arXiv subjects

Jiarong Liu

Publications and source records attributed to Jiarong Liu.

4 recordsLinked to original sources

arXiv2025

A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection

Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application objectives and heterogeneous metric assumptions. This study introduces a problem-oriented framework that reinterprets existing metrics based on the specific evaluation challenges they are designed to address, rather than their mathematical forms or output structures. We categorize over twenty commonly used metrics into six dimensions: 1) basic accuracy-driven evaluation; 2) timeliness-aware reward mechanisms; 3) tolerance to labeling imprecision; 4) penalties reflecting human-audit cost; 5) robustness against random or inflated scores; and 6) parameter-free comparability for cross-dataset benchmarking. Comprehensive experiments are conducted to examine metric behavior under genuine, random, and oracle detection scenarios. By comparing their resulting score distributions, we quantify each metric's discriminative ability -- its capability to distinguish meaningful detections from random noise. The results show that while most event-level metrics exhibit strong separability, several widely used metrics (e.g., NAB, Point-Adjust) demonstrate limited resistance to random-score inflation. These findings reveal that metric suitability must be inherently task-dependent and aligned with the operational objectives of IoT applications. The proposed framework offers a unified analytical perspective for understanding existing metrics and provides practical guidance for selecting or developing more context-aware, robust, and fair evaluation methodologies for time series anomaly detection.

cs.AI↗

arXiv2025

Exclusive Generation of Single-Atom Sulfur for Ultrahigh Quality Monolayer MoS$_2$ Growth

Preparation of high-quality two-dimensional (2D) transition metal dichalcogenides (TMDCs) is the precondition for realizing their applications. However, the synthesized 2D TMDCs (e.g., MoS$_2$) crystals suffer from low quality due to the massive defects formed during the growth. Here, we report the single-atom sulfur (S1) as a highly reactive sulfur species to grow ultrahigh-quality monolayer MoS$_2$. Derived from battery waste, the sulfurized polyacrylonitrile (SPAN) is found to be exclusive and efficient in releasing S1. The monolayer MoS$_2$ prepared by SPAN exhibits an ultralow defect density of $~7\times 10^{12}$ cm$^{-2}$ and the narrowest photoluminescence (PL) emission peak with full-width at half-maximum of ~47.11 meV at room temperature. Moreover, the statistical resonance Raman and low-temperature PL results further verify the significantly lower defect density and higher optical quality of SPAN-grown MoS$_2$ than the conventional S-powder-grown samples. This work provides an effective approach for preparing ultrahigh-quality 2D single crystals, facilitating their industrial applications.

cond-mat.mtrl-sci↗

arXiv2025

An Inorganic Liquid Crystalline Dispersion with 2D Ferroelectric Moieties

Electro-optical effect based liquid crystal devices have been extensively used in optical modulation techniques, in which the Kerr coefficient reflects the sensitivity of the liquid crystals and determines the strength of the device operational electric field. The Peterlin-Stuart theory and the O'Konski model jointly indicate that a giant Kerr coefficient could be obtained in a material with both a large geometrical anisotropy and an intrinsic polarization, but such a material is not yet reported. Here we reveal a ferroelectric effect in a monolayer two-dimensional mineral vermiculite. A large geometrical anisotropy factor and a large inherent electric dipole together raise the record value of Kerr coefficient by an order of magnitude, till $3.0\times 10^{-4}$ m V$^{-2}$. This finding enables an ultra-low operational electric field of $10^2$-$10^4$ V m$^{-1}$ and the fabrication of electro-optical devices with an inch-level electrode separation, which is not practical previously. Because of its high ultraviolet stability (decay <1% under ultraviolet exposure of 1000 hours), large-scale, and energy-efficiency, prototypical displayable billboards have been fabricated for outdoor interactive scenes. The work provides new insights for both liquid crystal optics and two-dimensional ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2023

Maximum Entropy Heterogeneous-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample complexity, training instability, and the risk of converging to a suboptimal Nash Equilibrium. In this paper, we propose a unified framework for learning stochastic policies to resolve these issues. We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL. Based on the MaxEnt framework, we propose Heterogeneous-Agent Soft Actor-Critic (HASAC) algorithm. Theoretically, we prove the monotonic improvement and convergence to quantal response equilibrium (QRE) properties of HASAC. Furthermore, we generalize a unified template for MaxEnt algorithmic design named Maximum Entropy Heterogeneous-Agent Mirror Learning (MEHAML), which provides any induced method with the same guarantees as HASAC. We evaluate HASAC on six benchmarks: Bi-DexHands, Multi-Agent MuJoCo, StarCraft Multi-Agent Challenge, Google Research Football, Multi-Agent Particle Environment, and Light Aircraft Game. Results show that HASAC consistently outperforms strong baselines, exhibiting better sample efficiency, robustness, and sufficient exploration. See our page at https://sites.google.com/view/meharl.

cs.MA↗