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Jianming Deng

Publications and source records attributed to Jianming Deng.

9 recordsLinked to original sources
arXiv2024

Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection

Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these models exhibit a bias for contrast-based images, with diminished performance on texture-based medical images. This study introduces the notion of a population-level context for pathology detection and employs a graph theoretic approach to model and incorporate it into the latent code of an autoencoder via a refinement module we term PopuSense. PopuSense seeks to capture additional intra-group variations inherent in biomedical data that a local or global context of the convolutional model might miss or smooth out. Proof-of-concept experiments on contrast-based and texture-based images, with minimal adaptation, encounter the existing preference for intensity-based input. Nevertheless, PopuSense demonstrates improved separability in contrast-based images, presenting an additional avenue for refining representations learned by a model.

eess.IV↗
arXiv2024

DynamicLight: Two-Stage Dynamic Traffic Signal Timing

Reinforcement learning (RL) is gaining popularity as an effective approach for traffic signal control (TSC) and is increasingly applied in this domain. However, most existing RL methodologies are confined to a single-stage TSC framework, primarily focusing on selecting an appropriate traffic signal phase at fixed action intervals, leading to inflexible and less adaptable phase durations. To address such limitations, we introduce a novel two-stage TSC framework named DynamicLight. This framework initiates with a phase control strategy responsible for determining the optimal traffic phase, followed by a duration control strategy tasked with determining the corresponding phase duration. Experimental results show that DynamicLight outperforms state-of-the-art TSC models and exhibits exceptional model generalization capabilities. Additionally, the robustness and potential for real-world implementation of DynamicLight are further demonstrated and validated through various DynamicLight variants. The code is released at https://github.com/LiangZhang1996/DynamicLight.

cs.LG↗
arXiv2024

DataLight: Offline Data-Driven Traffic Signal Control

Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges. While most RL-based TSC systems typically employ an online approach, facilitating frequent active interaction with the environment, learning such strategies in the real world is impractical due to safety and risk concerns. To tackle these challenges, this study introduces an innovative offline data-driven approach, called DataLight. DataLight employs effective state representations and reward function by capturing vehicular speed information within the environment. It then segments roads to capture spatial information and further enhances the spatially segmented state representations with sequential modeling. The experimental results demonstrate the effectiveness of DataLight, showcasing superior performance compared to both state-of-the-art online and offline TSC methods. Additionally, DataLight exhibits robust learning capabilities concerning real-world deployment issues. The code is available at https://github.com/LiangZhang1996/DataLight.

cs.LG↗
arXiv2023

Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization

Reinforcement learning (RL) techniques for traffic signal control (TSC) have gained increasing popularity in recent years. However, most existing RL-based TSC methods tend to focus primarily on the RL model structure while neglecting the significance of proper traffic state representation. Furthermore, some RL-based methods heavily rely on expert-designed traffic signal phase competition. In this paper, we present a novel approach to TSC that utilizes queue length as an efficient state representation. We propose two new methods: (1) Max Queue-Length (M-QL), an optimization-based traditional method designed based on the property of queue length; and (2) AttentionLight, an RL model that employs the self-attention mechanism to capture the signal phase correlation without requiring human knowledge of phase relationships. Comprehensive experiments on multiple real-world datasets demonstrate the effectiveness of our approach: (1) the M-QL method outperforms the latest RL-based methods; (2) AttentionLight achieves a new state-of-the-art performance; and (3) our results highlight the significance of proper state representation, which is as crucial as neural network design in TSC methods. Our findings have important implications for advancing the development of more effective and efficient TSC methods. Our code is released on Github (https://github. com/LiangZhang1996/AttentionLight).

cs.LG↗
arXiv2021

The Observation of Ferroelastic and Ferrielectric Domains in AgNbO3 Single Crystal

Compared to AgNbO3 based ceramics, the experimental investigations on the single crystalline AgNbO3, especially the ground state and ferroic domain structures, are not on the same level. Here in this work, based on successfully synthesized AgNbO3 single crystal using flux method, we observed the coexistence of ferroelastic and ferrielectric domain structures by a combination study of polarized light microscopy and piezoresponse force microscope, this finding may provide a new aspect for studying AgNbO3. The result also suggests a weak electromechanical response from the ferrielectric phase of AgNbO3 which is also supported by the transmission electron microscope characterization. Our results reveal that the AgNbO3 single crystal is in a polar ferrielectric phase at room temperature, clarifying its ground state which is controversial from the AgNbO3 ceramic materials.

cond-mat.mtrl-sci↗
arXiv2020

Conductive Domain Walls in Non-Oxide Ferroelectrics Sn2P2S6

The conductive domain wall (CDW) is extensively investigated in ferroelectrics, which can be considered as a quasi-two-dimensional reconfigurable conducting channel embedded into an insulating material. Therefore, it is highly important for the application of ferroelectric nanoelectronics. Hitherto, most CDW investigations are restricted in oxides, and limited work has been reported in non-oxides to the contrary. Here, by successfully synthesizing the non-oxide ferroelectric Sn2P2S6 single crystal, we observed and confirmed the domain wall conductivity by using different scanning probe techniques which origins from the nature of inclined domain walls. Moreover, the domains separated by CDW also exhibit distinguishable electrical conductivity due to the interfacial polarization charge with opposite signs. The result provides a novel platform for understanding electrical conductivity behavior of the domains and domain walls in non-oxide ferroelectrics.

cond-mat.mtrl-sci↗
arXiv2019

Thickness-dependent in-plane polarization and structural phase transition in van der Waals Ferroelectric CuInP2S6

Van der Waals (vdW) layered materials have rather weaker interlayer bonding than the intra-layer bonding, therefore the exfoliation along the stacking direction enables the achievement of monolayer or few layers vdW materials with emerging novel physical properties and functionalities. The ferroelectricity in vdW materials recently attracts renewed interest for the potential use in high-density storage devices. As the thickness going thinner, the competition between the surface energy, depolarization field and interfacial chemical bonds may give rise to the modification of ferroelectricity and crystalline structure, which has limited investigations. In this work, combining the piezoresponse force microscope scanning, contact resonance imaging, we report the existence of the intrinsic in-plane polarization in vdW ferroelectrics CuInP2S6 (CIPS) single crystals, whereas below a critical thickness between 90-100 nm, the in-plane polarization disappears. The Young's modulus also shows an abrupt stiffness at the critical thickness. Based on the density functional theory calculations, we ascribe these behaviors to a structural phase transition from monoclinic to trigonal structure, which is further verified by transmission electron microscope technique. Taken together, these findings demonstrate the foundational importance of structural phase transition for enhancing the rich functionality and broad utility of vdW ferroelectrics.

cond-mat.mtrl-sci↗
arXiv2018

High Curie temperature half metallic 2D M2Se3 (M = Co, Ni, and Pd) monolayers with superior mechanical flexibility

Pursuing two-dimensional (2D) intrinsic ferromagnetism with high Curie temperature and great mechanical flexibility has attracted great interest in flexible spintronics. In the present work, we carried out a density functional theory (DFT) investigation on the 2D M2Se3 (M=Co, Ni and Pd) monolayers to understand their structural stabilities, electronic, magnetic and mechanical properties. Our results show that the Co2Se3 monolayer exhibits a fascinating half-metallic ferromagnetism with high Curie temperature (>700K). In addition, due to their unique buckling hinge-like structure, M2Se3 monolayers possess the large out-of-plane negative Poisson's ratio (NPR) and superior mechanical flexibility evidenced by their unusual critical strain two times greater than the well-known 2D materials. These findings imply that 2D M2Se3 family is the promising materials for the applications in the flexible and high-density spintronic nanodevices.

cond-mat.mtrl-sci↗