice Systems
Lecheng Zheng, Zhengzhang Chen, Haifeng Chen
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Traditional data-driven RCA methods are typically limited to offline app…
RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection
Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao +4
Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where d…
LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
Lecheng Zheng, Zhengzhang Chen, Dongjie Wang +3
Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. However, progress in this field has been hindered by the lack of large-scale,…
Enhancing atomic ordering, magnetic and transport properties of Mn2VGa Heusler alloy thin films toward negatively spin-polarized charge injection
Z. H. Li, H. Suto, V. Barwal +10
Magnetic materials with negative spin polarization have attracted attention for their potential to increase the design freedom of spintronic devices. This study investigated the ef…
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
Junxiang Wang, Guangji Bai, Wei Cheng +3
Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classifica…
GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection
Yufei Li, Yanchi Liu, Haoyu Wang +6
Logs play a crucial role in system monitoring and debugging by recording valuable system information, including events and states. Although various methods have been proposed to de…
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Chen Ling, Xujiang Zhao, Xuchao Zhang +8
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples. Despite the potenti…
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
Dongjie Wang, Zhengzhang Chen, Yanjie Fu +2
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system fail…
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling, Xujiang Zhao, Jiaying Lu +21
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of app…
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni +4
In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal re…
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection
Xinyang Feng, Dongjin Song, Yuncong Chen +3
Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable.…
Learning Disentangled Representations for Time Series
Yuening Li, Zhengzhang Chen, Daochen Zha +4
Time-series representation learning is a fundamental task for time-series analysis. While significant progress has been made to achieve accurate representations for downstream appl…
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
Liang Tong, Zhengzhang Chen, Jingchao Ni +4
We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: t…
Unsupervised Document Embedding via Contrastive Augmentation
Dongsheng Luo, Wei Cheng, Jingchao Ni +8
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-superv…
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Yinjun Wu, Jingchao Ni, Wei Cheng +7
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…
T-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Denghui Zhang, Yanchi Liu, Wei Cheng +5
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional tur…
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Zhiwei Wang, Zhengzhang Chen, Jingchao Ni +3
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed in…